The rapid growth of IoT and real-time applications has led to a surge in data generation, traditionally processed in cloud-centric architectures. However, this paradigm introduces high latency, bandwidth bottlenecks, and privacy concerns. Fog computing, supported by edge devices, addresses these challenges by enabling computation closer to data sources. This survey presents a comprehensive review of recent studies on task offloading and resource allocation in fog computing, with a focus on Machine Learning (ML) and Deep Learning (DL)-based techniques. We analyze strategies across the fog-cloud continuum, considering factors such as latency, energy consumption, network utilization, and cost. The review also highlights simulation tools, architectural models, and placement policies. Unresolved challenges and interdependencies among research issues are discussed, and future directions are outlined with actionable evaluation metrics. This article serves as a valuable reference for researchers and practitioners aiming to optimize intelligent resource management in fog-enabled IoT environments.

In the rapidly evolving landscape of Software-Defined Networking (SDN), the enhancement of security measures against sophisticated cyber threats is paramount. Among these threats, inference attacks pose a significant risk by allowing adversaries to deduce the configurations and policies of SDN switches, thereby undermining the integrity and confidentiality of the network infrastructure. To address this critical issue, we introduce a novel dynamic rule replacement policy for SDN switches, leveraging the capabilities of a Support Vector Machine (SVM) for its implementation. Our approach utilizes a comprehensive set of statistical features, including duration analysis of flow rules, dispersion of packet match fields, and frequency of packet arrivals to identify patterns indicative of potential inference attacks. By dynamically adjusting the rules within SDN switches based on the analysis of these features, our policy significantly enhances the resilience of the network against such attacks. To accelerate the innovation and development of network services, this study proposes an integrated SDN architecture deployed over a serverless framework. This work serves as a starting point to enable researchers to realize the concept of modular serverless functions over traditional SDN environments. We show during inference attacks how a serverless framework improves the latency and resource utilization of the network compared to a traditional SDN framework. This study demonstrates an improvement in preventing inference attacks without compromising the performance and efficiency of the SDN infrastructure.

The gradual advancement of Internet-connected vehicles has transformed roads and highways into an intelligent ecosystem. This advancement has led to a widespread adoption of vehicular networks, driven by the enhanced capabilities of automobiles. However, managing mobility-aware computations, ensuring network security amidst instability, and overcoming resource constraints pose significant challenges in heterogeneous vehicular network applications within Fog computing. Moreover, the latency overhead remains a critical issue for tasks sensitive to latency and deadlines. The objective of this research is to develop a Mobility-aware Secured offloading and Scheduling (M-SOS) technique for a Dew-enabled vehicular Fog-Cloud computing system. This technique aims to address the issues outlined above by moving the computations closer to the edge of the network. Initially, a Dew-facilitated vehicular Fog network is proposed, leveraging heterogeneous computing nodes to handle diverse vehicular requests efficiently and ensuring uninterrupted services within the vehicular network. Further, task management is optimized using a Fuzzy logic that categorizes tasks based on their specific requirements and identifies the target layers for offloading. Besides, a cryptographic algorithm known as SHA-256 RSA enhances security. Moreover, a novel Linear Weight-based JAYA scheduling algorithm is introduced to assign tasks to appropriate computing nodes. The proposed algorithm surpasses the comparable algorithms by 23% in terms of AWT, 18% in terms of latency rate, 14% and 23% in terms of meeting the hard-deadline (H_d) and soft-deadline (S_d), and 35% in terms of average system cost, respectively.

There has been a significant paradigm shift from wired to wireless technology in in-vehicular networks. This shift is driven by the need for greater scalability, cost-effectiveness, and flexibility. In the automotive industry, traditional wired protocols such as the local interconnect network (LIN) and media-oriented systems transport (MOST) for non-critical systems (NCSs) add complexity to installation and maintenance, incur higher material costs, and offer limited scalability and mobility. NCSs, such as infotainment and weather forecast systems, do not require low latency and do not impair vehicle function when unavailable. This article presents an advanced methodology for enhancing connectivity in noncritical in-vehicular networks using Nordic Semiconductor's (nRFs) enhanced-nRF24L01 (EnRF24L01) module. The EnRF24L01 module is the nRF24L01 module that incorporated the Sensor-Medium Access Control (S-MAC) algorithm for energy-efficient communication. The proposed method enables seamless communication between NCSs using a tree-based primary and secondary architecture, where the primary is the actuator and the secondary is the sensor node. To optimize energy efficiency using synchronized sleep/wake schedules, reduce power consumption, and enhance scalability, the S-MAC protocol was incorporated. Comprehensive experiments were conducted in simulated environments using optimized network engineering tool (OPNET) and Proteus Circuit Simulators, analyzing critical performance metrics: latency, jitter, throughput, packet delivery ratio (PDR), and energy efficiency. The results indicate that the proposed method supports a greater number of nodes with enhanced data transmission rates and operates at lower voltages, thereby extending the communication range and reducing overall power consumption. Additionally, hardware simulation results demonstrate the successful integration of EnRF24L01 modules with Arduino for wireless data transmission, showing significant improvements in scalability, energy efficiency, and adaptability, as well as architectural and operational costs and maintenance efficiency.

Software Defined Networking (SDN) is vulnerable to Distributed Denial of Service (DDoS) attacks due to its centralized architecture. These attacks involve injecting large numbers of fake packets with spoofed header field information into the controller, leading to network malfunctions. Existing solutions often block both malicious and benign traffic indiscriminately, resulting in a high False Positive Rate. In this paper, we present DDoSBlocker, a lightweight and protocol-independent DDoS defense system designed to identify and block the source points of DDoS attacks without disrupting legitimate traffic. DDoSBlocker combines a time-based address mapping method with a triggering-based machine learning method to accurately identify attack sources. It introduces four novel features: percentage of fake destination IPs, average bytes per packet, percentage of bidirectional flow rules, and percentage of fake flow rules. The system then installs blocking rules at the attack sources, providing immediate mitigation. The model outperforms existing mitigation solutions such as destination point blocking, clustering, and backtracking. Implemented in the Floodlight controller, DDoSBlocker was evaluated under four attack scenarios using different performance metrics. The proposed model, utilizing a random forest classifier, demonstrated 99.71% accuracy, an average detection time of 3 s, an average mitigation time of 0.5 s, and a False Positive Rate of 0.51%.

The integration of agri-technology with the Internet of Agricultural Things (IoAT) is revolutionizing the field of smart agriculture, particularly in diagnosing and treating Oryza sativa (rice) diseases. Given that rice serves as a staple food for over half of the global population, ensuring its healthy cultivation is crucial, particularly with the growing global population. Accurate and timely identification of rice diseases, such as Brown Leaf Spot (BS), Bacterial Leaf Blight (BLB), and Leaf Blast (LB), is therefore essential to maintaining and enhancing rice production. In response to this critical need, the research introduces a timely detection system that leverages the power of Digital Twin (DT)-enabled Fog computing, integrated with Edge and Cloud Computing (CC), and supported by sensors and advanced technologies. At the heart of this system lies a sophisticated deep-learning model built on the robust AlexNet neural network architecture. This model is further refined by including Quaternion convolution layers, which enhance colour information processing, and Atrous convolution layers, which improve depth perception, particularly in extracting disease patterns. To boost the model's predictive accuracy, the Chaotic Honey Badger Algorithm (CHBA) is employed to optimize the CNN hyperparameters, resulting in an impressive average accuracy of 93.5 %. This performance significantly surpasses that of other models, including AlexNet, AlexNet-Atrous, QAlexNet, and QAlexNet-Atrous, which achieved respective accuracies of 75 %, 84 %, 89 %, and 91 %. Moreover, the CHBA optimization algorithm outperforms other techniques like CSO, BSO, PSO, and CJAYA and demonstrates optimal results with an 80–20 % training-testing parameter split. Service latency analysis further reveals that the Fog-Edge-assisted environment is more efficient than the Cloud-assisted model for latency reduction. Additionally, the DT-enabled QAlexNet-Atrous-CHBA model proves to be far superior to its non-DT counterpart, showing substantial improvements in 18.7 % in Accuracy, 17 % in recall, 19 % in Fβ-measure, 17.3 % in specificity, and 13.4 % in precision, respectively. These enhancements are supported by convergence analysis and the Quade rank test, establishing the model's effectiveness and potential to significantly improve rice disease diagnosis and management. This advancement promises to contribute significantly to the sustainability and productivity of global rice cultivation.

Wireless communication has become a global standard for various commodity devices. This change has created a new opportunity to extend the research toward occupancy detection, perimeter security, elderly monitoring, localization, etc. Device-free localization (DFL) is considered the most exciting research problem, where the target location is inferred from the target-induced shadowed links within the wireless network. The geometric scheme is computationally inexpensive and accurate among existing schemes in the literature. However, the accuracy of the geometric schemes mainly relies on the dense wireless networks. This paper proposes a geometric approach that utilizes the angle and distance parameters for localization. Unlike other geometric schemes, the proposed scheme identifies the target even in sparsely deployed environments. The proposed scheme estimates the distances by mapping the received signal strength (RSS) across the shadowed links. The estimated distances across the heavily shadowed link estimate the target distance. Similarly, the distance across the shadowed link estimates the target angle. To evaluate the performance of the proposed scheme, we have considered various performance metrics, such as the varying size of the deployment area, the varying number of reference nodes, and the varying mapping range. The results show that the proposed scheme improved the localization accuracy compared to comparable schemes.

Self-driving vehicles are envisioned as automated and safety-focused vehicles facilitating smooth movement on roads. This research proposes a novel, robust, and intelligent navigation framework for such vehicles through an integrated fusion of advanced technologies like predictive analytics with remote sensing and detection for accurate obstacle/object detection. TaskTrek, ViewVerse, and RuleRise form the core of the essential model governing vehicle-environment interaction. TaskTrek handles kinematic trajectory synthesis and space-time traffic modeling, ViewVerse provides LiDAR-based volumetric perception and radar-assisted navigational intelligence, and RuleRise manages topological localization, vehicle actuation, and autonomous decision-making through multimodal sensory fusion. The model applies an iterative Multi-FacBiNet method, which uses the cognitive Fully Convolutional Neural Network (FCNN) method to detect and classify obstacles during vehicle movement on the road. Upon stimulation during vehicle movement, the model provided an encouraging outcome. The fusion of predictive intelligence, Radar, and sensing technologies gave 95.3% proficiency. Minimum obstacle detection, processing, and response delays of 0.116 seconds, 0.105 seconds, and 0.36 seconds, respectively, are recorded. The computed mean obstacle detection accuracy for right, left, front and back camera angles are 88.3%, 83.8%, 91.4%, and 89.9%, respectively. Further, a comprehensive analysis of the model’s performance in different on-road scenarios considering metrics like traffic load, road type, and region density was done. The model generated a very impressive accuracy of obstacle detection on all parameters. The results of this study not only aid in accelerating the development of precise navigation-enabled self-driving vehicles but also in the context of environmentally friendly mobility/motion tracking solutions.

Internet of things (IoT) facilitates a variety of heterogeneous devices to be enabled with network connectivity via various network architectures to gather and exchange real-time information. On the other hand, the rise of IoT creates Distributed Denial of Services (DDoS) like security threats. The recent advancement of Software Defined-Internet of Things (SDIoT) architecture can provide better security solutions compared to the conventional networking approaches. Moreover, limited computing resources and heterogeneous network protocols are major challenges in the SDIoT ecosystem. Given these circumstances, it is essential to design a low-cost DDoS attack classifier. The current study aims to employ an improved feature selection (FS) technique which determines the most relevant features that can improve the detection rate and reduce the training time. At first, to overcome the data imbalance problem, Edited Nearest Neighbor-based Synthetic Minority Oversampling (SMOTE-ENN) was exploited. The study proposes SFMI, an FS method that combines Sequential Feature Selection (SFE) and Mutual Information (MI) techniques. The top k common features were extracted from the nominated features based on SFE and MI. Further, Principal component analysis (PCA) is employed to address multicollinearity issues in the dataset. Comprehensive experiments have been conducted on two benchmark datasets such as the KDDCup99, CIC IoT-2023 datasets. For classification purposes, Decision Tree, K-Nearest Neighbor, Gaussian Naive Bayes, Random Forest (RF), and Multilayer Perceptron classifiers were employed. The experimental results quantitatively demonstrate that the proposed SMOTE-ENN+SFMI+PCA with RF classifier achieves 99.97% accuracy and 99.39% precision with 10 features.

The Internet of Things (IoT) is rapidly expanding, connecting an increasing number of devices daily. Having diverse and extensive networking and resource-constrained devices creates vulnerabilities to various cyber-attacks. The IoT with the supervision of Software Defined Network (SDN) enhances the network performance through its flexibility and adaptability. Different methods have been employed for detecting security attacks; however, they are often computationally efficient and unsuitable for such resource-constraint environments. Consequently, there is a significant requirement to develop efficient security measures against a range of attacks. Recent advancements in deep learning (DL) models have paved the way for designing effective attack detection methods. In this study, we leverage Genetic Algorithm (GA) with a correlation coefficient as a fitness function for feature selection. Additionally, mutual information (MI) is applied for feature ranking to measure their dependency on the target variable. The selected optimal features were used to train a hybrid DNN model to uncover attacks in IoT networks. The hybrid DNN integrates Convolutional Neural Network, Bi-Gated Recurrent Units (Bi-GRU), and Bidirectional Long Short-Term Memory (Bi-LSTM) for training the input data. The performance of our proposed model is evaluated against several other baseline DL models, and an ablation study is provided. Three key datasets InSDN, UNSW-NB15, and CICIoT 2023 datasets, containing various types of attacks, were used to assess the performance of the model. The proposed model demonstrates an impressive accuracy and detection time over the existing model with lower resource consumption.

Objective: The modern era of cognitive intelligence in clinical space has led to the rise of ‘Medical Cognitive Virtual Agents’ (MCVAs) which are labeled as intelligent virtual assistants interacting with users in a context-sensitive and ambient manner. They aim to augment users' cognitive capabilities thereby helping both patients and medical experts in providing personalized healthcare like remote health tracking, emergency healthcare and robotic diagnosis of critical illness, among others. The objective of this study is to explore the technical aspects of MCVA and their relevance in modern healthcare. Methods: In this study, a comprehensive and interpretable analysis of MCVAs are presented and their impacts are discussed. A novel system framework prototype based on artificial intelligence for MCVA is presented. Architectural workflow of potential applications of functionalities of MCVAs are detailed. A novel MCVA relevance survey analysis was undertaken during March-April 2023 at Bhubaneswar, Odisha, India to understand the current position of MCVA in society. Results: Outcome of the survey delivered constructive results. Majority of people associated with healthcare showed their inclination towards MCVA. The curiosity for MCVA in Urban zone was more than in rural areas. Also, elderly citizens preferred using MCVA more as compared to youths. Medical decision support emerged as the most preferred application of MCVA. Conclusion: The article established and validated the relevance of MCVA in modern healthcare. The study showed that MCVA is likely to grow in future and can prove to be an effective assistance to medical experts in coming days.

Among other deep learning (DL) architectures, the convolutional neural network (CNN) has wide applications in speech recognition, face detection, natural language processing, and computer vision. Multiply and Accumulate (MAC) unit is a core part of CNN and requires large computations and memory resources. They result in more power dissipation for low-power embedded devices. Hence, the hardware implementation of CNN to produce high throughput is one of the challenges nowadays. Therefore, sparsity is introduced in weights by a non-linear method with a minor compromise in accuracy. Experimental results also show the enhancement of 52% sparsity with a 4% loss in accuracy. In addition, an indexing module is proposed to perform Single Instruction Multiple Data (SIMD) operations in the fully connected layer to perform only effective operations without multiplication. This module is used along with sparsity to offer better results as compared to SOTA methods. Cadence RTL compiler results show that the proposed indexing module saves 1.3 nJ of energy as compared to the existing methods.

SDN has evolved to revolutionize next-generation networks, offering programmability for on-the-fly service provisioning, primarily supported by the OpenFlow (OF) protocol. The limited storage capacity of Ternary Content Addressable Memory (TCAM) for storing flow tables in OF switches introduces vulnerabilities, notably the Low-Rate Flow Table Overflow (LOFT) attacks. LOFT exploits the flow table's storage capacity by occupying a substantial amount of space with malicious flow, leading to a gradual degradation in the flow-forwarding performance of OF switches. To mitigate this threat, we propose FloRa, a machine learning-based solution designed for monitoring and detecting LOFT attacks in SDN. FloRa continuously examines and determines the status of the flow table by closely examining the features of the flow table entries. When suspicious activity is identified, FloRa promptly activates the machine-learning based detection module. The module monitors flow properties, identifies malicious flows, and blacklists them, facilitating their eviction from the flow table. Incorporating novel features such as Packet Arrival Frequency, Content Relevance Score, and Possible Spoofed IP along with Cat Boost employed as the attack detection method. The proposed method reduces CPU overhead, memory overhead, and classification latency significantly and achieves a detection accuracy of 99.49% which is more than the state-of-the-art methods to the best of our knowledge. This approach not only protects the integrity of the flow tables but also guarantees the uninterrupted flow of legitimate traffic. Experimental results indicate the effectiveness of FloRa in LOFT attack detection, ensuring uninterrupted data forwarding and continuous availability of flow table resources in SDN.

In Cloud-based computing, job scheduling and load balancing are vital to ensure on-demand dynamic resource provisioning. However, reducing the scheduling parameters may affect datacenter performance due to the fluctuating on-demand requests. To deal with the aforementioned challenges, this research proposes a job scheduling algorithm, which is an improved version of a swarm intelligence algorithm. Two approaches, namely linear weight JAYA (LWJAYA) and chaotic JAYA (CJAYA), are implemented to improve the convergence speed for optimal results. Besides, a load-balancing technique is incorporated in line with job scheduling. Dynamically independent and non-pre-emptive jobs were considered for the simulations, which were simulated on two disparate test cases with homogeneous and heterogeneous VMs. The efficiency of the proposed technique was validated against a synthetic and real-world dataset from NASA, and evaluated against several top-of-the-line intelligent optimization techniques, based on the Holm's test and Friedman test. Findings of the experiment show that the suggested approach performs better than the alternative approaches.

Opinion spam detection is a challenge for online review systems and social forum operators. Opinion spamming costs businesses and people money since it deceives customers as well as automated opinion mining and sentiment analysis systems by bestowing undeserved positive opinions on target firms and/or bestowing fake negative opinions on others. One popular detection approach is to model a review system as a network of users, products, and reviews, for example using review graph models. In this article, we study the effects of network scale on network-based review spammer detection models, specifically on the trust model and the SpammerRank model. We then evaluate both network models using two large publicly available review datasets, namely: the Amazon dataset (containing 6 million reviews by more than 2 million reviewers) and the UCSD dataset (containing over 82 million reviews by 21 million reviewers). It has been observed that SpammerRank model provides a better scaling time for applications requiring reviewer indicators and in case of trust model distributions are flattening out indicating variance of reviews with respect to spamming. Detailed observations on the scaling effects of these models are reported in the result section.

Cloud computing is widely being used by researchers’ academia and industry for its abundant opportunities. Different technologies such as Internet of Things, Edge Computing, and Fog Computing are gradually integrating with the cloud platform due to its scalability and availability. The number of cloud users is also increasing exponentially. The requests generated from wide range of users are random. Executions of request and providing the quality of service are one of the promising issues in cloud environment. Optimization of response time and commutation cost is the major concern in cloud environment. Researchers have proposed many heuristics, meta-heuristic approaches for solving the load balancing issues in cloud platform. In this paper, authors have proposed a hybrid approach for load balancing in cloud computing using genetic algorithm with gravitational search algorithm. Simulations are carried out using cloud Sim Simulator and comparisons are made with other competitive approaches to evaluate the performance of the system. It is observed that the hybrid approach outperforms in various measures.

With the advancement and rapid development of the internet, the most convenient strategies for patients are mainly provided with digital healthcare systems that mainly includes the use of mobile health technology which is quite efficient. Moreover, this field is slightly shifting and also indicating interest towards the smart and intelligent models as there are quite a lot of benefits associated with it like cost decrement, easy to understand and also including the personal satisfaction of patients. The latest application of m-health medical treatment is now still on the process of the investigation because still users are facing challenges in the clinical environment. This m-health approach can be applied to accurately determine skin cancer symptoms in patients. In this paper, an impact of m-healthcare on disease diagnosis is demonstrated. A new m-health module for skin cancer diagnosis called ‘Smart Skin-Proto’ is developed. Then its usage in skin cancer assessment is also highlighted and upon implementation, the model records optimal performance which records an accuracy of 96.2% with 15 decision trees count. Also the overall latency of this application is less than other existing mobile apps.

In India Japanese Encephalitis (JEV) has been a major public health problem. In endemic districts of country each year there is a large-scale outbreak occurring of JEV. Research says that Japanese Encephalitis is a flavivirus related to West Nile Virus, Yellow Fever and Dengue and it is escalated by mosquitoes. Japanese Encephalitis is although rare, but the fatality rate is around 30%. Till now there is no cure for JEV, the entire treatment is focused for supporting the patient to overcome disease and relieving severe clinical sign. Maximum number of JEV cases in India are of infants and the fatality rate is around 30% which is a great matter of concern. Here Force of Infection denotes the rate at which sensitive individuals acquire an infectious disease. In India, states which report major outbreak of Japanese Encephalitis are Uttar pradesh, Andhra Pradesh, West Bengal, Karnataka, Assam, Tamil Nadu, Bihar, Goa and Manipur. The impacting factors include Climate, Rice Distribution, Livestock Distribution, Population Density, Specific Age Group Density, Urban/Rural Category and Elevation. Impacting Factors may change with the location. Here we have used Machine learning algorithms like Ridge Regression, Lasso Regression, ElasticNet Regression and Multi-layer Perceptron for the prediction of Force of Infection of Japanese Encephalitis Virus. ElasticNet Regression Algorithm is also used for extracting the significant attribute from the JEV Dataset. The proposed model generated an optimum performance in context to the error rate and accuracy of prediction.

Security in Software Defined Network (SDN) architecture is becoming the most substantial challenge. This paper introduces a novel threat model focused on flow table modification in the P4-programmable SDN data plane, outlining an attacker's stochastic manipulation of flow rules from a compromised switch. A detection framework is proposed to identify the malicious switch within the network by utilizing the thrift port. Moreover, a fuzzy-rule-based mitigation strategy has been proposed to identify the severity of attacks. The feasibility and effectiveness of the methodology are evaluated using a developed testbed setup by employing Facebook datacenter fabric topology in a Mininet emulator and BMv2 switch.

Logic locking has become a robust method for reducing the risk of intellectual property (IP) piracy, overbuilding, and hardware Trojan threats throughout the lifespan of integrated circuits (ICs). Nevertheless, the majority of reported logic locking approaches are susceptible to satisfiability (SAT)-based attacks. The existing SAT-resistant logic locking methods provide a tradeoff between security and effectiveness and require a significant design overhead. In this article, a novel gate replacement-based input-dependent key-based logic locking (IDKLL) technique is proposed. We first introduce the concept of IDKLL, and how the IDKLL can mitigate the SAT attacks completely. Unlike conventional logic locking, the IDKLL approach uses multiple key sequences (KSs) (instead of a single KS) as the correct key to lock/unlock the design functionality for all inputs. Based on this IDKLL concept, we developed several locked gates. Further, we propose a lightweight gate replacement-based IDKLL called GateLock that locks the design by replacing exciting gates with their respective IDKLL-based locked gates. The security analysis of the proposed method shows that it prevents the SAT attack completely and forces the attacker to apply a significantly large number of brute-force attempts to decipher the key. The experimental evaluation on International Symposium on Circuits and Systems (ISCAS) and International Test Conference (ITC) benchmarks shows that the proposed GateLock method completely prevents the SAT-based attacks and requires an average of 56.7%, 72.7%, and 87.8% reduced area, power, and delay compared to cascaded locking (CAS-Lock) and strong Anti-SAT (SAS) approaches.

Implementing innovative farming practices becomes imperative for a country whose economy relies heavily on agricultural products. Over recent years, the swift process of urbanization and the depletion of forests have influenced farmers. Due to the lack of rainwater harvesting and changing weather patterns, many crop failure cases have been registered in the last few years. To prevent loss of annual crop production, many researchers propose the technology-driven smart farming method. Smart agriculture involves utilizing technology to create a controlled environment for the management of the crops. Smart farming increases crop production and provides small farmers with an alternative income source. The government initiated many pilot projects to promote smart agriculture in India. Yet, the absence of technological assistance and skilled procedures poses a challenge for most farmers aiming to thrive in this industry. This paper introduces a smart freshwater recirculating aquaculture system based on IoT technology. The proposed system has integrated sensors and actuators. The sensor system monitors the water parameters, and actuators maintain the aquaculture environment. An intelligent data analytics algorithm played a significant role in monitoring and maintaining the freshwater aquaculture environment. The analytics derived the relationship between the water parameters and identified the relative change. From the experimental evaluation, we have identified that the M5 model tree algorithm has the highest accuracy for monitoring the relative change in water parameters.

With the increasing use of biometrics in Internet of Things (IoT)-based applications, it is essential to ensure that biometric-based authentication systems are secure. Biometric characteristics can be accessed by anyone, which poses a risk of unauthorized access to the system through spoofed biometric traits. Therefore, it is important to implement secure and efficient security schemes suitable for real-life applications, less computationally intensive, and invulnerable. This work presents a hybrid template protection scheme for secure face recognition in IoT-based environments, which integrates Cancelable Biometrics and Bio-Cryptography. Mainly, the proposed system involves two steps: 1) face recognition and 2) face biometric template protection. The face recognition includes face image preprocessing by the tree structure part model (TSPM), feature extraction by ensemble patch statistics (EPS) technique, and user classification by multiclass linear support vector machine (SVM). The template protection scheme includes cancelable biometric generation by modified FaceHashing and a Sliding-XOR (called S-XOR)-based novel Bio-Cryptographic technique. A user biometric-based key generation technique has been introduced for the employed Bio-Cryptography. Three benchmark facial databases, CVL, FEI, and FERET, have been used for the performance evaluation and security analysis. The proposed system achieves better accuracy for all the databases of 200-D cancelable feature vectors computed from the 500-D original feature vector. The modified FaceHashing and S-XOR method shows superiority over existing face recognition systems and template protection.

The vehicular ad hoc network (VANET) provides a variety of applications and is gaining popularity due to the reuse of network resources. Exact location identification with optimum delay is the demand of all vehicle users. Currently, individuals are utilizing the Global Positioning System (GPS) to ascertain the precise geographical coordinates of a given location. The drawback of GPS technology is its inability to accurately determine location and use GPS in some remote areas. Searching for the location of a particular house randomly incurs a loss of fuel as well as increases delay. This motivates us to propose one automated method through VANET using location service-based routing for the location identification of a house. The proposed work involves searching for the location of a house using the open-source MongoDB database, and the operations on the database are performed using the tool Node-Red. By simulation using SUMO and Network Simulator 2.35, the proposed work is evaluated and compared with existing location service-based routing like geographic location service (GLS) and hierarchical location service (HLS). The proposed work performs better in terms of routing efficiency (end-to-end latency, packet delivery rate), location efficiency (request sends, query success rate, and request travel time), and routing and location overhead (MAC bandwidth consumption). Also, the performance of the proposed work is presented as stable by increasing the number of vehicles. The statistical analysis of the packet delivery ratio and CBR end-to-end latency is carried out using T score.

The development of the Internet of Things (IoT) technology is leading to a new era of smart applications such as smart transportation, buildings, and smart homes. Moreover, these applications act as the building blocks of IoT-enabled smart cities. The high volume and high velocity of data generated by various smart city applications are sent to flexible and efficient cloud computing resources for processing. However, there is a high computation latency due to the presence of a remote cloud server. Edge computing, which brings the computation close to the data source is introduced to overcome this problem. In an IoT-enabled smart city environment, one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay constraint. An efficient resource allocation at the edge is helpful to address this issue. In this paper, an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation problem. First, we presented a three-layer network architecture for IoT-enabled smart cities. Then, we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization problem. Learning Automata (LA) is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource mapping. An extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.

One of the main causes of energy consumption in Internet of Vehicles (IoV) networks is an ill-designed network congestion control protocol, which results in numerous packet drops, lower throughput, and increased packet retransmissions. In IoV network, the objective to increase network throughput can be achieved by minimizing packets re- transmission and optimizing bandwidth utilization. It has been observed that the congestion control mechanism (i.e., the congestion window) can plays a vital role in mitigating the aforementioned challenges. Thus, this paper present a cross-layer technique to controlling congestion in an IoV network based on throughput and buffer use. In the proposed approach, the receiver appends two bits in the acknowledgment (ACK) packet that describes the status of the buffer space and link utilization. The sender then uses this information to monitor congestion and limit the transmission of packets from the sender. The proposed model has been experimented extensively and the results demonstrate a significantly higher network performance percentage in terms of buffer utilization, link utilization, throughput, and packet loss.

With the idea of shifting towards a smart future there is a lot of research being done in the area of internet of things (IoT) and wireless communication, especially 5G network technology. These technologies are instrumenting society towards a world of high connectivity, through secure evolutionary telecommunication methodologies. In this chapter we understand the role of 5G networks in enhancing IoT devices and discuss their security aspects. Integration of IoT and software defined network termed as SDIoT enables automatic traffic rerouting, device reconfiguration, and bandwidth allocation seamlessly. Smart cities utilize the SDIoT integrated with 5G to gather real-time data, better understand how demand patterns are changing, and respond with quicker and more affordable solutions. The authors try to understand the existing research scenario in 5G networks and IoT, and what areas are being taken into consideration for improvement in the coming future. Copyright

Twitter is a radiant platform with a quick and effective technique to analyze users' perceptions of activities on social media. Many researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder group. The sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning tools. An assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine (SVM). This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare, behaviour estimation, etc. In addition, the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive, negative and neutral tweets. In this work, we obligated Twitter Application Programming Interface (API) account and programmed in python for sentiment analysis approach for the computational measure of user's perceptions that extract a massive number of tweets and provide market value to the Twitter account proprietor. To distinguish the results in terms of the performance evaluation, an error analysis investigates the features of various stakeholders comprising social media analytics researchers, Natural Language Processing (NLP) developers, engineering managers and experts involved to have a decision-making approach.

Air collection around the lung regions can cause lungs to collapse. Conditions like emphysema can cause chronic obstructive pulmonary disease (COPD), wherein lungs get progressively damaged, and the damage cannot be reversed by treatment. It is recommended that these conditions be detected early via highly complex image processing models applied to chest X-rays so that the patient’s life may be extended. Due to COPD, the bronchioles are narrowed and blocked with mucous, and causes destruction of alveolar geometry. These changes can be visually monitored via feature analysis using effective image classification models such as convolutional neural networks (CNN). CNNs have proven to possess more than 95% accuracy for detection of COPD conditions for static datasets. For consistent performance of CNNs, this paper presents an incremental learning mechanism that uses deep transfer learning for incrementally updating classification weights in the system. The proposed model is tested on 3 different lung X-ray datasets, and an accuracy of 99.95% is achieved for detection of COPD. In this paper, a model for temporal analysis of COPD detected imagery is proposed. This model uses Gated Recurrent Units (GRUs) for evaluating lifespan of patients with COPD. Analysis of lifespan can assist doctors and other medical practitioners to take recommended steps for aggressive treatment. A smaller dataset was available to perform temporal analysis of COPD values because patients are not advised continuous chest X-rays due to their long-term side effects, which resulted in an accuracy of 97% for lifespan analysis.

e-Learning is a sought-after option for learners during pandemic situations. In e-Learning platforms, there are many courses available, and the user needs to select the best option for them. Thus, recommender systems play an important role to provide better automation services to users in making course choices. It makes recommendations for users in selecting the desired option based on their preferences. This system can use machine intelligence (MI)-based techniques to carry out the recommendation mechanism. Based on the preferences and history, this system is able to know what the users like most. In this work, a recommender system is proposed using the collaborative filtering mechanism for e-Learning course recommendation. This work is focused on MI-based models such as K-nearest neighbor (KNN), Singular Value Decomposition (SVD) and neural network–based collaborative filtering (NCF) models. Here, one lakh of Coursera’s course review dataset is taken from Kaggle for analysis. The proposed work can help learners to select the e-Learning courses as per their preferences. This work is implemented using Python language. The performance of these models is evaluated using performance metrics such as hit rate (HR), average reciprocal hit ranking (ARHR) and mean absolute error (MAE). From the results, it is observed that KNN is able to perform better in terms of higher HR and ARHR and lower MAE values as compared to other models.

Classification of the patterns is a crucial structure of research and applications. Using fuzzy set theory, classifying the patterns has become of great interest because of its ability to understand the parameters. One of the problems observed in the fuzzification of an unknown pattern is that importance is given only to the known patterns but not to their features. In contrast, features of the patterns play an essential role when their respective patterns overlap. In this paper, an optimal fuzzy nearest neighbor model has been introduced in which a fuzzification process has been carried out for the unknown pattern using k nearest neighbor. With the help of the fuzzification process, the membership matrix has been formed. In this membership matrix, fuzzification has been carried out of the features of the unknown pattern. Classification results are verified on a completely llabelled Telugu vowel data set, and the accuracy is compared with the different models and the fuzzy k nearest neighbor algorithm. The proposed model gives 84.86% accuracy on 50% training data set and 89.35% accuracy on 80% training data set. The proposed classifier learns well enough with a small amount of training data, resulting in an efficient and faster approach.

The 3D Underwater Sensor Network (USNs) has become the most optimistic medium for tracking and monitoring underwater environment. Energy and collision are two most critical factors in USNs for both sparse and dense regions. Due to harsh ocean environment, it is a challenge to design a reliable energy efficient with collision free protocol. Diversity in link qualities may cause collision and frequent communication lead to energy loss; that effects the network performance. To overcome these challenges a novel protocol Forwarder Selection Energy Efficient Routing (FSE2R) is proposed. Our proposal's key idea is based on computation of node distance from the sink, Residual Energy (RE) of each node and Signal to Interference Noise Ratio (SINR). The node distance from sink and RE is computed for reliable forwarder node selection and SINR is used for analysis of collision. The novel proposal compares with existing protocols like H2AB, DEEP, and E2LR to achieve Quality of Service (QoS) in terms of throughput, packet delivery ratio and energy consumption. The comparative analysis shows that FSE2R gives on an average 30% less energy consumption, 24.62% better PDR and 48.31% less end-to-end delay compared to other protocols.

Nowadays, edge computing has become part of the Internet of Things (IoT) that plays a vital role in developing smart applications. As the usage of IoT devices significantly increases, at the same time, network edge infrastructure faces several security challenges. Distributed Denial-of-Service (DDoS) attack is one of the most severe threats to edge-cloud services. Therefore, designing a robust mitigating system is unavoidable for the network edge, and it must be able to recognize emerging attacks. This work proposes an anomaly-based DDoS detection approach that combines information-theoretic metrics and multivariate correlation analysis. The information-theoretic metric captures the randomness and complex nature of traffic behaviour. Similarly, multivariate correlation analysis identifies the relationship among traffic features. Combining information metrics and correlation analysis, we generate normal and attack traffic profiles for the training base to estimate density. The generated profiles build on the metrics including Triangle Area Mapping (TAM) with correlation analysis, Renyi's divergence, covariance, mean, and standard deviation, which enhances the detection performance of the proposed approach. The effectiveness of the proposed approach is evaluated using testbed and benchmark datasets. The results show that the proposed approach achieves 0.17% and 2.32%, and 0.50% higher accuracy compared to the baseline approaches on the testbed, UNSW and CIC-DDoS datasets, respectively.

Internet of medical things (IoMT) plays an important role nowadays to support healthcare system. The hospital equipment’s called as medical things are now connected to the cloud for getting many useful services. The data generated from the equipments are sent to the cloud for getting the desired service. In current scenario, most hospitals collect many images using equipments, but these equipments have less computational capability to process the huge generated data. In this work, one such equipment is considered which can take the human eye images and send the images to the cloud for detection of cherry red spot (CRS). CRS disease in eyes is considered as one of the very dangerous disease. The early diagnosis of CRS disease needs to be focused in order to avoid any adverse effect on human body. In this paper, a machine intelligence based model is proposed to detect the CRS disease areas in the human eyes by analyzing several CRS disease images using IoMT. The proposed approach is mainly focused on fuzzy rule-based mechanism to carry out the identification of such affected area in the eyes in cloud layer. From the results, it is observed that the CRS disease areas in the eyes are detected well with better detection accuracy and lower detection error than k-means algorithm. This approach will help the doctors to track the exact position of the affected areas in the eye for its diagnosis. The simulation is performed using socket programming written in Python 3 where a cloud server and a client device are created and images are sent from the client device to the server, and afterwards the detection of CRS is performed at the server using MATLAB R2015b. The proposed method is able to provide better performance in terms of detection accuracy, detection error and processing time as 94.67%, 5.33% and 1.1481% units respectively on an average case scenario.

With the technological evolution of mobile devices, 5G and 6G communication and users' demand for new generation applications viz. face recognition, image processing, augmented reality, etc., has accelerated the new computing paradigm of Mobile Edge Computing (MEC). It operates in close proximity to users by facilitating the execution of computational-intensive tasks from devices through offloading. However, the offloading decision at the device level faces many challenges due to uncertainty in various profiling parameters in modern communication technologies. Further, with the increase in the number of profiling parameters, the fuzzy-based approaches suffer inference searching overheads. In this context, a fuzzy-based approach with an optimal inference strategy is proposed to make a suitable offloading decision. The proposed approach utilizes the Classification and Regression Tree (CART) mechanism at the inference engine with reduced time complexity of O (|V|2log2| L|)), as compared to O (| L ||V|) of state-of-the-art, conventional fuzzy-based offloading approaches, and has been proved to be more efficient. The performance of the proposed approach is evaluated and compared with contemporary offloading algorithms in a python-based fog and edge simulator, YAFS. The simulation results show a reduction in average task processing time, average task completion time, energy consumption, improved server utilization, and tolerance to latency and delay sensitivity for the offloaded tasks in terms of reduced task failure rates.

Security is a major concern in vehicular networks for reliable communication between the source and the destination in smart cities. Data, these days, is in the form of safety or non-safety messages in formats like text, audio, images, video, etc. These information exchanges between the two parties need to be updated with a trust value (TV) by analyzing the communication data. In this paper, a machine learning-based misbehavior detection system (ML-MDS) is proposed for cognitive software-defined multimedia vehicular networks (CSDMV) in smart cities. In the proposed system, before communication, the vehicle must be aware of the TV of other vehicles. If the TV for a vehicle is higher than a threshold (th), then the communication happens and the whole transaction information is sent to the local software-defined network controller (LSDNC) for classification of behavior using the ML algorithm. After this, the TV is updated as per the last transaction status at LSDNC and the updated TV of the vehicle is sent to the main SDN controller for information gathering. In this system, the best ML algorithm for the ML-MDS model is selected by considering decision tree, support vector machine (SVM), neural network (NN), and logistic regression (LR) algorithms. The classification accuracy performance is evaluated using UNSW_NB-15 standard dataset for detecting the normal and malicious vehicles. NN shows better classification accuracy than other algorithms. The proposed ML-MDS is implemented and evaluated using OMNeT++ network simulator and the Simulation of Urban Mobility (SUMO) road traffic simulator by considering various parameters such as detection accuracy, detection time, and energy consumption. From the results, it is observed that the detection accuracy of proposed ML-MDS system is 98.4% as compared to Grover et al. scheme which was 80.2%. Also, for scalability issue the dataset size is increased and performance is evaluated in Orange 3.26.0 machine analytics tool and NN is found to be the best algorithm which shows high accuracy in detecting the attackers.

In major urban cities, government authorities have developed various service-requesting systems to report non-emergency public issues related to urban rare events such as noise, blocked driveways, illegal parking, etc. For certain events, request volumes can surge significantly, and timely response depends on accurate prediction. In this paper, we investigate how long it takes to resolve service requests by the agencies. This paper introduces NERPS, a non-emergency response system designed to forecast service request response time. Leveraging urban data, the model establishes connections between historical and future response times. In time series data, applying boot-strapping on the reminder component for generating synthetic data with original time series before fitting the model has been viewed to be effective. The NERPS integrates Holt-Winters with the Moving Block Bootstrap (MBB+HW) model for forecasting the service requests in the NYC dataset. Proposed model forecasts to generate 100-time series values and final prediction obtained by averaging the forecast set. The optimal block size is estimated via the flat-top lag windows technique. This research extends beyond prior studies by comparing the forecasting performance of proposed statistical methods with MI/DL approaches on complex and nonlinear time series data. We consider SARIMA, ARIMA, FB-Prophet, linear regression and basic LSTM as baseline models for response time forecasting and compare the proposed model with multistep ahead point forecasts. The results show that in most cases, the NERPS achieves low RMSE, MAE and Relative Errors among top complaint types and agencies.

With the increasing advent of applications and services adopting cloud-based technologies, generic automated tuning techniques of database services are gaining much attraction. This work identifies and proposes to overcome the potential challenges associated with deploying a tuning service as part of Platform-as-a-Service (PaaS) offerings for tuning of backing services. Offering an effective database tuning service requires such tuners whose architecture can support tuning multiple databases and numerous database versions deployed on various types of underlying hardware configurations with varying VM plans. Tuners that offer such capabilities usually attempt to leverage experiences gathered previously. By taking advantage of relevant past experiences, tuners classify the current workload to the most pertinent workload seen recently. In this work, a five-layered, fully connected neural network with ReLU activation function is being employed as the classification model to classify data points into relevant workload classes. The categorical cross-entropy function is employed as the loss function and optimized using Adam optimizer. The work handles the challenges related to the cold-start problem, issues in mapping, and cascading errors. The proposed solution can overcome these issues in a large-scale production environment. The results show that the model has 93.3% accuracy in 93.8% F1-score as compared to the previous model like Ottertune.

With continuous advancements in Internet technology and the increased use of cryptographic techniques, the cloud has become the obvious choice for data sharing. Generally, the data are outsourced to cloud storage servers in encrypted form. Access control methods can be used on encrypted outsourced data to facilitate and regulate access. Multi-authority attribute-based encryption is a propitious technique to control who can access encrypted data in inter-domain applications such as sharing data between organizations, sharing data in healthcare, etc. The data owner may require the flexibility to share the data with known and unknown users. The known or closed-domain users may be internal employees of the organization, and unknown or open-domain users may be outside agencies, third-party users, etc. In the case of closed-domain users, the data owner becomes the key issuing authority, and in the case of open-domain users, various established attribute authorities perform the task of key issuance. Privacy preservation is also a crucial requirement in cloud-based data-sharing systems. This work proposes the SP-MAACS scheme, a secure and privacy-preserving multi-authority access control system for cloud-based healthcare data sharing. Both open and closed domain users are considered, and policy privacy is ensured by only disclosing the names of policy attributes. The values of the attributes are kept hidden. Characteristic comparison with similar existing schemes shows that our scheme simultaneously provides features such as multi-authority setting, expressive and flexible access policy structure, privacy preservation, and scalability. The performance analysis carried out by us shows that the decryption cost is reasonable enough. Furthermore, the scheme is demonstrated to be adaptively secure under the standard model.

The offloading of computationally intensive tasks to edge servers is indispensable in the mobile edge computing (MEC) environment. Once the tasks are offloaded, the subsequent challenges lie in buffering them and assigning them to edge virtual machine (VM) resources to meet the multicriteria requirement. Furthermore, the edge resources’ availability is dynamic in nature and needs a joint prediction and optimal allocation for the efficient usage of resources and fulfillment of the tasks’ requirements. To this end, this work has three contributions. First, a delay sensitivity-based priority scheduling (DSPS) policy is presented to schedule the tasks as per their deadline. Secondly, based on exploratory data analysis and inferred seasonal patterns in the usage of edge CPU resources from the GWA-T-12 Bitbrains VM utilization dataset, the availability of VM resources is predicted by using a Holt–Winters-based univariate algorithm (HWVMR) and a vector autoregression-based multivariate algorithm (VARVMR). Finally, for optimal and fast task assignment, a parallel differential evolution-based task allocation (pDETA) strategy is proposed. The proposed algorithms are evaluated extensively with standard performance metrics, and the results show nearly 22%, 35%, and 69% improvements in cost and 41%, 52%, and 78% improvements in energy when compared with MTSS, DE, and min–min strategies, respectively.

In this paper, several solutions such as initial basic feasible solution (IBFS), optimal solution and degeneracy solution of the transportation problem are given, regarding the drug delivery from drug factories to different warehouses for minimizing the delivery time as well as cost of transportation according to the destination’s requirement. In this Pandemic, it is the most essential part of the pharmaceutical marketing to focus in this cost minimization. The cost of production varies from company to company, and the transportation cost from one company drug factory to multiple warehouses also varies. Each drug factory has some specific production capacity and each warehouse has some certain amount of requirement. To verify the efficiency of this problem, we use Vogel’s method to find IBFS and compare it with the Stepping stone method for optimization of the cost. In this work, we proposed a case study related to the above problem in which the drug items to be shipped from the drug factories to the warehouses, so that the cost of the transportation is minimized. It also explains the degeneracy in the transportation techniques. From the case study, it is found that the minimum transportation cost is Rs. 212 for both techniques. However, it is observed that the Stepping stone method reduces the degeneracy better than the Vogel’s method. For scalability, we have also simulated the methods in MATLAB to observe the results in two cases. From the two cases, it is seen that Stepping stone method shows minimum cost of transportation.

In this paper, the Airline crew scheduling problem is derived from an operational airway to solve some necessary problems in society. During our busy schedule to perform our day-to-day activities generalized monthly airways, and crew scheduling is associated to solve the crew problems. Somewhat recently their part of the issues that emerge in the carrier group Planning issue difficulties to the General public. The significant Difficulties are partitioned into group tasks and team blending in light of its huge size and arrangement completely beginning and its adaptable standards and guidelines of air terminal position. There are bunches of changes that happen to adjust these principles heaps of examination is going on. In this paper, we talk about the carrier team booking issue. By existence limiting the transportation cost of all flight sections from specific time frames to not aggravate the group individuals with the accessible team. functional team planning issue is portrayed on functional aviation scheduling routes. During our everyday activities, summed up week by week aviation routes, team individuals are related to tackling the group issues, which requires coverage of all aircraft at the insignificant expense and maximal benefit. All flight sections from a given period with the accessible team while limiting the unsettling influences of group individuals for taking care of at negligible transportation expense. In this work, we proposed the issues wherein the Aircraft timetable and team plan are fixed in an enhanced manner by giving the information. From the contextual investigation taken, the Optimal solution of the given airline cost between Bhubaneswar and Kolkata crew routes, the base layer over the long run is observed to be 65.5 Hrs.

Internet of Things (IoT) is an expanding technology that facilitate physical devices to interconnect each other over a public channel. Moreover, the security of the next-generation wireless mobile communication technology, namely, 5G with IoT, has been a field of much interest among researchers in the last several years. Previously, Sharif et al. have suggested an IoT-based lightweight three-party authentication scheme proclaiming a secured scheme against different threats. However, it was found that the scheme could not achieve user anonymity and guarantee session key security. Additionally, the scheme fails to provide proper authentication in the login phase, and it s unable to update a new password in the password change phase. Thus, we propose an improved three-factor-based data transmission authentication scheme (TDTAS) to address the weaknesses. The formal security analysis has been proved using the Real-or-Random (RoR) model. The informal security analysis demonstrates that the scheme is secure against several known attacks and achieves more security features. In addition, the comparison of the work with other related schemes demonstrates the proposed scheme has less communicational and storage costs.

Cardiovascular Diseases (CVDs), or heart diseases, are one of the top-ranking causes of death worldwide. About 1 in every 4 deaths is related to heart diseases, which are broadly classified as various types of abnormal heart conditions. However, diagnosis of CVDs is a time-consuming process in which data obtained from various clinical tests are manually analyzed. Therefore, new approaches for automating the detection of such irregularities in human heart conditions should be developed to provide medical practitioners with faster analysis by reducing the time of obtaining a diagnosis and enhancing results. Electronic Health Records(EHRs) are often utilized to discover useful data patterns that help improve the prediction of machine learning algorithms. Specifically, Machine Learning contributes significantly to solving issues like predictions in various domains, such as healthcare. Considering the abundance of available clinical data, there is a need to leverage such information for the betterment of humankind. Researchers have built various predictive models and systems over the years to help cardiologists and medical practitioners analyze data to attain meaningful insights. In this work, a predictive model is proposed for heart disease prediction based on the stacking of various classifiers in two levels(Base level and Meta level). Various heterogeneous learners are combined to produce strong model outcomes. The model obtained 92% accuracy in prediction with precision score of 92.6%, sensitivity of 92.6%, and specificity of 91%. The performance of the model was evaluated using various metrics, including accuracy, precision, recall, F1-scores, and area under the ROC curve values.

The publisher regrets to inform that Declaration of Competing Interest statement was not included in the published version of this article. The publisher would like to apologize for any inconvenience caused. The appropriate Declaration/Competing Interest statements, provided by the Authors, is included below. • All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. • This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. • The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript.

The advent of the Internet of Things (IoT) has resulted in significant technical development in the healthcare sector, enabling the establishment of Medical Cyber–Physical Systems (MCPS). The increased number of MCPS generates a massive amount of privacy-sensitive data, hence it is important to enhance the security of devices and data transmission in MCPS. Earlier several research studies were undertaken in order to enhance security in healthcare, but none of them could adapt to changing behaviors of data attacks. Here the role of blockchain and Reinforcement Learning (RL) comes into play since it can adjust itself to the nature of changing attacks, thus preventing any kind of attacks. This work proposes a solution, named Cogni-Sec, which employs a decentralized cognitive blockchain and Reinforcement Learning architecture and addresses the security issue. Blockchain is incorporated in the approach for data storage and transmission to increase the degree of security in the MCPS modules. Hyperledger Fabric is applied as the blockchain base which shows transaction query results with nearly 10% increased throughput, 69% less memory consumption, and 15% lower CPU usage when compared to Ethereum. Further security risk at the block mining level within a blockchain network is reduced by introducing distributed Reinforcement Learning architecture in replacement for the miner nodes, which imitates the cognitive behavior of miners in a distributed environment. Different multi-agent learning systems have been evaluated for building the mining agent. Among these, the a3c agent in distributed learning setup yields the optimum cumulative reward with a median value of 54.5 and minimizes the maximum number of data threats.

Cardiovascular diseases have the highest fatality rate among the world’s most deadly syndromes. They have become stress, age, gender, cholesterol, Body Mass Index, physical inactivity, and an unhealthy diet are all key risk factors for cardiovascular disease. Based on these parameters, researchers have suggested various early diagnosis methods. However, the correctness of the supplied treatments and approaches needs considerable fine-tuning due to the cardiovascular illnesses’ intrinsic criticality and life-threatening hazards. This paper proposes a framework for accurate cardiovascular disorder prediction based on machine learning techniques. To attain the purpose, the method employs an approach called synthetic minority over-sampling (SMOTE). The benchmark datasets are used to validate the framework for achieving better accuracy, such as Recall and Accuracy. Finally, a comparison has been presented with existing state-of-the-art approaches that shows 99.16% accuracy by a collaborative model by logistic regression and KNN.

Wireless sensor networks (WSNs) have various applications which include zone surveillance, environmental monitoring, event tracking where the operation mode is long term. WSNs are characterized by low-powered and battery-operated sensor devices with a finite source of energy. Due to the dense deployment of these devices practically it is impossible to replace the batteries. The finite source of energy should be utilized in a meaningful way to maximize the overall network lifetime. In the space domain, there is a high correlation among sensor surveillance constituting the large volume of the sensor network topology. Each consecutive observation constitutes the temporal correlation depending on the physical phenomenon nature of the sensor nodes. These spatio-temporal correlations can be efficiently utilized in order to enhance the maximum savings in energy uses. In this paper, we have proposed a Spatial and Temporal Correlation-based Data Redundancy Reduction (STCDRR) protocol which eliminates redundancy at the source level and aggregator level. The estimated performance score of proposed algorithms is approximately 7.2 when the score of existing algorithms such as the KAB (K-means algorithm based on the ANOVA model and Bartlett test) and ED (Euclidian distance) are 5.2, 0.5, respectively. It reflects that the STCDRR protocol can achieve a higher data compression rate, lower false-negative rate, lower false-positive rate. These results are valid for numeric data collected from a real data set. This experiment does not consider non-numeric values.

The recent COVID-19 pandemic has underlined the significance of digital health record management systems for pandemic mitigation. Existing smart healthcare systems (SHSs) fail to preserve system-level medical record openness and privacy while including mitigating measures such as testing, tracking, and treating (3T). In addition, current centralised compute architectures are susceptible to denial of service assaults because of DDoS or bottleneck difficulties. In addition, these current SHSs are susceptible to leakage of sensitive data, unauthorised data modification, and non-repudiation. In centralised models of the current system, a third party controls the data, and data owners may not have total control over their data. The Coviblock, a novel, decentralised, blockchain-based smart healthcare assistance system, is proposed in this study to support medical record privacy and security in the pandemic mitigation process without sacrificing system usability. The Coviblock ensures system-level openness and trustworthiness in the administration and use of medical records. Edge computing and the InterPlanetary File System (IPFS) are recommended as part of a decentralised distributed storage system (DDSS) to reduce the latency and the cost of data operations on the blockchain (IPFS). Using blockchain ledgers, the DDSS ensures system-level transparency and event traceability in the administration of medical records. A distributed, decentralised resource access control mechanism (DDRAC) is also proposed to guarantee the secrecy and privacy of DDSS data. To confirm the Coviblock’s real-time behaviour on an Ethereum test network, a prototype of the technology is constructed and examined. To demonstrate the benefits of the proposed system, we compare it to current cloud-based health cyber–physical systems (H-CPSs) with blockchain. According to the experimental research, the Coviblock maintains the same level of security and privacy as existing H-CPSs while performing considerably better. Lastly, the suggested system greatly reduces latency in operations, such as 32 milliseconds (ms) to produce a new record, 29 ms to update vaccination data, and 27 ms to validate a given certificate through the DDSS.

Most of the recent works on network science are focused on investigating various interactions among a set of entities present in a system that can be represented by multiplex network. Each type of relationship is treated as a layer of multiplex network. Some of the recent works on multiplex networks are focused on deriving layer similarity from node similarity where node similarity is evaluated using neighborhood similarity measures like cosine similarity and Jaccard similarity. But this type of analysis lacks in finding the set of nodes having the same influence in both the network. The discovery of influence similarity between the layers of multiplex networks helps in strategizing cascade effect, influence maximization, network controllability, etc. Towards this end, this paper proposes a pairwise similarity evaluation of layers based on a set of common core nodes of the layers. It considers the number of nodes present in the common core set, the average clustering coefficient of the common core set, and fractional influence capacity of the common core set to quantify layer similarity. The experiment is carried out on three real multiplex networks. As the proposed notion of similarity uses a different aspect of layer similarity than the existing one, a low positive correlation (close to non-correlation) is found between the proposed and existing approach of layer similarity. The result demonstrates that the degree of coreness difference is less for the datasets in the proposed method than the existing one. The existing method reports the coreness difference to be 40% and 18.4% for the datasets CS-AARHUS and EU-AIR TRANSPORTATION MULTIPLEX respectively whereas it is found to be 20% and 8.1% using proposed approach.

Environment monitoring is an important area apart from environmental safety and pollution control. Such monitoring performed by the physical models of the atmosphere is unstable and inaccurate. Machine Learning (ML) techniques on the other hand are more robust in capturing the dynamics in the environment. In this paper, a novel approach is proposed to build a cost-effective standardized environment monitoring system (IoT-EMS) in volunteer computing environment. In volunteer computing, the volunteers (people) share their resources for distributed computing to perform a task (environment monitoring). The system is based on the Internet of Things and is controlled and accessed remotely through the Arduino platform (volunteer resource). In this system, the volunteers record the environment information from the surrounding through different sensors. Then the sensor readings are uploaded directly to a web server database, from where they can be viewed anytime and anywhere through a website. Analytics on the gathered time-series data is achieved through ML data modeling using R Language and RStudio IDE. Experimental results show that the system is able to accurately predict the trends in temperature, humidity, carbon monoxide level, and carbon dioxide. The prediction accuracy of different ML techniques such as MLP, k-NN, multiple regression, and SVM are also compared in different scenarios.

Social networks are often published in the form of a simple graph. The simple graph representation of a social graph shows the dyadic relationship among the social entities whereas it is unable to efficiently represent the relationship among more than two entities, such as the relationship found in the social groups. This type of relationship is called super-dyadic relationship, and it can be effectively represented by a hypergraph model. This work proposes an anonymization scheme called rank-label anonymization for the privacy-preserving publication of a hypergraph structure. Here, an attack model called rank-label attack is proposed, and an anonymization solution is provided to counter this attack. The percentage of disclosure risk shows that the rank-label attack is stronger than the existing rank attack. We propose a method based on sequential clustering to achieve rank-label anonymization called sequential rank-label anonymization (SA). Another algorithm called greedy rank-label anonymization (GA) is also proposed. The quality of the anonymization solution reported by SA and GA is compared with the help of normalized anonymization cost (NCost). Results show that the NCost reported by SA is less than that of GA for both Adult and MAG-10 datasets. In Adult dataset, approximately 58% and 62% reduction in the average execution time of GA and SA are obtained than that of a general-purpose computing system due to the use of a high-performance computing system. In MAG-10 dataset, this average reduction percentage is reported to be 56% for GA and 53% for SA. The time complexity of SA is found to be O(n4) whereas it is O(n3) in case of GA.

Vision impairment is a major challenge faced by humanity on a large scale throughout the world. Affected people find independently navigating and detecting obstacles extremely tedious. Thus, a potential solution for accurately detecting obstacles requires an integrated deployment of the Internet of Things and predictive analytics. This research introduces "Vision Navigator,"a novel framework for assisting visually impaired users in obstacle analysis and tracking so that they can move independently. An intelligent stick named "Smart-fold Cane"and sensor-equipped shoes called "Smart-alert Walker"are the main constituents of our proposed model. For object detection and classification, the stick uses a single-shot detection (SSD) mechanism, which is followed by frame generation using the recurrent neural network (RNN) model. Smart-alert Walker is a lightweight shoe that acts as an emergency unit that notifies the user regarding the presence of any obstacle within a short distance range. This intelligent obstacle detection model using the SSD-RNN approach was deployed in real time and its performance was validated in indoor and outdoor environments. The SSD-RNN model computed an optimum accuracy of 95.06% and 87.68% indoors and outdoors, respectively. The model was also evaluated in the context of users' distance from obstacles. The proposed SSD-RNN model had an accuracy rate of 96.4% and 86.8% for close and distant obstacles, respectively, outperforming other models. Execution time for the SSD-RNN model was 4.82 s with the highest mean accuracy rate of 95.54% considering all common obstacles.

This paper develops an improved (more effective) and safer technology for detecting COVID-19 and thus contributes to the literature and the control of COVID-19. Coronavirus is a new infection that causes the coronavirus ailment called COVID-19. This disease was first found in bat at Wuhan, China, in December 2019. Starting from that time, it has spread rapidly throughout the globe. One of the main identifications of COVID-19 is that it can be handily distinguished by fever. Since this flare-up has begun, 'temperature screening utilizing infrared thermometers and RT-PCR has been utilized in advanced and developed countries to check the warmth of the body to identify the infected person. This is not a very effective way of detection, as it demands huge manpower and infrastructure to go and check one-by-one. Moreover, the close contact between the infected and the person checking can lead to the spread of coronavirus at a faster pace. This paper proposes a framework that can detect the coronavirus instantly and non-invasively from a human cough voice. The proposed framework is much safer as compared to conventional technologies used, as it reduces human interactions to a greater extent. It uses spectrographic images of the voice for COVID detection. This framework has been deployed in a web application to use them from any part of the world without exposing themselves to other infected people. This method encourages non-invasive mechanisms that will prevent from hurting sensitive areas, unlike conventional procedures.

Due to the rapid expansion of the Internet of Things (IoT), modern buildings are equipped with ubiquitous networked devices and ambient sensors. This transformation opened a new opportunity for occupancy detection using the current infrastructure. In the literature review, we identified various solutions that can facilitate passive localization using radio, motion sensors, RFID, NFC, and air quality sensors. However, radio devices are the most effective and inexpensive solution for occupancy detection. Passive localization (PL) is an emerging area of indoor occupancy detection. It has various applications in perimeter security, elderly monitoring, and the intelligent healthcare sector. In PL, the target is localized using the target-induced shadowed links within the wireless network. Researchers investigated various approaches for passive local-ization using analytical geometry and fingerprinting techniques. However, the existing fingerprinting and geometric schemes are computationally expensive and need a dense wireless network for greater accuracy. We have obtained better accuracy in the proposed PL scheme within a sparsely deployed wireless network. The performance of the proposed PL scheme is compared with the state-of-the-art Geometric filter (GF) scheme. The obtained results show that the proposed PL scheme achieved 0.21m mean accuracy of target tracking with 0.20 m of Root Mean Square Error (RMSE).

Indian Railways, the nation’s transport lifeline, is the world’s fourth-largest railway network, with more than 70,000 passenger coaches and over 11,000 locomotives. Hence, ensuring the safety and security of the people is a much-prioritized issue these days. According to a survey, an average of 110 accidents occurred each year between 2013 and 2018, in which around 990 people were killed and 1,500 injured. About 54% of accidents were due to reasons like lever crossing, fire, collisions, run away, and suicides. Such problems need to be analyzed intelligently in a cognitive manner based on human behavior and movement. The idea that communities or groups of people provide data similar to those obtainable from a single sensor is known as social sensing. This chapter presents a brief idea about social sensing and how it relates to Big Data. A framework based on the assimilation of Big data, Internet-of-Things, and Cloud Computing is presented for the entire railway operation mechanism. Along with that, some accident prevention techniques with Metaheuristics strategies are also discussed. Finally, the paper concludes with a discussion on some future research axes.

Background and objective: The world is currently facing a global emergency due to COVID-19, which requires immediate strategies to strengthen healthcare facilities and prevent further deaths. To achieve effective remedies and solutions, research on different aspects, including the genomic and proteomic level characterizations of SARS-CoV-2, are critical. In this work, the spatial representation/composition and distribution frequency of 20 amino acids across the primary protein sequences of SARS-CoV-2 were examined according to different parameters. Method: To identify the spatial distribution of amino acids over the primary protein sequences of SARS-CoV-2, the Hurst exponent and Shannon entropy were applied as parameters to fetch the autocorrelation and amount of information over the spatial representations. The frequency distribution of each amino acid over the protein sequences was also evaluated. In the case of a one-dimensional sequence, the Hurst exponent (HE) was utilized due to its linear relationship with the fractal dimension (D), i.e. D+HE=2, to characterize fractality. Moreover, binary Shannon entropy was considered to measure the uncertainty in a binary sequence then further applied to calculate amino acid conservation in the primary protein sequences. Results and conclusion: Fourteen (14) SARS-CoV protein sequences were evaluated and compared with 105 SARS-CoV-2 proteins. The simulation results demonstrate the differences in the collected information about the amino acid spatial distribution in the SARS-CoV-2 and SARS-CoV proteins, enabling researchers to distinguish between the two types of CoV. The spatial arrangement of amino acids also reveals similarities and dissimilarities among the important structural proteins, E, M, N and S, which is pivotal to establish an evolutionary tree with other CoV strains.

Problems like improper sampling (sampling on unnecessary variables) and undefined prior distribution (or taking random priors) often occur in model updating. Any such limitations on model parameters can lead to lower accuracy and higher experimental costs (due to more iterations) of structural optimisation. In this work, we explored the effective dimensionality of the model updating problem by leveraging the causal information. In order to utilise the causal structure between the parameters, we used Causal Bayesian Optimisation (CBO), a recent variant of Bayesian Optimisation, to integrate observational and intervention data. We also employed generative models to generate synthetic observational data, which helps in creating a better prior for surrogate models. This case study of a coupled slab structure in a recreational building resulted in the modal updated frequencies which were extracted from the finite element of the structure and compared to measured frequencies from ambient vibration tests found in the literature. The results of mode shapes between experimental and predicted values were also compared using modal assurance criterion (MAC) percentages. The updated frequency and MAC number that was obtained using the proposed model was found in least number of iterations (impacts experimental budget) as compared to previous approaches which optimise the same parameters using same data. This also shows how the causal information has impact on experimental budget.

Distributed denial of service (DDoS) attacks have always been a nightmare for network infrastructure for the last two decades. Existing network infrastructure is lacking in identifying and mitigating the attack due to its inflexible nature. Currently, software-defined networking (SDN) is more popular due to its ability to monitor and dynamically configure network devices based on the global view of the network. In SDN, the control layer is accountable for forming all decisions in the network and data plane for just forwarding the message packets. The unique property of SDN has brought a lot of excitement to network security researchers for preventing DDoS attacks. In this article, for the identification of DDoS attacks in the OpenFlow controller, a probabilistic technique with a central limit theorem has been utilized. This method primarily detects resource depletion attacks, for which the DARPA dataset is used to train the probabilistic model. In different attack scenarios, the probabilistic approach outperforms the entropy-based method in terms of false negative rate (FNR). The emulation results demonstrate the efficacy of the approach, by reducing the FNR by 98% compared to 78% in the existing entropy mechanism, at 50% attack rate.

The Software Defined Networking (SDN) paradigm decouples the network control functions from the data plane and offers a set of software components for flexible and controlled management of networks. SDN has promised to provide numerous benefits in terms of on-demand provisioning, automated load balancing, streamlining physical infrastructure, and flexibility in scaling network resources. In order to realize these network service offerings, there is an important need for developing an efficient, robust, and secure execution platform. As a primary contribution, we present a novel virtual execution platform for the OpenFlow controller using Network Function Virtualization (NFV). Theoretically, NFV can apply to any network function, which can simplify the managing of the heterogeneous data plane. The characteristics of our proposed architecture include pipe-lined processing of network traffic, virtualized and replicated execution of network functions, isolation between task nodes, and random mapping of traffic to task nodes. The proposed architecture has two major components: a Network Packet Schedulers (NPS) and a Task Engine (TE). The TE consists of Task Nodes (TNs) which are responsible for executing different network functions on various traffic flows and each TN is realized as a virtual machine. Upon receiving traffic from the data plane, NPS analyses the functional requirements of the traffic and different controller performance parameters. Then it allocates the traffic to appropriate TNs for executing necessary network functions. In this respect, it provides performance benefits, robustness, fine-grained modularity, and strong isolation security in the processing of traffic flows on the SDN platform. Efficacy of our proposed architecture has been demonstrated with a case study.

Machine learning methods are widely used to implement intrusion detection models for detecting and classifying intrusions in a network or a system. However, many challenges arise since hackers continuously change the attacking patterns by discovering new system vulnerabilities. The degree of malicious attempts increases rapidly; as a result, conventional approaches fail to process voluminous data. So, a sophisticated detection approach with scalable solutions is required to tackle the problem. A deep learning model is proposed to address the intrusion classification problem effectively. The LSTM (Long Short-Term Memory) and FCN (Fully Connected Network) deep learning approaches classify the benign and malicious connections on intrusion datasets. The objective is to classify multi-class attack patterns more accurately. The proposed deep learning model provides a better classification result in two-class and five-class problems. It achieves an accuracy of 98.52%, 98.94%, 99.03%, 99.36%, 100%, and 99.64% using KDDCup99, NSLKDD, GureKDD, KDDCorrected, Kyoto, NITRIDS dataset respectively.

Osmotic computing in association with related computing paradigms (cloud, fog, and edge) emerges as a promising solution for handling bulk of security-critical as well as latency-sensitive data generated by the digital devices. It is a growing research domain that studies deployment, migration, and optimization of applications in the form of microservices across cloud/edge infrastructure. It presents dynamically tailored microservices in technology-centric environments by exploiting edge and cloud platforms. Osmotic computing promotes digital transformation and furnishes benefits to transportation, smart cities, education, and healthcare. In this article, we present a comprehensive analysis of osmotic computing through a systematic literature review approach. To ensure high-quality review, we conduct an advanced search on numerous digital libraries to extracting related studies. The advanced search strategy identifies 99 studies, from which 29 relevant studies are selected for a thorough review. We present a summary of applications in osmotic computing build on their key features. On the basis of the observations, we outline the research challenges for the applications in this research field. Finally, we discuss the security issues resolved and unresolved in osmotic computing.

IoT technology is revolutionizing healthcare and is transforming it into more personalized healthcare. In the context of COVID-19 pandemic, IoT`s intervention can help to detect its spread. This research proposes an effective “Smart COVID-Shield” that is capable of automatically detecting prevalent symptoms like fever and coughing along with ensuring social distancing norms are properly followed. It comprises three modules which include Cough Detect Module (CDM) for dry cough detection, Temperature Detect module (TDM) for high-temperature monitoring, and Distance Compute Module (DCM) to track social distancing norm violator. The device comprises a combination of a lightweight fabric suspender worn around shoulders and a flexible belt wrapped around the waist. The suspender is equipped with a passive infrared (PIR) sensor and temperature sensor to monitor persistent coughing patterns and high body temperature and the ultra-sonic sensor verify 6 feet distance for tracking an individual's social distancing norms. The developed model is implemented in an aluminum factory to verify its effectiveness. Results obtained were promising and reliable when compared to conventional manual procedures. The model accurately reported when body temperature rises. It outperformed thermal gun as it accurately recorded a mean of only 4.65 candidates with higher body temperature as compared to 8.59% with the thermal gun. A significant reduction of 3.61% on social distance violators was observed. Besides this, the latency delay of 10.32 s was manageable with the participant count of over 800 which makes it scalable.

Social network analysis (SNA) has attracted a lot of attention in several domains in the past decades. It can be of 2-folds: one is content-based, and another one is structured-based analysis. Link prediction is one of the emerging research problems, which comes under structured-based analysis that deals with predicting the missing link, which is likely to appear in the future. In this article, the supervised machine learning techniques have been implemented to predict the possibilities of establishing the links in future. The major contribution in this article lies in feature construction from the topological structure of the network. Several structured-based similarity measures have been considered for preparing the feature vector for each nonexisting links in the network. The performance of the proposed algorithm has been extensively validated by comparing with other link prediction algorithms using both real-world and synthetic data sets.

Software-defined networks (SDNs) can help facilitate dynamic network resource provisioning in demanding applications, such as those involving Industrial Internet of Things (IIoT) devices and systems. For example, SDN-based systems can support increasing demands of multitenancy at the network layer, complex demands of microservices, etc. A typical (large) manufacturing setting generally comprises a broad and diverse range of IoT devices and applications to support different services (e.g., transactions on enterprise resource planning (ERP) software, maintenance prediction, asset management, and outage prediction). Hence, this work introduces a demand-supply-based economic model to enhance the efficiency of different multitenancy attributes at the network layer, which captures the computational complexity of industrial ERP-IoT transactions and performs network resource provisioning, based on the demand-supply principle. The proposed model is accompanied by a flow scheduler, which dynamically assigns ERP-IoT traffic flow entries on network devices to specific preconfigured queues. This scheduler is used to increase service providers' utility. The evaluation of the proposed model suggests the utility of our proposed approach.

In recent years, we have seen the impact of global warming on changing weather patterns. The changing weather patterns have shown a significant effect on the annual rainfall. Due to the lack of annual rainfall, developing countries like India have seen a substantial loss in annual crop production. Indian economy largely depends on agro products. To compensate for the economic loss, the Indian government encouraged the farmers to do integrated aquaculture-based farming. Despite government subsidies and training programs, most farmers find it difficult to succeed in aquaculture-based farming. Aquaculture farming needs skills to maintain and monitor underwater environments. The lack of skills for monitoring and maintenance makes the aquaculture business more difficult for farmers. To simplify the pearl farming aquaculture, we have proposed an Internet of Things (IoT)-based intelligent monitoring and maintenance system. The proposed system monitors the water quality and maintains an adequate underwater environment for better production. To maintain an aquaculture environment, we have forecasted the change in water parameters using an ensemble learning method based on random forests (RF). The performance of the RF model compared with the linear regression (LR), support vector regression (SVR), and gradient boosting machine (GBM). The obtained results show that the RF model outperformed the forecast of the DO with 1.428 mean absolute error (MAE) and pH with 0.141 MAE.

Multi-focus image fusion (MIF) uses fusion rules to combine two or more images of the same scene with various focus values into a fully focused image. An all-in-focus image refers to a fully focused image that is more informative and useful for visual perception. A fused image with high quality is essential for maintaining shift-invariant and directional selectivity characteristics of the image. Traditional wavelet-based fusion methods, in turn, create ringing distortions in the fused image due to a lack of directional selectivity and shift-invariance. In this paper, a classical MIF system based on quarter shift dual-tree complex wavelet transform (qshiftN DTCWT) and modified principal component analysis (MPCA) in the laplacian pyramid (LP) domain is proposed to extract the focused image from multiple source images. In the proposed fusion approach, the LP first decomposes the multi-focus source images into low-frequency (LF) components and high-frequency (HF) components. Then, qshiftN DTCWT is used to fuse low and high-frequency components to produce a fused image. Finally, to improve the effectiveness of the qshiftN DTCWT and LP-based method, the MPCA algorithm is utilized to generate an all-in-focus image. Due to its directionality, and its shift-invariance, this transform can provide high-quality information in a fused image. Experimental results demonstrate that the proposed method outperforms many state-of-the-art techniques in terms of visual and quantitative evaluations.

Fog computing has been prioritized over cloud computing in terms of latency-sensitive Internet of Things (IoT) based services. We consider a limited resource-based fog system where real-time tasks with heterogeneous resource configurations are required to allocate within the execution deadline. Two modules are designed to handle the real-time continuous streaming tasks. The first module is task classification and buffering (TCB), which classifies the task heterogeneity using dynamic fuzzy c-means clustering and buffers into parallel virtual queues according to enhanced least laxity time. The second module is task offloading and optimal resource allocation (TOORA), which decides to offload the task either to cloud or fog and also optimally assigns the resources of fog nodes using the whale optimization algorithm, which provides high throughput. The simulation results of our proposed algorithm, called whale optimized resource allocation (WORA), is compared with results of other models, such as shortest job first (SJF), multi-objective monotone increasing sorting-based (MOMIS) algorithm, and Fuzzy Logic based Real-time Task Scheduling (FLRTS) algorithm. When 100 to 700 tasks are executed in 15 fog nodes, the results show that the WORA algorithm saves 10.3% of the average cost of MOMIS and 21.9% of the average cost of FLRTS. When comparing the energy consumption, WORA consumes 18.5% less than MOMIS and 30.8% less than FLRTS. The WORA also performed 6.4% better than MOMIS and 12.9% better than FLRTS in terms of makespan and 2.6% better than MOMIS and 4.3% better than FLRTS in terms of successful completion of tasks.

The tremendous expansion of the Internet of Things (IoTs) has generated an enormous volume of near and remote sensing data, which is increasing with the emergence of new solutions for sustainable environments. Cloud computing is typically used to help resource-constrained IoT sensing devices. However, the cloud servers are placed deep within the core network, a long way from the IoT, introducing immense data transactions. These transactions require heavy electricity consumption and release harmful (Formula presented.) to the environment. A distributed computing environment located at the edge of the network named fog computing has been promoted to reduce the limitation of cloud computing for IoT applications. Fog computing potentially processes real-time and delay-sensitive data, and it reduces the traffic, which minimizes the energy consumption. The additional energy consumption can be reduced by implementing an energy-aware task scheduling, which decides on the execution of tasks at cloud or fog nodes on the basis of minimum completion time, cost, and energy consumption. In this paper, an algorithm called energy-efficient makespan cost-aware scheduling (EMCS) is proposed using an evolutionary strategy to optimize the execution time, cost, and energy consumption. The performance of this work is evaluated using extensive simulations. Results show that EMCS is 67.1% better than cost makespan-aware scheduling (CMaS), 58.79% better than Heterogeneous Earliest Finish Time (HEFT), 54.68% better than Bees Life Algorithm (BLA) and 47.81% better than Evolutionary Task Scheduling (ETS) in terms of makespan. Comparing the cost of the EMCS model, it uses 62.4% less cost than CMaS, 26.41% less than BLA, and 6.7% less than ETS. When comparing energy consumption, EMCS consumes 11.55% less than CMaS, 4.75% less than BLA and 3.19% less than ETS. Results also show that with an increase in the number of fog and cloud nodes, the balance between cloud and fog nodes gives better performance in terms of makespan, cost, and energy consumption.

In recent years, vehicular cloud computing (VCC) has gained vast attention for providing a variety of services by creating virtual machines (VMs). These VMs use the resources that are present in modern smart vehicles. Many studies reported that some of these VMs hosted on the vehicles are overloaded, whereas others are underloaded. As a circumstance, the energy consumption of overloaded vehicles is drastically increased. On the other hand, underloaded vehicles are also drawing considerable energy in the underutilized situation. Therefore, minimizing the energy consumption of the VMs that are hosted by both overloaded and underloaded is a challenging issue in the VCC environment. The proper and efficient utilization of the vehicle’s resources can reduce energy consumption significantly. One of the solutions is to improve the resource utilization of underloaded vehicles by migrating the over-utilized VMs of overloaded vehicles. On the other hand, a large number of VM migrations can lead to wastage of energy and time, which ultimately degrades the performance of the VMs. This paper addresses the issues mentioned above by introducing a resource management algorithm, called resource utilization-aware VM migration (RU-VMM) algorithm, to distribute the loads among the overloaded and underloaded vehicles, such that energy consumption is minimized. RU-VMM monitors the trend of resource utilization to select the source and destination vehicles within a predetermined threshold for the process of VM migration. It ensures that any vehicles’ resource utilization should not exceed the threshold before or after the migration. RU-VMM also tries to avoid unnecessary VM migrations between the vehicles. RU-VMM is extensively simulated and tested using nine datasets. The results are carried out using three performance metrics, namely number of final source vehicles (nfsv), percentage of successful VM migrations (psvmm) and percentage of dropped VM migrations (pdvmm), and compared with threshold-based algorithm (i.e., threshold) and cumulative sum (CUSUM) algorithm. The comparisons show that the RU-VMM algorithm performs better than the existing algorithms. RU-VMM algorithm improves 16.91% than the CUSUM algorithm and 71.59% than the threshold algorithm in terms of nfsv, and 20.62% and 275.34% than the CUSUM and threshold algorithms in terms of psvmm.

In the traditional irrigation process, a huge amount of water consumption is required which leads to water wastage. To reduce the wasting of water for this tedious task, an intelligent irrigation system is urgently needed. The era of machine learning (ML) and the Internet of Things (IoT) brings it is a great advantage of building an intelligent system that performs this task automatically with minimal human effort. In this study, an IoT enabled ML-trained recommendation system is proposed for efficient water usage with the nominal intervention of farmers. IoT devices are deployed in the crop field to precisely collect the ground and environmental details. The gathered data are forwarded and stored in a cloud-based server, which applies ML approaches to analyze data and suggest irrigation to the farmer. To make the system robust and adaptive, an inbuilt feedback mechanism is added to this recommendation system. The experimentation, reveals that the proposed system performs quite well on our own collected dataset and National Institute of Technology (NIT) Raipur crop dataset.

Coronaviruses are responsible for various diseases ranging from the common cold to severe infections like the Middle East syndromes and the severe acute respiratory syndrome. However, a new coronavirus strain known as COVID-19 developed into a pandemic resulting in an ongoing global public health crisis. Therefore, there is a need to understand the genomic transformations that occur within this family of viruses in order to limit disease spread and develop new therapeutic targets. The nucleotide sequences of SARS-CoV-2 are consist of several bases. These bases can be classified into purines and pyrimidines according to their chemical composition. Purines include adenine (A) and guanine (G), while pyrimidines include cytosine (C) and tyrosine (T). There is a need to understand the spatial distribution of these bases on the nucleotide sequence to facilitate the development of antivirals (including neutralizing antibodies) and epitomes necessary for vaccine development. This study aimed to evaluate all the purine and pyrimidine associations within the SARS-CoV-2 genome sequence by measuring mathematical parameters including; Shannon entropy, Hurst exponent, and the nucleotide guanine-cytosine content. The Shannon entropy is used to identify closely associated sequences. Whereas Hurst exponent is used to identifying the auto-correlation of purine-pyrimidine bases even if their organization differs. Different frequency patterns can be used to determine the distribution of all four proteins and the density of each base. The GC-content is used to understand the stability of the DNA. The relevant genome sequences were extracted from the National Center for Biotechnology Information (NCBI) virus database. Furthermore, the phylogenetic properties of the COVID-19 virus were characterized to compare the closeness of the COVID-19 virus with other coronaviruses by evaluating the purine and pyrimidine distribution.

Reliable vehicles are essential in vehicular networks for effective communication. Since vehicles in the network are dynamic, even a short span of misbehavior by a vehicle can disrupt the whole network which may lead to catastrophic consequences. In this paper, a Trust-Based Distributed DoS Misbehave Detection Approach (TBDDoSA-MD) is proposed to secure the Software-Defined Vehicular Network (SDVN). A malicious vehicle in this network performs DDoS misbehavior by attacking other vehicles in its neighborhood. It uses the jamming technique by sending unnecessary signals in the network, as a result, the network performance degrades. Attacked vehicles in that network will no longer meet the service requests from other vehicles. Therefore, in this paper, we proposed an approach to detect the DDoS misbehavior by using the trust values of the vehicles. Trust values are calculated based on direct trust and recommendations (indirect trust). These trust values help to decide whether a vehicle is legitimate or malicious. We simply discard the messages from malicious vehicles whereas the authenticity of the messages from legitimate vehicles is checked further before taking any action based on those messages. The performance of TBDDoSA-MD is evaluated in the Veins hybrid simulator, which uses OMNeT++ and Simulation of Urban Mobility (SUMO).We compared the performance of TBDDoSA-MD with the recently proposed Trust-Based Framework (TBF) scheme using the following performance parameters such as detection accuracy, packet delivery ratio, detection time, and energy consumption. Simulation results show that the proposed work has a high detection accuracy of more than 90% while keeping the detection time as low as 30 s.

This work introduces and aim to overcome the potential challenges while deploying automated tuning of relational database as a service for a Platform as a Service (PaaS) provider. Some of the major challenges identified in this work include (i) automated detection of performance throttling (figure out when the performance of the system is affected due to incorrect configurations of knobs) of a database and identify potential points where a database requires a tuning, (ii) scalability and accuracy of tuning service and (iii) applying the recommendations obtained from tuning services wherein applying an obtained recommendation might require a database restart. In this work, we present a generic tuning service architecture for PaaS providers. To deal with the above challenges, we introduce performance throttling engine which is responsible to detect potential points when a relational database actually needs a knob tuning, which helps in increasing the scalability and accuracy of the tuner deployments (responsible for tuning production landscapes). This work also proposes approaches that facilitate efficiently applying the recommendations without causing much disruption in Quality of Service (QoS) of the underlying database system. Lastly, the results are obtained by evaluation of the proposed methods and modules on multiple cloud native provisioners against various set of metrics.

The Internet of Things is visualized as a fundamental networking model that bridges the gap between the cyber and real-world entity. Uniting the real-world object with virtualization technology is opening further opportunities for innovation in nearly every individual's life. Moreover, the usage of smart heterogeneous multimedia devices is growing extensively. These multimedia devices that communicate among each other through the Internet form a unique paradigm called the Internet of Multimedia Things (IoMT). As the volume of the collected data in multimedia application increases, the security, reliability of communications, and overall quality of service need to be maintained. Primarily, distributed denial of service attacks unveil the pervasiveness of vulnerabilities in IoMT systems. However, the Software Defined Network (SDN) is a new network architecture that has the central visibility of the entire network, which helps to detect any attack effectively. In this regard, the combination of SDN and IoMT, termed SD-IoMT, has the immense ability to improve the network management and security capabilities of the IoT system. This article proposes an SDN-assisted two-phase detection framework, namely SD-IoMT-Protector, in which the first phase utilizes the entropy technique as the detection metric to verify and alert about the malicious traffic. The second phase has trained with an optimized machine learning technique for classifying different attacks. The outcomes of the experimental results signify the usefulness and effectiveness of the proposed framework for addressing distributed denial of service issues of the SD-IoMT system.

Rapid urbanisation has led to degradation in air quality index in past decades caused by pollutants generated by factories, industries and transportation. Designing an automated system for air quality tracking and monitoring is essential for generating awareness. Restrictions imposed by COVID 19 lockdown has resulted in the degradation of pollutants in air and having a great impact on air pollution management. An analysis of air pollution index based on vehicular pollutants and industrial pollutants is done, depicting the most polluted cities in India. Various machine learning models are compared so, as to figure out a better model for classification and analysis. Results delineate that Delhi was one of the most polluted cities before lockdown but shown a tremendous decrease in air pollution index after lockdown. Further boosting models proved to outperform other models in the prediction and forecasting of air quality index.

Malaria is a severe epidemic disease caused by Plasmodium falciparum. The parasite causes critical illness if persisted for longer durations and delay in precise treatment can lead to further complications. The automatic diagnostic model provides aid for medical practitioners to avail a fast and efficient diagnosis. Most of the existing work either utilizes a fully connected convolution neural network with successive pooling layers which causes loss of information in pixels. Further, convolutions can capture spatial invariances but, cannot capture rotational invariances. Hence to overcome these limitations, this research, develops an Imperative Dynamic routing mechanism with fully trained capsule networks for malaria classification. This model identifies the presence of malaria parasites by classifying thin blood smears containing samples of parasitized and healthy erythrocytes. The proposed model is compared and evaluated with novel machine vision models evolved over a decade such as VGG, ResNet, DenseNet, MobileNet. The problems in previous research are cautiously addressed and overhauled using the proposed capsule network by attaining the highest Area under the curve (AUC) and Specificity of 99.03% and 99.43% respectively for 20% test samples. To understand the underlying behavior of the proposed network various tests are conducted for variant shuffle patterns. The model is analyzed and assessed in distinct environments to depict its resilience and versatility. To provide greater generalization, the proposed network has been tested on thick blood smear images which surpassed with greater performance.

In the above article [1], the corresponding author was incorrectly identified. The corresponding author is the first author, S. Nithya.

Link prediction is an emerging research problem in social network analysis, where future possible links are predicted based on the structural or the content information associated with the network. In this paper, various machine learning (ML) techniques have been utilized for predicting the future possible links based on the features extracted from the topological structure. Moreover, feature sets have been prepared by measuring different similarity metrics between all pair of nodes between which no link exists. For predicting the future possible links various supervised ML algorithms like K-NN, MLP, bagging, SVM, decision tree have been implemented. The feature set for each instance in the dataset has been prepared by measuring the similarity index between the non-existence links. The model has been trained to identify the new links which are likely to appear in the future but currently do not exist in the network. Further, the proposed model is validated through various performance metrics.

Software defined networking has solved many challenging issues in the field of networking industry. It separates the control plane from the data forwarding plane. This makes SDN to be more powerful than traditional networking. However, energy cost enhances the overall network cost. Therefore, this issue needs to be addressed to improve design requirements and boost the networking performance. In this article, several energy efficiency techniques have been discussed. To represent it in more detail, a thematic taxonomy of energy efficiency techniques in SDN is given by considering several technical studies of the past research. These studies have been categorized into three sub categories of traffic aware model, end-host aware model and finally rule placement. These models are provided with detailed objective functions, parameters, constraints and detailed information. Furthermore, useful visions of each approach, its advantages and disadvantages and compressive analysis of energy efficiency techniques are also discussed. Finally, the paper is highlighted with the future directions for energy efficiency in SDN.

In this study, we set up a scalable framework for large-scale data processing and analytics using the big data framework. The popular classification methods are implemented, tuned, and evaluated by using intrusion datasets. The objective is to select the best classifier after optimizing the hyper-parameters. We observed that the decision tree (DT) approach outperforms compared with other methods in terms of classification accuracy, fast training time, and improved average prediction rate. Therefore, it is selected as a base classifier in our proposed ensemble approach to study class imbalance. As the intrusion datasets are imbalanced, most of the classification techniques are biased toward the majority class. The misclassification rate is more in the case of the minority class. An ensemble-based method is proposed by using K-Means, RUSBoost, and DT approaches to mitigate the class imbalance problem; empirically investigate the impact of class imbalance on classification approaches' performance; and compare the result by using popular performance metrics such as Balanced Accuracy, Matthews Correlation Coefficient, and F-Measure, which are more suitable for the assessment of imbalanced datasets.

Vehicular clouds (VCs) have become a promising research area due to its on-demand solutions, resource pooling, unified services, autonomous cloud formation and transformational management. It makes use of the underutilized resources of vehicles on the parking lot, roadways, driveways and streets, and creates the infrastructure to support various services offered by the cloud service provider (CSP) by deploying virtual machines (VMs). However, these vehicles can leave the coverage/grid of VC due to its mobility and change in the environment. Therefore, the hosted VMs on those vehicles can be transferred to other potential vehicles (i.e., migration) in order to avoid disruption of services. These services can be viewed as user requests (URs) submitted to the CSP by cloud users. Here, the challenging tasks are to map the URs to the VMs (or vehicles) and identify the potential vehicles for migration, and they need immediate attention. In this paper, we propose a smart cloud service management (SCSM) algorithm for VCs and address the above challenges. This algorithm consists of three phases, namely assignment of vehicles to grids, URs to grids and URs to vehicles by considering the mobility pattern of vehicles. The performance of SCSM is assessed using three traffic congestion scenarios and thirty-six instances of four datasets, and compared with round-robin (RR) and deficit weighted RR (DWRR) using seven performance metrics. The comparison results show that SCSM achieves 58% and 57% (33% and 33%) better than RR and DWRR in makespan (number of migrations) and other performance metrics.

The mobile users offload the application to nearby cloudlet servers instead of the remote cloud for better end-user experience. Each cloudlet is able to process real-time applications with the help of virtual machines (VM). While multiple applications running on the cloudlet, the possibility of overprovisioning issue is unavoidable due to massive task-offloading requests from mobile devices. In this regard, balancing the load, among the cloudlets in a high-interactive applications scenario, is a promising issue. In order to balance the cloudlet load, migration of VMs from an overloaded cloudlet to an underloaded cloudlet is a favored solution. During this process, a well-designed migration mechanism must be outlined that can perform two steps such as VM selection and cloudlet adaption. In this article, an application-aware cloudlet adaption and VM selection framework has been devised for balancing the load in a multicloudlet environment. The candidate-cloudlet adaption is based on a migration efficiency indicator that reduces the response time and enhances load-balancing rate. Furthermore, the effectiveness of the framework has been evaluated by comparing with other state-of-the-art cloudlet-selection strategies.

In recent years, the growth rate of Cloud computing technology is increasing exponentially, mainly for its extraordinary services with expanding computation power, the possibility of massive storage, and all other services with the maintained quality of services (QoSs). The task allocation is one of the best solutions to improve different performance parameters in the cloud, but when multiple heterogeneous clouds come into the picture, the allocation problem becomes more challenging. This research work proposed a resource-based task allocation algorithm. The same is implemented and analyzed to understand the improved performance of the heterogeneous multi-cloud network. The proposed task allocation algorithm (Energy-aware Task Allocation in Multi-Cloud Networks (ETAMCN)) minimizes the overall energy consumption and also reduces the makespan. The results show that the makespan is approximately overlapped for different tasks and does not show a significant difference. However, the average energy consumption improved through ETAMCN is approximately 14%, 6.3%, and 2.8% in opposed to the random allocation algorithm, Cloud Z-Score Normalization (CZSN) algorithm, and multi-objective scheduling algorithm with Fuzzy resource utilization (FR-MOS), respectively. An observation of the average SLA-violation of ETAMCN for different scenarios is performed.

Software-Defined Network (SDN) has become a promising network architecture in current days that provide network operators more control over the network infrastructure. The controller, also called as the operating system of the SDN, is responsible for running various network applications and maintaining several network services and functionalities. Despite all its capabilities, the introduction of various architectural entities of SDN poses many security threats and potential targets. Distributed Denial of Services (DDoS) is a rapidly growing attack that poses a tremendous threat to the Internet. As the control layer is vulnerable to DDoS attacks, the goal of this paper is to detect the attack traffic, by taking the centralized control aspect of SDN. Nowadays, in the field of SDN, various machine learning (ML) techniques are being deployed for detecting malicious traffic. Despite these works, choosing the relevant features and accurate classifiers for attack detection is an open question. For better detection accuracy, in this work, Support Vector Machine (SVM) is assisted by kernel principal component analysis (KPCA) with genetic algorithm (GA). In the proposed SVM model, KPCA is used for reducing the dimension of feature vectors, and GA is used for optimizing different SVM parameters. In order to reduce the noise caused by feature differences, an improved kernel function (N-RBF) is proposed. The experimental results show that compared to single-SVM, the proposed model achieves more accurate classification with better generalization. Moreover, the proposed model can be embedded within the controller to define security rules to prevent possible attacks by the attackers.

In Software Defined Networks (SDN), the decoupling of the control logic from the data plane provides numerous advantages. Since its inception, SDN is a subject of a wide range of criticism mainly related to the scalability issues of the control plane. To address these limitations, recent architectures have supported the implementation of multiple controllers. Usage of multiple controllers leads to Controller Placement Problems (CPP) particularly in wide area networks. In most of the placement strategies, authors focused on propagation latency, because it is a critical factor in real networks. In this paper, the placement problem has been formulated on the basis of propagation latency as an optimisation problem, and Simulated Annealing (SA) technique has been used to analyse the problem. Further, we investigate the behaviour of SA with four different neighbouring solutions technique. The effectiveness of the algorithms is carried out on TataNld topology and implemented using MATLAB simulator.

Software Defined Networks (SDNs) has brought a new form of network architecture that simplifies network management through innovations and programmability. But, the distributed control plane of SD-Wide Area Network is challenged by load imbalance problem due to the dynamic change of the traffic pattern. The packet_in messages are one of the major contributors of the control's load. When such packet rate exceeds a certain threshold limit, the response time for control request increases non-linearly. In order to achieve better end-user experience, most of the previous works considered the optimal switch to controller association with an objective to minimize the response time on LAN environment but ignores the consequence of large scale network. In this regard, the proposed work realizes the necessity of layer-2 and layer-3 controller in LAN and WAN environment separately. A load prediction based alertness approach has been introduced to reduce the burden of the controllers. This approach may create an additional delay for the initial packets of the flow entry that lead to more prediction error. However, the proposed method reduces the error by selecting an optimal timeout value of the flow. Further, minimization of the response time between router to the controller has been taken care of. An extensive simulation shows the efficacy of the proposed scheme.

In software-defined networks (SDNs), the deployment of multiple controllers improves the reliability and scalability of the distributed control plane. Recently, edge computing (EC) has become a backbone to networks where computational infrastructures and services are getting closer to the end user. The unique characteristics of SDN can serve as a key enabler to lower the complexity barriers involved in EC, and provide better quality-of-services (QoS) to users. As the demand for IoT keeps growing, gradually a huge number of smart devices will be connected to EC and generate tremendous IoT traffic. Due to a huge volume of control messages, the controller may not have sufficient capacity to respond to them. To handle such a scenario and to achieve better load balancing, dynamic switch migrating is one effective approach. However, a deliberate mechanism is required to accomplish such a task on the control plane, and the migration process results in high network delay. Taking it into consideration, this article has introduced an efficient switch migration-based load balancing (ESMLB) framework, which aims to assign switches to an underutilized controller effectively. Among many alternatives for selecting a target controller, a multicriteria decision-making method, i.e., the technique for order preference by similarity to an ideal solution (TOPSIS), has been used in our framework. This framework enables flexible decision-making processes for selecting controllers having different resource attributes. The emulation results indicate the efficacy of the ESMLB.

The distributed control plane is the alternate solution to reliability and scalability like potential issues in the software-defined wide area network (SDWAN), but the static mapping between controller and switches might cause an uneven load distribution among controllers. Migration of switches from one controller to another under-loaded controller is a solution to handle the peak traffic. However, the switch migration process is a complex process that may affect the end-user quality of service (QoS), as a result, the response time of the control messages are also affected. To address this issue, the authors present RTSM, a novel strategy, that optimises the response time of the control messages during switch migration. Further, an SDWAN architecture was proposed, in which the introduction of the layer-2 controller reduces the dependency on WAN communication of the forwarding devices. The Karush–Kuhn–Tucker conditions have been applied for target controller selection, which ensures improved response time. A switch selection method was also introduced, which minimally affects the end-user QoS during device migration in SDWAN scenario. To evaluate the performance of the RTSM, Mininet simulator with the Floodlight controller has been used, and the result shows that the proposed algorithm outperforms the other existing works during load balancing.

Network intrusion and cyber attacks are the most severe concern for Cloud computing service providers. The vulnerability of attacks is on a hike that manual or simple rule-based detection of cyber-attacks is not robust. In order to tackle cyber attacks in a reliable manner, an automated Intrusion Detection system equipped with a swarm intelligence (SI) based machine learning model (ML) is essential to deploy at entry points of the network. Nowadays, the application of SI with ML is used in various research areas. For an efficient IDS, choosing relevant features from the noisy data is an open question. In this regard, this paper proposes a method that utilizes the Whale Pearson hybrid feature selection wrapper for reducing the irrelevancy in the IDS model. Whale Pearson hybrid wrapper is an improved version of the binary Whale optimization Algorithm (WOA). The WOA is a type of SI algorithm which is inspired by the behavior of humpback whales. The proposed method has chosen 8 out of 42 features from the Hackereath Network attack prediction data-set, which are sufficient for building an efficient Intrusion detection model. The model trained with the eight features produces an accuracy of 80%, which is 8% greater than the accuracy produced by the original data-set with the KNN algorithm on ten-fold cross-validation.

The cloudlets can be deployed over mobile devices or even fixed state powerful servers that can provide services to its users in physical proximity. Executing workloads on cloudlets involves challenges centering on limited computing resources. Executing Virtual Machine (VM) based workloads for cloudlets does not scale due to the high computational demands of a VM. Another approach is to execute container-based workloads on cloudlets. However, container-based methods suffer from the cold-start problem, making it unfit for mobile edge computing scenarios. In this work, we introduce executing serverless functions on Web-assembly as workloads for both mobile and fixed state cloudlets. To execute the serverless workload on mobile cloudlets, we built a lightweight Web-assembly runtime. The orchestration of workloads and management of cloudlets or serverless runtime is done by introducing software-defined Cyber Foraging (SDCF) framework, which is a hybrid controller including a control plane for local networks and cloudlets. The SDCF framework integrates the management of cloudlets by utilizing the control plane traffic of the underlying network and thus avoids the extra overhead of cloudlet control plane traffic management. We evaluate SDCF using three use cases: (1) Price aware resource allocation (2) Energy aware resource scheduling for mobile cloudlets (3) Mobility pattern aware resource scheduling in mobile cloudlets. Through the virtualization of cloudlet resources, SDCF preserves minimal maintenance property by providing a centralized approach for configuring and management of cloudlets.

A new era of smart applications like smart home, smart transportation, the smart building begins with the development of the Internet of Things (IoT) technology. These applications collectively form an IoT enabled smart city. The presence of the centralized cloud in IoT enabled smart city causes a high end to end delay and enormous transmission pressure on the communication network. Edge computing is an extension of cloud computing infrastructure that pushes the computing storage and network resource similar to the cloud near the data source, i.e., IoT device. The major concern in the edge computing environment to execute tasks generated by IoT devices is optimal energy consumption while satisfying the delay constraint. In this paper, a multi-objective optimization problem in IoT enabled smart city is formulated as an energy and delay minimization problem. At first, a three-layer network architecture for IoT enabled smart cities is presented. After that, we design an auction-based edge resource allocation scheme considering the proposed network architecture, to provide low computing delay with energy-efficient service for delay-sensitive tasks. The simulation results indicate that the proposed approach performs better compared to some existing approaches.

In the current era of technology, the number of users connecting to the internet has increased rapidly. If the situation is like Pandemic (COVID-19), the demand of the data services is much higher than the expectation. The major problem of this situation is load management. SDN plays an important role in this regard. Being a programmable network, it is widely admirable for its network intelligence. In this paper, we discuss SDN architecture, its components, various applications and major challenges. The unique architecture differentiates SDN from traditional network. It makes it possible to take routing decision without depending upon the underlying architecture. We also present a systematic review on the study of the different load balancing approaches, existing solutions and various load balancing techniques in SDN. With the network intelligence, SDN is able to take the load balancing decision. We categorize the load balancing techniques based on controller, server, communication path selection and cloud-based environment. Finally, we present different research challenges, existing solutions under each technique.

Software defined networks (SDN) and OpenFlow (OF) technologies have brought a new form of network architecture that simplifies the network management through innovations and programmability. The controller of SDN architecture has an abstract view of the entire network that smoothly handles the underlying hardware devices. In SDN, the control messages (e.g. packetfiin) arriving at the controller have the highest contribution of the controller's load. Upon exceeding a certain threshold of these messages, the response time of the controller increases due to the over memory and CPU utilisation. In this respect, the balancing of control messages is essential. To balance this control messages, migration of OF switches from an overloaded controller to an underloaded controller is a viable solution. However, such migration process has to perform with a well-designed mechanism, failed to this the process might increase the response time of the control events. Taking this into account, the authors introduce controller adaption and migration decision (CAMD) framework, a switch migration based load balancing strategy, which effectively selects both switch and target controller such that it affects the response time minimally. This efficient switch migration technique ensures better load balancing with reduced response time and improved end-user quality of services.

The newly emerged software-defined networking (SDN) paradigm provides a flexible network management by decoupling the network control logic from the data plane, which could effectively resolve many security issues of legacy networks. One of such security issues is distributed denial of service (DDoS) attack, which is a rapidly growing network threat. This is usually performed on a target system to make an online service unavailable to the users. SDN can easily detect the DDoS attack due to the centralized control provisioning and network visibility. At the same time, the changes of fundamental architecture and the developments of various design entities pose a severe DDoS threat to the SDN platform. This paper presents a concise up-to-date review of security concerns of SDN, possible DDoS attack in individual layers of SDN and ongoing research efforts on SDN-enabled DDoS detection solutions. Based on the findings, an information distance-based flow discriminator framework has been discussed, which can discriminate the DDoS traffic during flash events, a similar looking legitimate traffic, in SDN environment. The information distance metric is used to describe the variations of traffic behavior of such events. The simulation results show that the information distance metric can effectively identify the DDoS traffic in comparison with other metrics with a higher detection rate. The proposed solution can detect the traffic at the edge switch so that the attack alert can be raised at the earliest.

Software Defined Networks (SDN) is a popular modern network technology that decouples the control logic from the underlying hardware devices. The control logic has implemented as a software entity that resides in a server called controller. In a Software-Defined Wide Area Network (SDWAN) with n nodes; deploying k number of controllers (k < n) is one of the challenging issue. Due to some internal or external factors, when the primary path between switch to controller fails, it severely interrupt the networks’ availability. In this regard, the proposed approach provides a seamless backup mechanism against single link failure with minimum communication delay based on the survivability model. In order to obtain an efficient solution, we have considered controller placement problem (CPP) as a multi-objective combinatorial optimization problem and solve it using two population-based meta-heuristic techniques such as: Particle Swarm Optimization (PSO) and FireFly Algorithm (FFA). For CPP, three metrics have been considered: (a) controller to switch latency, (b) inter-controller latency and (c) multi-path connectivity between the switch and controller. The performance of the algorithms is evaluated on a set of publicly available network topologies in order to obtain the optimum number of controllers, and controller positions. Then we present Average Delay Rise (ADR) metric to measure the increased delay due to the failure of the primary path. By comparing the performance of our scheme to competing scheme, it was found that our proposed scheme effectively improves the survivability of the control path and the performance of the network as well.

Software Defined Networking (SDN) is a new approach of managing and programming networks enabled by OpenFlow. For load balancing, a migration among OpenFlowDevice (OFDevice) is needed from heavy-loaded controller to least-loaded controller. During migration of OFDevice, the response time, jitter and packet loss for the end user are high. To address this problem, we propose a method in which the response time, jitter and packet loss are minimized during device migration. In this approach for migrating the optimal OFSwitch, we use-liveness, safety, and serializability. The proposed approach focuses on selecting such a OFDevice which causes minimum load on the controller. The experimental results show that our proposed method improves response time, jitter and packet loss.

The OpenFlow based Software Defined networks (SDN) is a new network architecture has gained much popularity in these days. Although the centralized control of SDN provides an enormous benefit, there are still a lot of security challenges are in control plane. As Distributed Denial of Services (DDoS) attack is one of the main security threat to the Internet, the goal of this paper is to detect the attack at the control layer by using the flow table information of the OpenFlow switches. The controller is the separate entity of SDN if it is made unreachable by a DDoS attack the entire architecture become defunct. In the current high-speed network scenario, discriminating a high-rate DDoS traffic from the flash events(FE) is a relatively more challenging task. The characteristics of the high-rate DDoS traffic are nearly similar to the legitimate FE traffic. Hence, in this work for detection purpose, we have used information theory based metrics such as General Entropy(GE) and Generalized Information Distance (GID). We evaluate the effectiveness of these metrics with Shannon entropy and Kullberg-Leibler divergence. The extensive simulation result shows that the considered metrics outperforms the other metrics with reduced false positives.

Distribution of video content over the Internet has drastically transformed the consumption of media. Content providers, naturally, would like to ensure that their videos play on users' devices whenever requested, without failure or interruptions. Due to the varying nature of user needs, procurement of computing resources proves to be tricky, leading to the popularity of cloud-based approach. Media cloud is a computing paradigm dedicated for multimedia services and delivers on demand services (e.g., video) by dynamically acquiring cloud resources. Use of cloud resources helps service providers to lessen their operational cost, reduce delay and abandonment rate to deliver adaptive video stream. The abandonment rate, delay, user engagement and repeat viewership plays a vital role in service providers revenues. In this paper, we use an analytical model based on queuing theory to find the effect of queue size (buffer size) on abandonment, blocking and successful services. Further, the relationship between the number of virtual machines, waiting time (delay) and abandonment rate is also examined. We also derive a relationship between the number of user requests in the system and the virtual machines required to respond to the same.

One of the unexplored research areas in Software Defined Networks (SDN) is load balancing of control messages (e.g. packet_in) among distributed controllers in Wide Area Networks. In SDN, on every unsuccessful match in the flow table for the incoming traffic flows, the switch sends packet_in to the controller for further action against the traffic flow. The packet_in messages are one of the major contributors of the control request (load) received by the controller. When it exceeds a certain threshold limit, the response time for the control request increases nonlinearly due to the over CPU utilization and congestion. When the controller gets overloaded, typically the OpenFlow-enabled Devices (OFDevices) are migrated from the current controller to another under loaded controller domain. This migration might cause large degradation of end users' QoS metrics. To resolve this issue, we introduce basic demand and supply curve based DSSDN, a new load balancing method that utilizes the load factors of Software Defined Wide Area Networks controllers. This method selects the OFDevice which causes maximum load on the controller and traversing minimum users traffic through it. The Karush-Kuhn-Tucker conditions are employed during the optimal controller selection by the OFDevices to improve the response time effectively. During implementation, virtual threads running on the controller representing the OFDevices are used to take the optimal decision instead of actual OFDevices. The experimental results show that during migration, the DSSDN stabilizes the load hikes, improves QoS, and increase the end users' utility without much disruptions in the network state.

The Software Defined Networks (SDN) and OpenFlow technologies become the emerging networking technology that supports the dynamic nature of the network functions through simplified network management. The main innovation behind SDN is the decoupling of forwarding plane and control plane. In control plane, the controller provides a pivotal point of control to distribute the policy information throughout the network through a standard protocol like OpenFlow. Despite numerous benefits, SDN security is still a matter of concern among the research communities. The Distributed Denial-of-Service (DDoS) attack have been posing a tremendous threat to the Internet since a long back. The variant of this attack is quickly becoming more and more complex. With the advancement in network technologies, on the one hand SDN become an important tool to defeat DDoS attacks, but on another hand, it becomes a victim of DDoS attacks due to the potential vulnerabilities exist across various SDN layer. Moreover, this article focuses on the DDoS threat to control plane which is the central point of SDN. The entropy-based DDoS detection method is a wildly used technique in the traditional network. For detection of DDoS attack in control layer of SDN, few works have employed entropy method. In this paper, taking the advantages of flow based nature of SDN, we proposed General Entropy (GE) based DDoS attack detection mechanism. The experimental results show that our detection mechanism can detect the attack quickly and achieve a high detection accuracy with a low false positive rate.

The cloud service providers make a considerable investment in setting up the data centers backbone network with the aim to maximize the network resource. However, the actual utilization of the network resources is hard to predict. With the invent of Software Defined Networking (SDN) and OpenFlow protocol, the network control layer has got the capability to communicate with the applications or services which are offered by the service provider. Moreover, a Software Defined Data center suggests resource virtualization at computing, storage, and network layer. The multi-tenancy is a well-accepted architecture in cloud computing where a single instance of a software application serves multiple customers. This work is a first of its kind, which aims at maximizing the network resources with respect to multi-tenancy at the network layer. In this work, with network multitenancy, different customers IoT traffic flows are prioritized, and then network resources are allocated to the traffic flows dynamically based on the priority. We considered a scenario of Enterprise Resource Planning (ERP) solutions deployed in the cloud which offers services in the form of Software as a Service to the customers. The IoT devices deployed at the manufacturing site makes transactions on the cloud ERP. This work focuses on prioritizing the ERP- IoT traffic to meets the demands of a multi-tenant data center network. The ERP-IoT flows are prioritized using a regression based machine learning technique for predicting the response time for execution of a query caused by a traffic flow in the ERP backend server. Later, the ERP-IoT flows are assigned to multiple queues created on each network device in data center. This assignment is performed based on the traffic flow priority and Demand Supply scores, which aims at maximizing network resource utilization. During performance evaluation, we observed that the proposed work with network multi-tenancy shows more than 10% increase in service providers utility with respect to standard data center single queue operations.

Mobile Edge computing (MEC) and caching are new forms of computing architecture which brings network functions to the network edge or physical proximity of the users. In MEC, the mobile cloudlets are smart phones which offer computational services to other smart phones available in the physical proximity. Selection of the device for offloading computationally intensive tasks is a very important criterion for end-users Quality of Service. This work focuses on optimal selection of devices which receives offloaded computational tasks. This paper presents a non-cooperative extensive game model where players maximize their pay-offs which leads to minimization of response time. Further, we make our proposed model to implicitly depend upon the battery life of the computational task receivers. The game model achieves Nash Equilibrium by using backward induction technique. This work also takes care of the device availability by clustering the previous availability data. Finally, we evaluate the performance of the proposed model upon response time, end-users utility and memory utilizations. We also see that the proposed work out performs the different schemes which we used for comparisons.

The primary innovations behind Software Defined Networks (SDN) are the decoupling of the control plane from the data plane and centralizing the network management through a specialized application running on the controller. Despite all its capabilities, the introduction of various architectural entities of SDN poses many security threats and potential target. Especially, Distributed Denial of Services (DDoS) is a rapidly growing attack that poses a tremendous threat to both control plane and forwarding plane of SDN. As the control layer is vulnerable to DDoS attack, the goal of this paper is to provide a defense system which is based on Learning Automata (LA) concepts. It is a self-operating mechanism that responds to a sequence of actions in a certain way to achieve a specific goal. The simulation results show that this scheme effectively reduces the TCP connection setup delay due to DDoS attack.

The primary innovations behind Software Defined Networks (SDN) are the decoupling of the control plane from the data plane and centralizing the network management through a specialized application running on the controller. In spite of many advantages, SDN based data centers’ security issues is still a matter of concern among the research communities. Although SDN becomes a valuable tool to defeat attackers, at the same time SDN itself becomes a victim of Distributed Denial-of-Service (DDoS) attacks due to the potential vulnerabilities exist across various SDN layer. The logically centralized controller is always an attractive target for DDoS attack. Hence, it is important to have a fast as well as accurate detection model to detect the control layer attack traffic at an early stage. We have employed information distance (ID) as a metric to detect the attack traffic at the controller. The ID metric can quantify the deviations of network traffic with different probability distributions. In this paper, taking the advantages of flow based nature of SDN, we proposed a Generalized Entropy (GE) based metric to detect the low rate DDoS attack to the control layer. The experimental results show that our detection mechanism improves the detection accuracy as compared to Shannon entropy and other statistical information distance metrics.

Software Defined Networks (SDN) paradigm was introduced to overcome the limitations of the traditional network such as vendor dependencies, inconsistency policies, etc. It becomes a promising network architecture that provides the operators more control over the network infrastructure. The controller also called the operating system of the SDN has the centralized control over the network. Despite all its capabilities, the introduction of various architectural entities poses many security threats to SDN layers. Among many such security issues, Distributed Denial of Services (DDoS) is a rapidly growing attack that poses a tremendous threat to SDN. It targets to the availability of the network, by flooding the controller with spoofed packets. It causes the controller to become paralyzed, and thereby the entire network becomes destabilize. Therefore, it is essential to design a robust DDoS detection mechanism to prevent the control plane attack. In this regard, we have used seven Machine Learning techniques to accurately classify and predict different DDoS attacks like Smurf, UDP flood, and HTTP flood. Experimental results with proper analysis have been presented in this work.

Green cloud computing is the latest research trend where various approaches are introduced to minimize the energy consumptions and carbon footprint produced by computers. Further, the pay-per-use pricing model used in the cloud system helps to achieve economy of scale. Noticeably, many real-time applications that demand both temporal and functional correctness are moving to the cloud. It becomes a challenging task for a cloud service provider to ensure real-time response while minimizing computation energy and execution cost. In this regard, task scheduling plays a key role in achieving a performance improvement of the system with several constraints. In this paper, we proposed a multi-constraint scheduling algorithm, namely MCSA for the real-time task in the virtualized cloud environment. First, we assign a score value to a VM based on computation energy and execution cost. Then, MCSA use this scoring value to choose the appropriate VM for a task. We analyzed the proposed MCSA algorithm through extensive simulations and experiments. We consider Guarantee Ratio, Average Execution Cost, and Average Energy Consumption under various scenarios to show the effectiveness of MCSA over some existing schemes.

Services are part of our daily activities, such as the emergence of new solutions powered by cutting-edge technologies such as Fog Computing. Fog computing, new ideas for the cloud in the network, is considered the suitable platforms for the Internet of Things services and many applications. This fog - cloud computing integration is not directly moving forward. Some services required ultra-low latency like e-Health, public safety. Unnecessary communication loads not only the core network but also the data center that it finds. To do this, fog nodes can pre-process the data before sending it to the cloud. Smart Gateways are suitable candidates for service deployment. This paper presents a design of a service-oriented architecture using smart gateways as fog nodes which reduce service latency.

Software Defined Networks (SDN) is a popular paradigm in the modern networking systems that decouples the control logic from the underlying hardware devices. The control logic has implemented as a software component and residing in a server called controller. To increase the performance, deploying multiple controllers in a large-scale network is one of the key challenges of SDN. To solve this, authors have considered controller placement problem (CPP) as a multi-objective combinatorial optimization problem and used different heuristics. Such heuristics can be executed within a specific time-frame for small and medium sized topology, but out of scope for large scale instances like Wide Area Network (WAN). In order to obtain better results, we propose Particle Swarm Optimization (PSO) and Firefly two population-based meta-heuristic algorithms for optimal placement of the controllers, which take a particular set of objective functions and return the best possible position out of them. The problem has been defined, taking into consideration both controllers to switch and inter-controller latency as the objective functions. The performance of the algorithms evaluated on a set of publicly available network topologies in terms execution time. The results show that the FireFly algorithm performs better than PSO and random approach under various conditions.

Software Defined Network has become one of the most important technology to manage the large scale networks. The separation of the control plane from the data plane in networking devices is the main idea of SDN. Currently, Open Flow is the popular SDN standard, which has a set of functionalities. In the emerging cloud scenario smart devices plays an important role. But they are facing latency and intermittent connectivity. For this fog devices are placing in-between cloud and smart devices. Fog computing is currently applying on connected vehicles, sensor network etc. This article looks into the vehicular network area as a case study where SDN architecture can apply on fog devices for enhancement of the performance and betterment of traffic management and QoS on distribution of real time data.

The deployment of cyber foraging systems includes deployment of Cloudlets as computational agents, which receive heavy computational demands during peak hours based on the context of service being offered. We introduce an intelligent pricing policy mechanism for cloudlets-services for mobile cloud users. The dynamic distributed pricing policy CPS (cyber-foraging pricing scheme) also introduces methods which can optimize the computational service requests fired by the mobile cloud users. In CPS, each fixed cloudlet optimizes its revenue using the supply-demand curve and sets a dynamic cost for the home and foreign users. CPS considers two types of mobile-cloud users i.e., foreign users and home users and computational requests are fired from both home and foreign users. The proposed work uses working day movement mobility model for the mobile cloud users. This work uses response time by a cloudlet to evaluate its real time home and foreign prices. We also propose a hostile environment pricing scheme for the service providers. We implemented the proposed pricing scheme on NS-3 and collected the results. The results also indicate high increase in the utility of the corresponding mobile-cloud user.

The Software Defined Network (SDN), is the next generation Internet technology, which not only solve the ossification of the Internet, but also creates new innovations and simplify the network management. The key idea behind SDN is separation of control plane from data plane, as a result devices in the data plane becomes the forwarding device and transfer all the decision making activities in a centralized system called a controller. In SDN architecture, it is a great challenge to find a solution to the controller placement problem (CPP). The CPP decides where to place the K number of controllers with a limited amount of resources within the SDN network. Among many proposed solutions to this problem, we apply clustering algorithms such as K-median, K-center on the topology which are obtained from a topology-zoo. A spectral clustering technique has been proposed for partitioning the network and place the controller within the sub-domain. Latency is one of the performance matrices that we have considered in the simulation. The simulation results show that the graph partition method reduces the inter-controller latency within the network.

The newly emerged Software Defined Networks (SDN) can apply to Wide Area Network (WAN) for network control and management. To achieve this, a logically centralized but a physically distributed control architecture is normally required. This type of control architecture consists of multiple controllers. For maintaining the global consistency of the network, controller placement is a key issue in a Wide Area SDN. The controller placement problem (CPP) refers to selecting the number and proper positions of the controllers to improve the performance of the SDN control plane. Although various approaches are made to solve CPP for the small and medium-size network, still it requires an alternative solution for a large scale network like WAN. It needs enough time to select the number of controllers and their location, within a limited resource constraint. In this work, we propose two stochastic meta-heuristic techniques for finding optimal locations of the controllers that optimize the latency between the switch to a designated controller. We develop the algorithm for Particle Swarm Optimization and Firefly that solve CPP, with the fitness function as the controller to switch latency, which needs to be minimized. The extensive simulation shows that Firefly provides a better result in terms of computation time and cost that are close to optimal.

The service (task) allocation problem in the distributed computing is one form of multidimensional knapsack problem which is one of the best examples of the combinatorial optimization problem. Nature-inspired techniques represent powerful mechanisms for addressing a large number of combinatorial optimization problems. Computation of getting an optimal solution for various industrial and scientific problems is usually intractable. The service request allocation problem in distributed computing belongs to a particular group of problems, i.e., NP-hard problem. The major portion of this chapter constitutes a survey of various mechanisms for service allocation problem with the availability of different cloud computing architecture. Here, there is a brief discussion towards the implementation issues of various metaheuristic techniques like Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), BAT algorithm, etc. with various environments for the service allocation problem in the cloud.

These days the usage of network is growing at a faster pace, at the same time a lot of challenges is facing by the net- work administrator, to tackle the frequent network access by the users. The network infrastructure is growing rapidly to meet the business need, but it requires re-policing and reconfiguration of the network. But managing the under- lying infrastructure becomes more complicated to handle the unprecedented network demand. The Software Defined Network (SDN), is the next generation Internet technology, which not only solves the ossification of the Internet, but also creates innovations and simplifies the network management. The key idea behind SDN is separation of control plane from the data plane, as a result, devices in the data plane simple becomes the forwarding device and transfer all the decision- making activities in a centralized system called a controller. Among many, OpenFlow is the standard and most popular SDN protocol that interacts between controller and forward- ing devices. In this article, we will give an overview of the basic architecture of SDN and OpenFlow, SDN-controller interaction and benefits of SDN.

In the last couple of years Software Defined Network (SDN) have come into existence which empowers network operators with more flexibility to manage and program their network. This type of network solves the limitation of legacy networks. Data plane and control planes are separated from each other as a result data plane devices simple act as a packet forwarding device and leaving the decision making part to a centralized system called controller. Though it has a lot of advantages, still security of SDN is an open issue. In modern day living wireless sensor network (WSN) technologies come across all most all areas which creates another research dimension called IoT where sensors and actuators blend in one piece. Application of SDN architecture in the IoT environment is a higher challenge. In this article we will present the security challenges in SDN and a secured architecture for IoT in an SDN based network.

Software Defined Network is one of the most recent Internet technology that manages the large scale network. Both from implementation and performance point of view SDN will improve the next generation networking services. It is important to find a solution to the controller placement problem is remaining a key issue in SDN based architecture. It decides where to place the controllers with a limited amount of resources within the network. This paper illustrates a preliminary work on the controller placement in SDN environment using an existing heuristic technique. More formally, a network is given by a set of elements (either switches or routers) they must be managed by the controller(s), the algorithm finds the number controller(s) require to cover all the network elements within the network in a optimal way. The primary criteria is the distance between all nodes and selected controllers is minimized. Controller's capacity is a constraint of the controller, that restricts a controller to manage an unlimited number of data plane devices. We have proposed and implemented simulated annealing algorithm with greedy heuristic to solve the controller placement problem.

Cloud computing is a novel paradigm which relies on the vision of resource sharing over the Internet. The concept of resource virtualization, i.e. hiding the detail specification of the resources from the end users is the key idea of cloud computing. But the tenants have limited visibility over the network resources. The Network-as-a-Service (NaaS) framework integrates the cloud computing services with direct tenant access to the network infrastructure. The Network virtualization (NV) is such a platform that acts as a mediation layer to provide NaaS to tenants. NV supports the coexistence of multiple virtual networks, which is the collection of virtual nodes and virtual links on the same underlying physical infrastructure. Prior to set up a virtual network in an NV Environment, resource discovery and resource allocation are the primary job. In this chapter, we have discussed on basic NV architecture, surveyed the previous work on the resource allocation along with ongoing research projects on network virtualization.

Hadoop is a very efficient distributed processing framework. It's based on map-reduce approach where the application is divided into small fragments of work, each of which may be executed on any node in the cluster. Hadoop is very efficient tool in storing and processing unstructured, semi-structured and structured data. Unstructured data usually refers to the data stored in files not in traditional row and column way. Examples of unstructured data is e-mail messages, videos, audio files, photos, web-pages, and many other kinds of business documents. Our work primarily focuses on detecting malware for unstructured data stored in Hadoop distributed file system environment. Here we use calm AV's updated free virus signature database. We also propose a fast string search algorithm based on map-reduce approach.