The widespread adoption of IoT networks in various domains has brought about a transformative impact on our interactions with technology and the surrounding environment. Yet, as the complexity and scale of IoT deployments continue to increase, the need for ensuring network security has become a top priority. Consequently, trust-based access control has emerged as a promising security approach for IoT networks. By evaluating the trustworthiness of devices based on factors such as device behavior, historical data, reputation, and contextual information, trust-based access control offers numerous advantages. The primary objective of this project is to develop a trust-based access control mechanism specifically designed for IoT networks. By integrating traffic analysis, fuzzy clustering, and a fuzzy trust model, the project aims to assess the trust values of devices within the network. These trust values will subsequently be employed to establish access control policies, thereby enhancing the security and efficiency of IoT systems. The utilization of this trust-based approach holds great potential in enhancing reliability and secured interactions among IoT network devices while mitigating the risks associated with unauthorized access.

The overfitting of deep learning models trained using moderately imbalanced datasets is the main factor in increasing the success rate of membership inference attacks. While many oversampling methods have been designed to minimize the data imbalance, only a few defend the deep neural network models against membership inference attacks. We introduce the privacy preserving synthetic minority oversampling technique (PP-SMOTE), that applies privacy preservation mechanisms during data preprocessing rather than the model training phase. The PP-SMOTE oversampling method adds Laplace noise to generate the synthetic data points of minority classes by considering the L1 sensitivity of the dataset. The PP-SMOTE oversampling method demonstrates lower vulnerability to membership inference attacks than the DNN model trained on datasets oversampled by GAN and SVMSMOTE. The PP-SMOTE oversampling method helps retain more model accuracy and lower membership inference attack accuracy compared to the differential privacy mechanisms such as DP-SGD, and DP-GAN. Experimental results showcase that PP-SMOTE effectively mitigates membership inference attack accuracy to approximately below 0.60 while preserving high model accuracy in terms of AUC score approximately above 0.90. Additionally, the broader confidence score distribution achieved by the PP-SMOTE significantly enhances both model accuracy and mitigation of membership inference attacks (MIA). This is confirmed by the loss-epoch curve which shows stable convergence and minimal overfitting during training. Also, the higher variance in confidence scores complicates efforts of attackers to distinguish training data thereby reducing the risk of MIA

Poppet valves used in internal combustion engines have a high risk of failure due to significant temperature and pressure. These poppet valves need surface finishing at the nano-scale level to prolong their life during their working use. In the present research, the chosen poppet valve has narrow ridge profiles, which is difficult to nano-finish by conventional processes due to certain limitations. The magnetorheological fluid-based finishing method can be effectively used for this kind of complicated narrow profile. For the magnetorheological fluid-based finishing processing of the poppet valve, a novel magnet fixture and setup is used. For checking the efficiency of this setup, surface characterization and surface roughness for polished and unpolished surfaces are outlined using a field-emission scanning electron microscope, microscope and optical profilometer. The final surface roughness of S = 23.1?nm at poppet profiles were obtained. All manufacturing defects like burrs, dents, scratches and pits are almost removed. The study of finishing forces in the magnetorheological fluid-based finishing method is also carried out using magnetostatic fluid–solid interaction, experimental and theoretical analysis. This force analysis supports the development of the material dislodgement model to anticipate material removal rate while finishing. The gap (error = 12.87%) between the experimental and theoretical material removal rate is marginal. It has high accuracy and reliability for specific applications.

Three-dimensional (3D) bioprinting technology enables the fabrication of porous structures with complicated and variable geometries, allowing for the equitable distribution of cells and the regulated release of signalling components, which distinguishes it from traditional tissue scaffolding approaches. In 3D bioprinting, various cell-laden materials, including organic and synthetic polymers, have been used to create scaffolding systems and extracellular matrix (ECM) for tissue engineering (TE). However, significant technological hurdles remain, including bio-ink composition, printability, customizing mechanical and biological characteristics in hydrogel implants, and cell behaviour guiding in biomaterials. This chapter investigates several methodologies for hydrogel-based bio-inks that can mimic the ECM environment of real bone tissue. The study also looks at the process factors of bio-ink formulations and printing, as well as the structural requirements and production methods of long-lasting hydrogel scaffolds. Finally, contemporary bioprinting techniques are discussed, and the chapter concludes with an overview of the existing obstacles and probable future prospects for smart hydrogel-based bio-inks/scaffolds in tissue regeneration.

The increasing number and diversity of connected devices in IoT applications make them dynamic and unpredictable. The presence of new devices and the removal of existing ones may lead to variations in device availability and characteristics. Due to the heterogenity of resources, requirements of users become more dynamic and the provisioning of resources also becomes challenging. Especially in microservice-based IoT applications, systems are highly distributed and heterogeneous, consisting of a wide variety of devices and services with differing capabilities and requirements. Static resource allocation approaches, which allocate resources based on predefined rules or fixed configurations, may not able to adapt to these dynamic changes. Conventional static resource allocation approaches are inadequate for large-scale IoT systems due to lack context awareness. This paper presents an approach that integrates context-awareness for dynamic resource provisioning using reinforcement learning in microservice-based IoT systems. The system optimize resource allocation strategies by considering contextual factors such as device properties, functionalities, environmental conditions, and user requirements. Integrating reinforcement learning allows the framework to constantly learn and adjust its resource provisioning methods, resulting in better performance and resource reuse. The experimental analysis demonstrates the effectiveness of the framework in optimizing resource utilization, improving system efficiency, and enhancing overall performance. The study highlights the potential of machine learning mechanisms to further optimize resource utilization and emphasizes the importance of future research to analyze the scalability, robustness, and overall performance of context-aware resource provisioning. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

The integration of social dynamics into the Internet of Things (IoT) networks, termed Social IoT (SIoT), presents a challenging task with regards to trust management due to the dynamic and socially influenced nature of the SIoT networks. Classical trust models struggle to adapt to the complex SIoT environments, leaving the possibility of malicious attacks. This paper proposes a framework for the SIoT ecosystem, taking advantage of blockchain technology and Neural Networks to enhance trustworthiness assessment to mitigate risks. The proposed framework leverages blockchain for secure data storage and transaction transparency to ensure the integrity of the information. Neural network algorithms like Recurrent Neural Networks (RNN) and Bidirectional Encoder Representations from Transformers (DistilBERT) are used to assess trust in real-time, taking into account evolving social interactions, leveraging the advantage provided by transfer learning. The simulation-based experiments are conducted to evaluate the efficiency of the proposed framework for detecting and mitigating malicious attacks in SIoT environments. Results demonstrate the robustness of the solution.

Many IoT applications are now using microservices design concepts and have developed as an emergent technology by leveraging containerization, modularity, autonomous deployment and loose coupling. The requirement of different software design patterns is essential to aid in the creation of scalable, interoperable and reusable solutions. In IoT systems and software development, several IoT patterns, such as IoT design patterns and IoT architectural patterns, have been studied. But, most of the studied design patterns are domain-specific, and they do not consider the impact of data quality in the design process. Also, in IoT environment data quality plays an important role while processing the data to produce accurate and timely decisions. Therefore, this paper presents a formal approach to incorporate the data quality dimensions in design patterns for the microservice based IoT applications. Here, data quality evaluation parameters are integrated with various microservice design patterns suitable to IoT applications such as event sourcing pattern, chained microservice pattern, API gateway pattern etc. to ensure the effective data communication and high-quality services provided by the IoT applications. Further, the proposed quality driven design patterns are systematically defined using Event-B language and validated through Rodin platform.

Internet of Things (IoT) intended to connect various physical devices in multiple domains to offer high quality ondemand services. In this scope, identification of intended devices is remains a challenge because of heterogeneity and wide distribution. Context plays a significant role to enable provision of adequate services to the users based on their preferences. In addition, the context can help to adapt with the dynamic environment changes. Therefore, the aim of this paper is to address how the context can be discovered from IoT data, and its influence in recommending the IoT devices. For this, a weighted clustering mechanism is applied aiming to discover the informative contexts and recommended the devices to the user based on the context similarity. The proposed model is extensible, independent of domain and taking into account the constraints of the IoT like availability, applicability, etc. Further, this model is validated through a cross validation mechanism which shows accurate prediction of probable contexts.

The Internet of Things (IoT) revolutionizes the technology landscape by enabling a wide spectrum of services and applications, characterized by a large number of devices, communication protocols, and data formats. Seamless integration among various IoT-enabled technologies is the most challenging task as the technical standards are disjoint. This often results in monolithic structures with very poor scalability. Further, data heterogeneity in IoT networks increases the measure of multidimensionality, which poses a critical challenge of sharing data with other business applications. Therefore, IoT-based solution requires an architectural framework supported by a large number of independent and specialized microservices towards providing sufficient scalability and interoperability. In this manuscript, a layered architectural framework is proposed where a novel device template concept is introduced to enable flexible and interoperable integration of microservices with IoT devices. The proposed model is formally validated using Event-B in the Rodin platform with the help of proof obligations. Again, a service aggregation algorithm is proposed to reduce the latency and increase the reuse of microservices. Further, the proposed architectural framework is implemented and compared with other similar frameworks. The experimental result shows that the proposed architectural framework enables enhanced interoperability, scalability, and reusability of IoT devices and microservices.

IoT devices are typically authenticated directly by gateways present in the network. However, in large and complex IoT systems like the smart city or smart industry which consist of thousands of connected devices, it may not be always feasible to be directly connected to the gateway while it may be possible to be connected to another device. Therefore, already authenticated devices should facilitate the new device to get authenticated by the gateway. To address this issue, the existing protocols use multiple authentication protocols based on different cryptography techniques, which are difficult to implement and manage in resource constrained IoT devices. In this paper, we propose a Transitive device authentication protocol based on the Chebyshev polynomial. The transitive authentication protocol utilizes the session key established in the mutual authentication between the intermediate device and gateway. Both the mutual authentication and transitive authentication protocols are relying on the same preregistration and authentication mechanism. To ensure the security of the proposed authentication protocol, detailed security analysis is carried out, and the secure session key establishment is verified using the BAN logic. Moreover, the proposed protocol is tested against crucial attacks in the Scyther tool. These formal analyses and Scyther attack simulation show that the proposed protocol is capable of withstanding critical attacks. Finally, to verify the efficiency, the protocol implementation is experimentally compared with similar approaches studied in the literature. The results show that the proposed protocol offers better performance, providing significantly lower response time, handshake duration, memory utilization, and energy consumption.

The Internet of Things (IoT) enables sophisticated smart technologies by analyzing various sensor data. Complexity of IoT devices is increasing rapidly as it getting intertwined in our daily lives with the usage of smart sensors, actuators, and other smart devices. This interconnected of smart devices often produces very complex datasets which enable multimodal services. Multimodality enables applications to combine and analyze the data of multiple literacies within one medium. Enabling an effective multimodal IoT network demands efficient data representation of various sensing and actuating devices. This work is focused on profiling the smart devices, i.e., resource description. It provides the device description, categorization of its properties, capabilities, and functionalities so that a suitable resource can be discovered effectively. A formal model of IoT has been presented to describe the resources with the support of multimodality. The model is described through the Event-B language, and the Rodin platform is used to find the correctness of the model.

The healthcare sector has engaged in substantial research in terms of technological advancement to provide patients with efficient and secure services. With the adaption of the internet of things (IoT) in the healthcare domain, remote patients are now able to share their health records with medical experts at distant locations, leading to more efficient and less expensive services. Given that data sharing over the internet entails the patients' privacy, therefore, it is necessary to ensure that data is transmitted securely so that an adversary cannot tamper with it. Again, as IoT devices are resource-constrained, therefore, it is very important to transmit the data in a lighter format. This paper presents a mechanism for the communication of data or images over the network in a secure and lighter format. The proposed mechanism is implemented on Modified Chebyshev Polynomial and suitable data and image compression techniques with minimum distortion.

We introduce a version of the Minority Game where the total number of available choices is D>2, but the agents only have two available choices to switch. For all agents at an instant in any given choice, therefore, the other choice is distributed between the remaining D?1 options. This brings in the added complexity in reaching a state with the maximum resource utilization, in the sense that the game is essentially a set of MG that are coupled and played in parallel. We show that a stochastic strategy, used in the MG, works well here too. We discuss the limits in which the model reduces to other known models. Finally, we study an application of the model in the context of population movement between various states within a country during an ongoing epidemic. we show that the total infected population in the country could be as low as that achieved with a complete stoppage of inter-region movements for a prolonged period, provided that the agents instead follow the above mentioned stochastic strategy for their movement decisions between their two choices. The objective for an agent is to stay in the lower infected state between their two choices. We further show that it is the agents moving once between any two states, following the stochastic strategy, who are less likely to be infected than those not having (or not opting for) such a movement choice, when the risk of getting infected during the travel is not considered. This shows the incentive for the moving agents to follow the stochastic strategy.

The impact of the ongoing COVID-19 pandemic is being felt in all spheres of our lives – cutting across the boundaries of nation, wealth, religions or race. From the time of the first detection of infection among the public, the virus spread though almost all the countries in the world in a short period of time. With humans as the carrier of the virus, the spreading process necessarily depends on the their mobility after being infected. Not only in the primary spreading process, but also in the subsequent spreading of the mutant variants, human mobility plays a central role in the dynamics. Therefore, on one hand travel restrictions of varying degree were imposed and are still being imposed, by various countries both nationally and internationally. On the other hand, these restrictions have severe fall outs in businesses and livelihood in general. Therefore, it is an optimization process, exercised on a global scale, with multiple changing variables. Here we review the techniques and their effects on optimization or proposed optimizations of human mobility in different scales, carried out by data driven, machine learning and model approaches.

Cross-program propagation of tainted data (such as sensitive information or user input) in an interactive IoT system is listed among the OWASP IoT top 10 most critical security risks. When programs run on distinct devices, as it occurs in IoT systems, they communicate through different channels in order to implement some functionality. Hence, in order to prove the overall system secure, an analysis must consider how these components interact. Standard taint analyses detect if a value coming from a source (such as methods that retrieve user input or sensitive data) flows into a sink (typically, methods that execute SQL queries or send data into the Internet), unsanitized (that is, not properly escaped). This work devises a cross-program taint analysis that leverages an existing intra-program taint analysis to detect security vulnerabilities in multiple communicating programs. The proposed framework has been implemented above the intra-program taint analysis of the Julia static analyzer. Preliminary experimental results on multi-program IoT systems, publicly available on GitHub, show that the technique is effective and detects inter-program flows of tainted data that could not be discovered by analyzing each program in isolation.

The numerous design specifications in the service-oriented architecture (SOA) standard space reflects knowledge captured from the various perspectives. However, most of these approaches merely exhibit any compliance with service design facets described in reference architectures. Moreover, majority of this approach lacks correspondence between the business process facets to service design facets and its real-world effects. This leads to semantic gap between services representation, its association with the business processes, and invocation of services to its real-world effects. In this paper a formal model of service-oriented system (SOS) is proposed. The SOS is divided into information model, process model and action model. The semantic relationship between these models helps in reducing the gap between the business processes and services, as well as services to its real-world effect. Further, the proposed service model facilitates flexible, reusable and scalable service composition and it follows the open reference standards of SOA.

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