In today’s rapidly evolving digital landscape, blockchain and artificial intelligence (AI) are at the forefront of transforming various industries, and education is no exception. The convergence of these two revolutionary technologies promises to reshape the modern education system by enhancing data security, promoting personalized learning, and creating decentralized frameworks for record-keeping and credentialing. This book delves into how blockchain and AI can drive a more inclusive, efficient, and secure educational ecosystem, where student-centered approaches and data integrity are paramount.This book is organized into several sections, each exploring the distinct roles of blockchain and AI within education. It begins with an introduction to the fundamental principles of these technologies and an overview of their individual strengths. Following this, chapters examine blockchain’s role in secure credential verification, decentralized learning platforms, and the protection of digital records. Next, the discussion shifts to AI applications, covering adaptive learning models, predictive analytics, and AI-driven administrative support. Finally, the book provides real-world case studies and future projections on how blockchain and AI together can tackle challenges in education, such as data privacy, resource distribution, and student engagement, ultimately creating an interconnected and resilient educational framework.This book is designed for educators, administrators, policymakers, technology enthusiasts, and researchers who are interested in the transformative potential of emerging technologies in education. It serves as a comprehensive guide for those looking to understand the practical applications and implications of blockchain and AI in the modern education system.

With the advent of smart healthcare services, rapid and automated diagnosis from images of skin lesions is critical to combat fast-spreading viruses such as monkeypox (Mpox or MPX) and significantly improve public health. The recent cases in Thailand reporting a suspected first case on August 21, 2024, and Sweden on August 14, 2024, among others, highlight the pandemic threat of Mpox. This study presents the Deep Digital Twin Services for Personalized Treatment (D2T-PT) model, which combines transfer learning and Digital Twin (DT) technology to improve the accuracy of Mpox detection and real-time monitoring, supported by the Squeeze-and-Excitation Block (SEB) attention mechanism, which opens up new horizons for personalized healthcare. Convolutional Neural Network (CNN) models were tested on the Monkeypox Skin Lesion Dataset (MSLD), with the advanced adaptive NasNetMobile model achieving excellent results: 100% recall, 98% ROC score, 97.78% accuracy with precision of 95%. This robust model enables physicians to make early and accurate Mpox diagnoses and monitor patient response to treatment in real-time, ultimately helping to contain the spread of the virus

The increasing adoption of Unmanned Aerial Ve-hicles (UAV s) in various critical applications necessitates robust security measures to protect these systems from cyber threats. In response, this research introduces an innovative Intrusion Detection System (IDS) specifically tailored for UAV s. The proposed IDS leverages Hierarchical Attention-based Long Short-Term Memory (H-LSTM) networks to effectively model the intricate temporal dependencies in UAV data. This architecture allows for comprehensive surveillance of UAV behavior, capturing both short-term anomalies and long-term deviations from expected patterns. The hierarchical attention mechanism enables the system to focus on salient features within the data, enhancing detection accuracy and robustness. To address the critical need for interpretable AI in cybersecurity, we incorporate Shapley Ad-ditive Explanations (SHAP) into our IDS. SHAP values provide a coherent and intuitive explanation of the IDS's decisions by emphasizing the specific features and their contributions to the intrusion detection process. The performance of the proposed system is rigorously evaluated using the N-BaIoT dataset. Our experiments demonstrate that the H-LSTM-based IDS outper-forms traditional methods, achieving a higher detection rate while minimizing false positives. Moreover, the incorporation of SHAP explanations facilitates rapid incident analysis, allowing security professionals to discern between genuine threats and benign anomalies effectively.

This paper presents the System for Emotion and Engagement Recognition in Education (SEERE), a cutting-edge advancement integrating computer vision and deep learning tech-nologies to evaluate real-time student engagement through facial emotion recognition and eye tracking. SEERE, a transformative educational tool built on the robust YOLO V8 architecture, customizes the FER2013 dataset, making use of meticulously annotated emotion and eye position data. It goes further, es-tablishing a unique ‘concentration metric,’ a quantitative index of student engagement, bridging a gap in modern responsive teaching approaches. Higher concentration metrics signal height-ened student engagement, offering educators real-time data to adjust teaching techniques and feedback accordingly. The paper provides a thorough review of facial emotion recognition models, setting the stage for understanding the innovative strides made by SEERE. Detailed discussions on the prototype's design and architecture are followed by initial experimental results, reinforcing the system's validity and potential

Drones, also known as Unmanned Aerial vehicles(UAVs), are increasingly used in various applications, including agriculture, construction and infrastructure, environmental conservation, water resources management(WRM) etc. Multiple drones are interconnected with each other as UAV swarms. These UAV swarms offer a versatile and cost-effective tool for WRM. This paper proposes the multi-drone network for WRM known as HydroDrone. The purpose of HydroDrone is to provide valuable data and insights to support decision making and improve the resilience of water supply systems on the verge of ever-changing environmental conditions. In addition, we discussed task allocation to individual drones within the swarm. HydroDrone schedules and balances the task load for water resource management. We also introduced security to multi-drone communication with the base and core stations by incorporating blockchain technology due to its decentralized nature.

The integration of Unmanned Aerial Vehicles (UAVs) in smart agriculture has significantly enhanced precision farming practices, enabling real-time monitoring and data collection for improved crop management. However, the reliance on wireless communication in UAV networks poses security challenges that can compromise the integrity and confidentiality of sensitive agricultural data. This paper proposes a novel approach to address these concerns through the incorporation of blockchain technology for secure communication in UAV networks deployed for smart agriculture. The proposed system leverages the decentralized and tamper-resistant nature of blockchain to establish a trust-based communication framework. Each UAV node in the network is equipped with a blockchain-enabled communication protocol, ensuring that data exchanges are securely recorded in an immutable ledger. This not only enhances data integrity but also mitigates the risk of unauthorized access and manipulation. To facilitate secure communication, smart contracts are employed to automate and enforce predefined rules governing data transactions within the UAV network. This ensures that only authenticated and authorized entities can access and modify agricultural data, fostering a transparent and accountable ecosystem. Additionally, cryptographic techniques such as public-key encryption enhance the confidentiality of transmitted data, safeguarding sensitive information from eavesdropping and unauthorized interception. The proposed blockchain-enabled secure communication system is further enhanced by incorporating consensus mechanisms that validate and confirm the integrity of data across the network. By doing so, the trustworthiness of the entire UAV network is strengthened, reducing the likelihood of malicious activities and enhancing overall system resilience. © 2024 IEEE.

School teachers, both experienced and novice, are bound to follow the predesigned K-12 curriculum focusing primarily on theoretical content knowledge. They have only limited opportunities to get acquainted with experiential teaching methods incorporating practical laboratory experiments. Deficiency of practical knowledge upskill programs predominantly affects teaching competence in subjects like basic electronics. Fostering electronics teaching competency is often ignored despite the higher significance of electronics. Further, there is a scarcity of research studies on the effectiveness of practical electronics training for school teachers. Against this backdrop, this paper explores the impact of a hands-on training cum experimentation program for school teachers organized by the IEEE Education Society (EdSoc) Kerala Chapter. Titled as 'School Teachers' Electronics Practicals Upskilling Program (STEP-UP),' it envisioned upskilling school teachers of Kerala, a southern state in India. The STEP-UP was focused on basic electronics engineering for day-to-day applications. To study the impact of STEP-UP on school teachers, we used the Kirkpatrick model, an established method for evaluating training programs. The impact assessment of the training program is deliberated based on the revised Kirkpatrick model with the integration of STEP-UP keywords. It was inferred from the study that school teachers are interested in actively participating in practical skill development programs. Moreover, teachers' degree of involvement emphasizes the potential of such programs in enhancing teaching quality rooted in experiential learning. The paper ends with offering a few suggestions and recommendations in accordance with the research findings on the impact of STEP-UP.

The engineering education today emphasizes the need to combine book learning with real-world application. However, much of the research done by undergraduates, which could be very valuable, is scattered and not fully used. To address this, a new tool called 'AI-based Research Companion (ARC)' has been developed. ARC leverages advanced Generative AI technology, including GPT-4, to systematically organize, enhance, and offer personalized recommendations for undergraduate research projects. This platform is more than a simple tool; it aims to inspire undergraduates to dive into research by making the process approachable and engaging, thus increasing participation in research activities. Initial assessments of ARC have revealed an encouraging rise in student engagement with research, indicating a shift towards more research-oriented projects. The integration of GPT-4 within ARC stands out significantly; it precisely addresses the detailed demands of undergraduate research by providing a tailored, intelligent exploration pathway. By incorporating GPT-4's advanced features with a user-centric design, ARC emerges as an innovative platform, emphasizing the pivotal role of Generative AI in enhancing and expanding undergraduate research initiatives.

Vehicle-road cooperation systems (VRCSs) use next-generation Internet technologies, including 5G, edge computing, and artificial intelligence to improve mobility, comfort, and travel efficiency. Internet of Vehicles (IoV) ecosystem serves as the technological backbone for VRCS by enabling seamless communication and data exchange between vehicles, infrastructure, and traffic management centers. This enables real-time, high-speed communication, efficient data processing, and enhanced security, fostering the development of autonomous driving, smart traffic management, and seamless connectivity within the VRCS ecosystem. At the same time, cyber attacks have become more complex, persistent, organized, and weaponized in IoV network. Threat intelligence (TI) has emerged as a prominent security approach to obtain a complete view of the dynamically growing cyber threat environment. On the other hand, modeling TI is a challenging task due to the limited labels available for different cyber threat sources. Second, most of the available designs require a large investment of resources and use handcrafted features, making the entire process error-prone and time-consuming. To tackle these challenges, this article presents TIMIF, a deep-learning-based TI modeling and identification framework for intelligent IoV and is based on three key modules: first, the proposed TIMIF adopts an automated pattern extractor (APE) module to extract hidden patterns from IoV networks. Employing its output, we design a TI-based detection (TIBD) module to detect abnormal behavior and TI-attack type identification (TIATI) module to identify attack types. Extensive experiments are carried out on three different publicly intrusion data sources, namely, HCRL-car hacking, ToN-IoT, and CICIDS-2017 to illustrate the utility of the TIMIF framework over some commonly used baselines and state-of-the-art techniques.

This book reviews research works in recent trends in blockchain, AI, and Digital Twin based IoT data analytics approaches for providing the privacy and security solutions for Fog-enabled IoT networks. Due to the large number of deployments of IoT devices, an IoT is the main source of data and a very high volume of sensing data is generated by IoT systems such as smart cities and smart grid applications. To provide a fast and efficient data analytics solution for Fog-enabled IoT systems is a fundamental research issue. For the deployment of the Fog-enabled-IoT system in different applications such as healthcare systems, smart cities and smart grid systems, security, and privacy of big IoT data and IoT networks are key issues. The current centralized IoT architecture is heavily restricted with various challenges such as single points of failure, data privacy, security, robustness, etc. This book emphasizes and facilitates a greater understanding of various security and privacy approaches using the advances in Digital Twin and Blockchain for data analysis using machine/deep learning, federated learning, edge computing and the countermeasures to overcome these vulnerabilities.

Data-driven modeling using Artificial Intelligence (AI) is envisioned as a key enabling technology for Zero Touch Network (ZTN) management. Specifically, AI has shown huge potential for automating and modeling the threat detection mechanism of complicated wireless systems. The current data-driven AI systems, however, lack transparency and accountability in their decisions, and assuring the reliability and trustworthiness of the data collected from participating entities is an important obstacle to threat detection and decision-making. To this end, we integrate smart contracts with eXplainable AI (XAI) to design a robust cybersecurity framework for ZTN. The proposed framework uses a blockchain and smart contract-enabled access control and authentication mechanism to ensure trust among the participating entities. Additionally, with the collected data, we designed Digital Twins (DTs) for simulating the attack detection operation in the ZTN environment. Specifically, to provide a platform for analysis and the development of an Intrusion Detection System (IDS), the DTs are equipped with a variety of process-aware attack scenarios. A Self Attention-based Long Short Term Memory (SALSTM) network is used to evaluate the attack detection capabilities of the proposed framework. Furthermore, the explainability of the proposed AI-based IDS is achieved using the SHapley Additive exPlanations (SHAP) tool. The experimental results using N-BaIoT and a self-generated DTs dataset confirm the superiority of the proposed framework over some baseline and state-of-the-art techniques.

Artificial Intelligence (AI) based cyber threat detection tools are widely used to process and analyze a large amount of data for improved intrusion detection performance. However, these models are often considered as black box by the cybersecurity experts due to their inability to comprehend or interpret the reasoning behind the decisions. Moreover, AI-based threat hunting is data-driven and is usually modeled using the data provided by multiple cloud vendors. This is another critical challenge, as a malicious cloud can provide false information (i.e., insider attacks) and can degrade the threat-hunting capability. In this paper, we present a blockchain-enabled eXplainable AI (XAI) for enhancing the decision-making capability of cyber threat detection in the context of Smart Healthcare Systems. Specifically, first, we use blockchain to validate and store data between multiple cloud vendors by implementing a Clique Proof-of-Authority (C-PoA) consensus. Second, a novel deep learning-based threat-hunting model is built by combining Parallel Stacked Long Short Term Memory (PSLSTM) networks with a multi-head attention mechanism for improved attack detection. The extensive experiment confirms its potential to be used as an enhanced decision support system by cybersecurity analysts.

The profound growth in the human population over the past two centuries has created a new issue in food security. Due to the high demand for food, there is an increasing burden on the agricultural supply chain (ASC) to satisfy the hunger of every individual. As a result, there is a possibility for spoilt or contaminated food to enter the ASC. If the end-consumer consumes these bad food products, it can lead to food poisoning and even death in certain circumstances. In order to ensure that the food delivered to the consumer is safe, it is necessary to monitor the food product as it passes through the different entities present in the ASC. The traditional ASC lacks traceability and reliability. Traceability is necessary in determining the origin of a crop, while reliability is necessary in preventing foul play by any entity. Therefore, developing a traceable and reliable system for the existing ASC model has become very important. The transparent, decentralized, and immutable qualities of Blockchain, along with the help from IoT devices, will allow us to actively trace the food from farm-to-fork as it passes through the supply chain while maintaining a high reliability between each entity. Thus, this paper proposes a novel ASC model, incorporated using Blockchain and IoT technology, to mitigate the traceability and reliability issues in the ASC.

The proliferation of agricultural supply chain encompasses participants such as farmers, intermediate silos, transformation plants, and clients. Managing this agro-supply chain involves various functions related to the flow of both materials and information. Before entering the market, crop protection products and inputs undergo rigorous testing and regulatory scrutiny. Despite these measures, counterfeit products reach end-users due to insufficient transparency and the sharing of outdated information among stakeholders. To address this issue, the paper suggests a three-tiered integrated solution: the Product layer, Blockchain layer, and SSI layer. This strategy involves attaching Near-Field Communication (NFC) tags to the packages at the product layer, with the blockchain monitoring each step of the supply chain. The NFC tags can be read to verify the authenticity and other details of the product. Certifications for products, inputs, and the identities of dealers and consumers are stored as self-sovereign IDs in digital wallets. The authenticity details of producers undergo auditing by the certification authority, which is then transferred to the verification authority. The Verifier confirms these details and generates a verifiable presentation received by the consumer, enabling them to make informed purchases. This approach eliminates product tampering and the involvement of unverified producers and dealers in the supply chain. The comprehensive explanation and investigation of proposed framework state adequate guidelines to make counterfeit resistance agricultural supply chain system.

The use of Open Radio Access Networks (Open RAN) in vehicular networks can lead to better connectivity, reliability, and performance. However, communication in this setting is often done over an unsecured wireless network, which creates a challenge in verifying the validity of received transactions by Internet of Vehicles (IoV) due to the untrusted network. It also creates a potential for attackers to tamper with the data content and conduct different IoV-related attacks. To address these issues, a new framework named 'STIoV' has been proposed for secure and trustworthy communication in IoV. The framework includes a mutual authentication scheme to register and exchange session keys among the IoV participants, and a credit-based trust management system to assign reputation scores for the vehicular devices. The latter scheme discards transactions with low credit scores. To overcome the complexity and variability of the IoV network, digital twin technology is used to map Road Side Units (RSU) servers into virtual space, which facilitates constructing the vehicular relation model. An Intrusion Detection System (IDS) based on deep learning techniques is also introduced to detect anomalies in the traffic flow. The legitimate data is further used by the blockchain scheme for transaction verification, block creation and addition. Finally, the proposed framework has been evaluated based on two network intrusion datasets, and the results show the accuracy and efficacy of STIoV in comparison to several recent state-of-the-art solutions.

The consumer Internet of Things (IoT) applications in particular smart cities are mostly equipped with Internet-connected networked devices to improve city operations by giving access to a massive amount of valuable information. However, these smart devices in a smart city environment mostly use public channels to access and share data among different participants. This has introduced a great interest in using authentication and key agreement (AKA) mechanisms and intrusion detection systems (IDS) based on artificial intelligence (AI) techniques. However, most of the AKA mechanisms have high computation and communication costs and cannot be trusted completely. On the other hand, the AI-based IDS are treated as blackbox by the security analyst due to their inability to explain the reasons behind the decision. In this direction, we have integrated blockchain-based AKA mechanism with explainable artificial intelligence (XAI) for securing smart city-based consumer applications. Specifically, first, the participating entities communicate with each other in a secure manner to exchange data using a blockchain-based AKA mechanism. On the other hand, we have used SHapley Additive exPlanations (SHAP) mechanism to explain and interpret the prominent features that are most influential in the decision. The practical implementation of the proposed framework proves the efficiency over other recent state-of-the-art techniques.

The next-generation digital revolution is anticipated to be the convergence of Consumer Internet of Things (CIoT) platforms and Metaverse. The use of Metaverse in CIoT can offer a hyper-spatiotemporal, self-sustaining 3-D virtual shared space for people to interact, work, and play. Despite the hype around CIoT-inspired Metaverse, security and privacy concerns are seen as the two biggest obstacles in the communication infrastructure and information gathering procedures. The eXplainable Artificial Intelligence (XAI) and blockchain have the potential to reshape and transform the CIoT-inspired Metaverse by bringing significant enhancements in terms of explainability, interpretability, transparency, traceability, and immutability regarding data and communications. In this article, we first discuss the security and privacy issues in CIoT-inspired Metaverse. Second, we discuss the importance and properties of XAI and blockchain with a use case to demonstrate the benefits of the proposed architecture to tackle the aforementioned obstacles. Last, we highlight the future research directions in building futuristic CIoT-inspired Metaverse.

The integration of the Internet of Things (IoT) with traditional healthcare systems has improved quality of healthcare services. However, the wearable devices and sensors used in Healthcare System (HS) continuously monitor and transmit data to the nearby devices or servers using an unsecured open channel. This connectivity between IoT devices and servers improves operational efficiency, but it also gives a lot of room for attackers to launch various cyber-attacks that can put patients under critical surveillance in jeopardy. In this article, a Blockchain-orchestrated Deep learning approach for Secure Data Transmission in IoT-enabled healthcare system hereafter referred to as “BDSDT” is designed. Specifically, first a novel scalable blockchain architecture is proposed to ensure data integrity and secure data transmission by leveraging Zero Knowledge Proof (ZKP) mechanism. Then, BDSDT integrates with the off-chain storage InterPlanetary File System (IPFS) to address difficulties with data storage costs and with an Ethereum smart contract to address data security issues. The authenticated data is further used to design a deep learning architecture to detect intrusion in HS network. The latter combines Deep Sparse AutoEncoder (DSAE) with Bidirectional Long Short-Term Memory (BiLSTM) to design an effective intrusion detection system. Experiments on two public data sources (CICIDS-2017 and ToN-IoT) reveal that the proposed BDSDT outperformed state-of-the-arts in both non-blockchain and blockchain settings and have obtained accuracy close to 99% using both datasets.

The recent technological advancements in wireless communication systems and the Internet of Things (IoT) have accelerated the development of zero touch networks (ZTNs). ZTNs provide self-monitoring, self-configuring, and automated service-level policies that cannot be fulfilled by the traditional network management and orchestration approaches. Despite the hype, the majority of data exchange between participating entities occurs over insecure public channels, which present a number of possible security risks and attacks. Toward this end, we first analyze the attack surface on IoT-enabled ZTNs and the inherent architectural flaws for such threats. After an overview of attack surface, this article presents a new deep-learning-and blockchain-assisted case study for secure data sharing in ZTNs. Specifically, first, we design a novel variational autoencoder (VAE) and attention-based gated recurrent units (AGRU)-based intrusion detection system (IDS) for ZTNs. Second, a novel authentication protocol that combines blockchain, smart contracts (SCs), elliptic curve cryptography (ECC), and a proof of authority (PoA) consensus mechanism is developed to improve secure data sharing in ZTNs. The extensive experimental results show the effectiveness of the proposed approach. Lastly, this work discusses critical issues, opportunities, and open research directions to solve these challenges.

The integration of Terrestrial and Non-Terrestrial Networks (TNTNs) with the sixth-generation (6G) wireless ecosystem will revolutionize the futuristic communication networks by enabling comprehensive interconnection and quality of service. However, such an integrated network will raise serious security and privacy issues due to the insecure communication among untrusted participating entities over an open unsecured public channel. As a result, the entire ecosystem can be accessed by both legitimate users and adversaries. Distributed AI when integrated with blockchain has a great potential to provide a feasible alternative to solve the security and privacy issues of 6G-Assisted TNTNs. As a case study, a distributed AI is first adopted to cooperatively participate in the training process of a global model directly on devices. This approach ensures privacy and security of user data, and also confirms that only valid data is used by smart contracts to execute consensus mechanism. The multiple parallel blockchain are employed to securely and efficiently manage, and share data at each layer of 6G-Assisted TNTNs. The efficiency of the proposed framework is demonstrated by numerical findings. Finally, prospective open research issues in employing distributed AI and blockchain for 6G-Assisted TNTNs are highlighted.

Internet of Things (IoT)-enabled Smart Grid (SG) network is envisioned as the next-generation network for intelligent and efficient electric power transmission. In SG environment, the Smart Meters (SMs) mostly exchange services and data from Service Providers (SPs) via insecure public channel. This makes the entire SG ecosystem vulnerable to various security threats. Motivated from the aforementioned challenges, we incorporate Digital Twin (DT) technology, Software-Defined Networking (SDN), Deep Learning (DL) and blockchain into the design of a novel SG network. Specifically, a secure communication channel is first designed using an authentication method based on blockchain technology that has the ability to withstand a number of well-known assaults. Second, a new DL architecture that includes a self-attention mechanism, a Bidirectional-Gated Recurrent Unit (Bi-GRU) model, fully connected layers, and a softmax classifier is designed to enhance the attack detection process in SG environments. To deliver low latency and real-time services, the SDN is next employed as the network’s backbone to send requests from SMs to a global SDN controller. DT technology is finally integrated into the SDN control plane, which stores the operating states and behavior models of SMs and communicates with SMs. The efficiency of the proposed framework is demonstrated by the blockchain implementation used in the SG network to assess computing time for the various numbers of transactions per block. Finally, the numerical results based on the N-BaIoT dataset shows better intrusion detection.

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