Federated Learning (FL) is revolutionizing edge networks by enabling decentralized Machine Learning (ML) while preserving data privacy. This chapter explores the integration of Federated Learning in Edge Networks, highlighting its role in distributed intelligence, real-time decision-making, and adaptive learning at the edge. Unlike traditional centralized learning, FL allows models to be trained locally on edge devices—such as IoT sensors, mobile devices, and autonomous systems—without transferring raw data, ensuring privacy, security, and bandwidth efficiency. We discuss key architectural frameworks, communication protocols, and optimization techniques that enhance FL’s performance in edge environments. Challenges such as heterogeneous data distribution, resource constraints, security vulnerabilities, and model aggregation are examined, along with recent advancements in privacy-preserving mechanisms, including differential privacy and secure multiparty computation. Additionally, the chapter presents real-world applications of FL in smart cities, healthcare, autonomous systems, and industrial IoT, demonstrating its potential to drive intelligent, decentralized decision-making. By connecting FL with Edge Computing (EC) and AI-based analytics, this work provides insights into the future of privacy-centric, scalable, and efficient AI solutions at the edge. The discussion offers a roadmap for researchers and practitioners to leverage FL-driven intelligence in dynamic, resource-constrained edge networks.

Technological developments in the communications sector are augmenting the Internet of Vehicles (IoV) to a great extent. As a result, the IoV environment is changing and includes lower costs, lightning-fast data exchange, and minimal response time. However, when cyberspace grows, these advantages come with increased privacy and security problems. IoV causes communication hiccups and network congestion as vehicles rely on trusted authorities (TA) for registration and authenticity. Furthermore, the traceability, anonymity, and transparency of the intermediary devices and the central TA need to be made clear. This paper uses blockchain technology to propose a unique vehicle authentication handover framework to overcome these issues. The suggested solution also integrates Proof of Reputation (PoR), an updated blockchain consensus algorithm, to improve transparency. Using the Elliptic Curve Cryptography (ECC) to minimise computational delay time, the method reduces key sizes without affecting security.

The rapid progress of the Internet of Vehicles (IoV) has created new potential for intelligent transportation systems (ITS), including improved connectivity between vehicles, roadside units (RSUs), and cloud servers. However, effective, safe, and scalable authentication and key management are required to maintain the security of vehicle communication. This paper introduces a certificate-less authentication and key management mechanism explicitly designed for dynamic IoV environments. The proposed framework uses secure key exchange, cluster formation for efficient intra-vehicle communication, and mutual authentication between vehicles, RSUs, and cloud servers rather than traditional certificate-based public key infrastructures (PKI). By eliminating the certificate requirement, the protocol minimizes communication overhead and computational complexity while retaining a strong security posture. Simulation results show that the protocol is effective at securing vehicle-to-vehicle (V2V), vehicle-to-RSU (V2RSU), and RSU-to-cloud server communications, with low latency, high scalability, and resilience to known security attacks such as replay, impersonation, and man-in-the-middle attacks. This study provides a unique, lightweight, and secure approach for next-generation IoV systems, supporting a safe and efficient ITS ecosystem.

AIoT in medicine, or the combination of artificial intelligence (AI) and the internet of things (IoT), is transforming healthcare delivery and patient outcomes. This chapter contains a collection of real- world case studies and success stories demonstrating the revolutionary power of AIoT technology in various medical fields. In these instances, the focus is on how AIoT boosts diagnosis accuracy, enables personalised treatment regimens, and improves operational efficiencies in healthcare organisations. Key case studies include using AIoT in remote patient monitoring, where continuous data gathering from wearable devices, paired with AI algorithms, enables real- time health tracking and early intervention. Another example involves the application of AIoT in the predictive maintenance of medical equipment, which reduces downtime and ensures the availability of key items. Furthermore, the authors investigate the significance of AIoT in optimising hospital workflows, such as expediting patient admissions and inventory management using smart sensors and automated systems.

The next-generation Internet of Vehicles (IoVs) seamlessly integrates humans, vehicles, roadsideunits (RSUs), and service platforms to improve road safety, enhance transit efficiency, and deliverconvenience while preserving environmental sustainability. However, the frequent handoversbetween RSUs in IoVs expose communication to insecure public channels, rendering the systemsusceptible to various security threats and attacks. To address these challenges, we proposea blockchain-enabled light weight handover authentication (FLEX-HAND) scheme. FLEX-HANDensures secure handover of traffic information during vehicle transitions between RSUs using alightweight mutual authentication and key agreement protocol, supported by blockchain technology.The mechanism enables vehicles to authenticate anonymously and securely exchange sessionkeys with the next RSU during the handover process, preserving data integrity and confidentiality.A trusted cluster head aggregates the data, which is securely transmitted to the nearby RSUusing the established session keys. The RSU communicates with a cloud server (CS) for furtherdata aggregation and transaction generation. These transactions are structured into blocks and validatedusing a voting-based consensus mechanism within a peer-to-peer network of cloud servers,ensuring tamper-resistant data storage on the blockchain. Through rigorous informal security analysisand formal verification using the random oracle model, FLEX-HAND is demonstrated to beresilient against a wide array of security attacks, including impersonation, replay, and session keycompromise. Comparative studies highlight the superior performance of FLEX-HAND over existingapproaches, offering enhanced security, functionality, and reduced communication and computationoverhead. Furthermore, blockchain simulation validates the practical feasibility and efficiencyof the proposed handover authentication scheme in dynamic IoV environments.

The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling real-time monitoring, data sharing, and improved patient outcomes through interconnected medical devices. However, IoMT faces significant challenges, including data security, privacy, interoperability, and trust among stakeholders. Blockchain technology, with its decentralized, immutable, and transparent nature, offers promising solutions to address these challenges. This chapter explores the integration of blockchain technology in IoMT, presenting a comprehensive overview of its potential to enhance data security, ensure patient privacy, and streamline operations through smart contracts and decentralized frameworks. Key architectural components, blockchain-enabled frameworks, and consensus mechanisms suitable for IoMT are discussed in detail. Real-world applications, including patient data management, supply chain transparency, and remote patient monitoring, are highlighted alongside case studies demonstrating successful implementations. The chapter also examines the limitations and challenges of adopting blockchain in IoMT, such as scalability, regulatory compliance, and integration complexity. Finally, it identifies future research directions, emphasizing the role of emerging technologies like AI, IoT, and quantum-resistant blockchain in advancing IoMT. By bridging the gap between technology and healthcare, this chapter underscores the transformative potential of blockchain in building a secure, efficient, and trustworthy IoMT ecosystem.

The extensive use of predictive models in healthcare raises significant challenges in dealing with patient data privacy and adherence to laws like the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR). Federated Learning (FL) presents a viable alternative by enabling cooperative model training over dispersed datasets, protecting sensitive patient data security and confidentiality. This study explores the use of hybrid, vertical, and horizontal FL paradigms in predicting heart disease. The study concentrates on how these strategies handle issues with data distribution, increase privacy by utilizing methods like homomorphic encryption and differential privacy, and eventually raise predictive model accuracy. This study highlights FL’s ability to transform healthcare by analyzing real-world use cases and metrics. It sets new benchmarks for ethical artificial intelligence (AI) in medicine by illustrating how privacy-preserving machine learning may produce precise illness prediction while protecting patient confidentiality.

6G communication is a revolutionary technology in wireless communication. It overtakes the 5G technology in terms of security. 6G opens its boundaries for the various Internet of Things applications in smart cities. Internet of Vehicles (IoV) sends and receives traffic-related data between various entities such as vehicles, pedestrians, roadside units (RSUs), mobiles, cloud servers, and other entities. This advancement in 6G ensures that communication between entities is more secure in an IoV environment. The increasing number of entities causes trust and privacy issues as it generates massive amounts of data with increased mobility. This chapter introduced the blockchain concept, which provides security and privacy in the IoV environment. Simultaneously, federated learning (FL) protects the user’s privacy. FL reduces the attacks and ensures privacy by storing the information locally. In addition, blockchain provides immutability by allowing only trusted parties to participate. Integrating blockchain and FL provides more security among various entities in transportation systems. This chapter discusses the blockchain and FL challenges and 6G communication technology solutions. The simulation is performed using SUMO simulator to achieve the dynamic vehicular environment.

Intelligent transportation systems (ITS) improve vehicle connectivity, traffic efficiency, and road safety. Conversely, quick and safe vehicle authentication still poses a significant issue, especially at the handover time when switching between roadside units (RSUs), where network efficacy is influenced by computational overhead and re-authentication delays. To overcome these issues, this paper proposes DYNAMIC-TRUST. This blockchain-based authentication framework relies on the Proof of Trust (PoT) consensus mechanism to avoid redundant re-authentication, minimizing computation and communication costs. Compared to conventional authentication approaches, our method decentralizes vehicle revocation, allowing RSUs to revoke compromised vehicles autonomously without relying on a trusted authority, providing resilience regardless of adversarial conditions. The proposed framework’s resistance to identity theft, replay, and Sybil attacks has been proven by formal security analysis using Scyther and the Real-Or-Random (ROR) oracle model. Also, the Simulation of Urban Mobility (SUMO) is used to evaluate real-world practicality, proving improved scalability, lowered authentication latency, and greater network efficiency over various vehicular circumstances. Blockchain’s potential for enhancing vehicular network performance, trust, and security is highlighted in this study, which helps to develop smart cities and 6G-enabled Internet of Vehicles (IoV) infrastructures.

Intelligent Transport Systems (ITS) represent a burgeoning and transformative concept aimed at reshaping the landscape of mobility both within and outside cities. The Internet of Vehicles (IoV) serves as a networked ecosystem that integrates infrastructure, pedestrians, fog, cloud, and vehicles to enhance the capabilities of ITS. While IoV holds tremendous promise for advancing transportation systems, its networked and data-centric nature raises numerous security concerns. Several solutions have recently been proposed to address these IoV-related challenges; however, many of them involve significant computational overhead and exhibit security flaws. Moreover, there is concern about malicious vehicles infiltrating the network and potentially gaining unauthorized access to services. To tackle these challenges, we present SEATS, a ground breaking solution. The system aims to ensure the secure exchange of information, authentication by both parties, and effective key management among vehicles, roadside units (RSU), and cloud servers. We conduct extensive security and privacy assessments on the proposed approach using the Real-or-Random (ROR) oracle model and Scyther tools, supplemented by an informal security study. The framework is simulated using the Objective Modular Network Testbed in C++ (OMNet++). To demonstrate the efficacy of our approach, we compare it to existing methods, evaluating computation and communication costs.

Autonomous vehicles (AVs) play a significant role in intelligent transportation systems (ITS), which handle vehicles without human interference. The AVs are integrated with the Internet of Vehicles (IoV) to connect with more vehicles, sensors, and fog servers and share data. This makes the vehicles vulnerable to attacks and leads to unauthorized access. To overcome this drawback, we introduced biometric authentication for secure communication of vehicles, roadside units (RSUs), and fog servers. In this protocol, we generated two session keys between entities. The communication cost, computation cost, and security parameters are compared with existing methods to show that the proposed protocol is more efficient than others. Finally, the formal and informal security analysis ensures the proposed protocol is more secure.

Modern living is significantly impacted by intelligent transportation systems (ITS), which have the potential to alter how transportation is maintained and improve multiple facets of day-to-day mobility while also increasing security, effectiveness, and convenience. ITS offers the fundamental framework and technology necessary for IoV to operate efficiently. The IoV ecosystem foundation is the integration of sensors, communication networks, architectural components, and data analyses from ITS, which enables vehicles to join a connected, intelligent transportation network. Although ITS and IoV have many advantages, the increasing connectivity and data sharing also pose security risks, including those related to eavesdropping, authentication, privacy, and data integrity. To address these issues, we developed the novel, lightweight SAKM-ITS protocol, which enables authentication and key management between vehicles, roadside units (RSUs), and cloud servers. Using Scyther and Tamarin Prover tools, the protocol security is tested. For different attacks, an informal security study is also conducted. We also compared the findings with other recent computing and communication costs studies.

The advancements in telecommunications are significantly benefiting the Internet of Vehicles (IoV) in various ways. Minimal latency, faster data transfer, and reduced costs are transforming the landscape of IoV. While these advantages accompany the latest improvements, they also expand cyberspace, leading to security and privacy concerns. Vehicles rely on trusted authorities for registration and authentication processes, resulting in bottleneck issues and communication delays. Moreover, the central trusted authority and intermediate nodes raise doubts regarding transparency, traceability, and anonymity. This paper proposes a novel vehicle authentication handover framework leveraging blockchain, IPFS, and hybrid computing. The framework uses a Proof of Reputation (PoR) consensus mechanism to improve transparency and traceability and the Elliptic Curve Cryptography (ECC) cryptosystem to reduce computational delays. The suggested system assures data availability, secrecy, and integrity while maintaining minimal latency throughout the vehicle re-authentication. Performance evaluations show the system's scalability, with creating keys, encoding, decoding, and registration operations done rapidly. Simulation is performed using SUMO to handle vehicle mobility in an IoV environment. The findings demonstrate the practicality of the proposed framework in vehicular networks, providing a reliable and trustworthy approach for IoV communication.

This paper comprehensively analyzes Facebook data, a rich source of valuable information within big data. The study encompasses data collection, preprocessing, and exploratory analysis of a substantial dataset derived from Facebook interactions and activities. Through advanced data processing techniques and statistical methodologies, we unveil meaningful insights into user behavior, content engagement, and patterns on the platform. This analysis has significant implications for understanding user preferences, trends, and the dynamics of social networking in the digital age. The study revealed valuable trends, patterns, and metrics related to user interactions, posting habits, etc. Integrating Hive commands for data analysis and R programming for visualization offered a powerful synergy that made the findings accessible and visually compelling. The project underscores the importance of big data analytics in unraveling the hidden dimensions of social media and offers a practical demonstration of the power of data-driven decision-making. The findings and visualizations derived from this analysis shed light on the vast landscape of Facebook, enabling informed decisions and future research in social media analytics.

In the field of vehicular communication, the Internet of Vehicles (IoV) serves as a new era that guarantees increased connectivity, efficiency, and safety. The modern area and new technology have their challenges and constraints, though. This paper thoroughly examines these constraints significantly; we show how blockchain technology is being used to overcome them. This paper primarily explores the complexities of Blockchain-enabled Internet of Vehicles (BIoV) architectures, the applications they serve, and the robust security features they provide through a systematic literature review (SLR). In addition, we look at the several ways that blockchain and IoV might be integrated and investigate the subtle factors that should be considered when choosing consensus algorithms to maximize performance on different blockchains. This paper also addresses the methods and tools used to identify and avoid fraudulent activities in BIoV networks at a maximum level of security. It also reveals the wide range of BIoV applications and analyzes the different security levels they provide. In closing, we give an idea of the possibilities that will continue to develop the blockchain and IoV environment, reducing the roadblocks and advancing this combination toward a more secure, effective, and connected future for vehicle communication systems.

In upcoming iterations of the internet of vehicles (IoVs), seamless communication will be facilitated among individuals, vehicles, roadside units (RSUs), and communication platforms. The overarching objectives include enhancing transit efficiency, ensuring comfort, improving road safety, and concurrently fostering environmental conservation. This research introduces a secure fog service for vehicular ad hoc networks (VANETs), enabling diverse traffic data services such as road alerts, congestion control, and autonomous driving. The authors propose a novel authentication approach for fog services. Leveraging physical unclonable function (PUF) and blockchain, this approach facilitates authentication between vehicles and road-side units (RSU), circumventing potential fraudulent fog nodes. A comprehensive security analysis demonstrates its resilience against known attacks. Comparative evaluation against existing approaches underscores our protocol's superior balance of security and overhead, making it well-suited for secure vehicle fog environments.

The Intelligent Transportation Systems (ITS) is a leading-edge, developing idea that seeks to revolutionize how people and things move inside and outside cities. Internet of Vehicles (IoV) forms a networked environment that joins infrastructure, pedestrians, fog, cloud, and vehicles to develop ITS. The IoV has the potential to improve transportation systems significantly, but as it is networked and data-driven, it poses several security issues. Numerous solutions to these IoV issues have recently been put forth. However, significant computing overhead and security concerns afflict the majority of them. Moreover, malicious vehicles may be injected into the network to access or use unauthorized services. To improve the security of the IoV network, the Mayfly algorithm is used to optimize the private keys continuously. To address these difficulties, we propose a novel VESecure system that provides secure communication, mutual authentication, and key management between vehicles, roadside units (RSU), and cloud servers. The scheme undergoes extensive scrutiny for security and privacy using the Real-or-Random (ROR) oracle model, Tamarin, and Scyther tools, along with the informal security analysis. An Objective Modular Network Testbed in OMNet++ is used to simulate the scheme. We prove our scheme's efficiency by comparing it with other existing methods regarding communication and computation costs.

As the cyber theft is increases, information safety and confidentiality are the major issues in wireless communications. 6G technology overcomes these difficulties to build a secured intelligent transportation system (ITS). Conventional transportation system faces high computation cost when road side unit (RSU) process each authentication vehicle request. In this chapter, to address this issue we introduced batch authentication and key exchange to secure user privacy and prevent attacks. To ensure message integrity, this system provides location-based information safely from RSU to vehicle without any changes. This system reduces the communication and computational costs. Simulation is performed using simulation of urban mobility (SUMO) simulator.

This paper proposes a novel approach to enhancing education systems by integrating federated learning techniques with big data analytics. Traditional data analysis methods in educational settings often need help regarding data privacy, security, and scalability. Federated learning addresses these issues by enabling collaborative model training across distributed datasets without data centralization, thus preserving the privacy of sensitive information. By harnessing the vast amounts of educational data generated from various sources such as online learning platforms, student information systems, and academic applications, federated learning empowers educational institutions to derive valuable insights while respecting data privacy regulations. Leveraging the collective intelligence of decentralized data sources, federated learning algorithms facilitate the development of robust predictive models for student performance, personalized learning recommendations, and early intervention strategies. Moreover, federated learning enables continuous model improvement by aggregating local model updates from participating institutions, ensuring adaptability to evolving educational landscapes. This paper explores the technical foundations of federated learning, its application in education systems, and its potential benefits in improving learning outcomes and fostering data-driven decision-making in education. Through a comprehensive review of existing literature and case studies, this research aims to provide insights into the opportunities and challenges associated with implementing federated learning-based big data analytics in education systems, ultimately paving the way for a more efficient and personalized approach to education.