The increasing integration of Vehicle-to-Grid (V2G) systems in modern energy networks necessitates secure, transparent, and efficient mechanisms for energy trading. However, existing centralized approaches are susceptible to cyberattacks, lack transparency, and face scalability challenges. This paper proposes a blockchain-enabled decentralized platform to ensure secure and tamper-proof energy transactions between electric vehicles (EVs) and the grid. The system leverages smart con-tracts for automated settlements, cryptographic protocols for enhanced data privacy, and artificial intelligence (AI) models for real-time anomaly detection and fraud prevention. By integrating blockchain's transparency and immutability with AI's adaptability, the proposed solution addresses key challenges in trust, scalability, and cybersecurity. A prototype implementation demonstrates the platform's effectiveness, achieving improved transaction throughput, reduced latency, and enhanced resistance to malicious activities compared to traditional systems. Extensive simulations validate the scalability and resilience of the proposed framework under varying transaction loads and attack scenarios. This work lays the foundation for a transformative approach to energy trading, fostering increased trust, participation, and sustainability in V2G markets.

6G applications rely on data-intensive AI models for network optimization. These demand a scalable and energyefficient framework to handle massive device networks with stringent latency requirements which current solutions struggle to support. Although reinforcement learning (RL) and split learning have matured to provide commercial solutions elsewhere. Current solutions in 6G have not used them systematically to achieve the sustainability goals. In this paper, we propose a three-layer framework that minimizes energy consumption of the communication system capable of handling large number of devices. The proposed solution uses RL agents at the edge layer to mathematically model the system and communicate to fog layer for aggregation. The aggregated feature maps are further communicated to cloud layer for global model training. We use split learning for communication and training, the learning at each device are communicated for global model creation effectively. Each edge device improves the overall RL model where system matures quickly consuming minimal energy. The proposed framework's efficacy has been tested extensively for accuracy and scalability, in terms of energy consumption, latency and memory utilizations. The simulation results validate the claims of maturity in models across edge, fog and cloud levels.

Different types of cryptocurrencies have surged rapidly in recent years, with recent trends in tokenization and memecoin. This growth highlights the cryptocurrency market’s rapid expansion, especially in token creation at decentralized exchanges (DEXs). At the same time, the evolution of artificial intelligence (AI) and Web3 presents significant challenges, including sustained user engagement in AI-driven applications while maintaining utility and scalability. Existing game theory-based methods often increase centralization and hence introduce security vulnerabilities. This hinders widespread adoption. To overcome these challenges, we propose Slinky Web3AI, a framework that integrates blockchain technology with AI to address these challenges. Slinky Web3AI supports long-term ecosystem development through a decentralized token creation mechanism and privacy-preserving incentive structures. Our proposed architecture demonstrates improved security, scalability, and efficiency, validated by experimental results. Slinky Web3AI provides a foundational framework for future AI and blockchain applications for future smart communities.

The Internet of Things (IoTs)-based remote healthcare applications provide fast and preventative medical services to the patients at risk. However, predicting heart disease is a complex task, and diagnosis results are rarely accurate. To address this issue, a novel Recommendation System for Cardiovascular Disease (CVD) Prediction Using IoT Network (DEEP-CARDIO) has been proposed for providing prior diagnosis, treatment, and dietary recommendations for cardiac diseases. Initially, the physiological data are collected from the patients remotely by using the four biosensors, such as ECG sensor, pressure sensor, pulse sensor, and glucose sensor. An Arduino controller receives the collected data from the IoT sensors to predict and diagnose the disease. A CVD prediction model is implemented by using bidirectional-gated recurrent unit (BiGRU) attention model, which diagnoses the CVD and classifies into five available cardiovascular classes. The recommendation system provides physical and dietary recommendations to cardiac patients based on the classified data, via user mobile application. The performance of the DEEP-CARDIO is validated by Cloud Simulator (CloudSim) using the real-time Framingham's and Statlog heart disease dataset. The proposed DEEP CARDIO method achieves an overall accuracy of 99.90%, whereas the MABC-SVM, HCBDA, and MLbPM methods achieve 86.91%, 88.65%, and 93.63%, respectively.

Advances in the connected vehicle and cloud computing technologies, Big data, and artificial intelligence techniques have opened new research opportunities. We can integrate them to work out the issues originating from transportation complexities and offer improved services. In this work, we present a seamless multi-module multi-layer vehicular cloud computing system developed using resources of parked vehicles, cloud computing facilities, and vehicular networking technologies. It can offer transportation-specific AI and Big data-empowered services to on-road vehicles. As use cases, we present two innovative and improved services, vehicular Big data mining and vehicular route optimization. A physical testbed is formed to show the feasibility of this work. Results analysis shows that the systems perform better than the standalone systems and servers under different scenarios. Relevant fundamental challenges and future outlooks are also highlighted in this work.

Perception of the disease and its management impacts patients with Psoriatic arthritis (PsA) to a great degree. Studies examining patients’ viewpoints and perception of their disease and its management are scarce. This multicentric cross-sectional survey was undertaken to understand the perspectives of patients with PsA. A survey questionnaire with items on demographics, awareness about their disease, treatment, physical therapy, quality of life and satisfaction with the care received was designed. After internal and external validation, a pilot survey was conducted, and the questionnaire was finalized. The final survey (with translations in local languages) was carried out at 17 centres across India. There were 262 respondents (56% males) with mean age of 45.14 ± 12.89 years. In 40%, the time lag between onset of symptoms and medical assessment for it was more than a year. In most of the patients, the diagnosis of PsA was made by a rheumatologist. Over 83% of patients were consulting their rheumatologist periodically as advised and fully compliant with the treatment. Lack of time and cost of therapy were the most common reasons for non-adherence to therapy. Eighty-eight patients (34%) were not fully satisfied with their current treatment. Over two-third of patients had never seen a physiotherapist due to barriers including a lack of time, pain, and fatigue. The daily activities and employment status were affected in nearly 50% of patients with PsA. The current survey has identified a gap in patients’ awareness levels and helps healthcare providers in understanding the varied perceptions of patients with PsA. Addressing these issues in a systematic manner would potentially improve the treatment approaches, outcomes, and patient satisfaction levels.

Owing to the prevalence of the Internet of things (IoT) devices connected to the Internet, the number of IoT-based attacks has been growing yearly. The existing solutions may not effectively mitigate IoT attacks. In particular, the advanced network-based attack detection solutions using traditional Intrusion detection systems are challenging when the network environment supports traditional as well as IoT protocols and uses a centralized network architecture such as a software defined network (SDN). In this paper, we propose a long short-term memory (LSTM) based approach to detect network attacks using SDN supported intrusion detection system in IoT networks. We present an extensive performance evaluation of the machine learning (ML) and deep learning (DL) model in two SDNIoT-focused datasets. We also propose an LSTM-based architecture for the effective multiclass classification of network attacks in IoT networks. Our evaluation of the proposed model shows that our model effectively identifies the attacks and classifies the attack types with an accuracy of 0.971. In addition, various visualization methods are shown to understand the dataset’s characteristics and visualize the embedding features.

Bio-inspired metaheuristics can be useful for the optimization of complex systems. Wireless sensor networks (WSNs) are massively distributed cyber-physical systems whose efficient operation requires appropriate design and control strategies. In certain contexts, like with randomly deployed WSNs, the physical network configuration can be affected only minimally, and optimal control strategies are crucial for optimizing network performance metrics like lifetime, coverage, and energy consumption. These metrics often conflict with each other, making network optimization a complex multi-objective problem. In this study, we introduce an improved version of a bi-objective genetic algorithm for the optimization of sensor network lifetime and target coverage. The new algorithm uses the generic evolutionary optimization framework together with a problem-specific heuristic mutation operator. We investigate the ability of the algorithm to find sensor schedules that extend network lifetime, and improve average target coverage while satisfying the minimum coverage requirement and show that the improved algorithm delivers better schedules than the original GA.

Summary Many scholastic researches have begun around the globe about the competitive technological interventions like 5G communication networks and its challenges. The incipient technology of 6G networks has emerged to facilitate ultrareliable and low‐latency applications for sustainable smart cities which are infeasible with the existing 4G/5G standards. Therefore, the advanced technologies like machine learning (ML), block chain, and Internet of Things (IoT) utilizing 6G network are leveraged to develop cost‐efficient mechanisms to address the issues of excess communication overhead in the present state of the art. Initially, the authors discussed the key vision of 6G communication technologies, its core technologies (such as visible light communication [VLC] and THz), and the existing issues with the existing network generations (such as 5G and 4G). A detailed analysis of benefits, challenges, and applications of blockchain‐enabled IoT devices with application verticals like Smart city, smart factory plus, automation, and XR that form the key highlights for 6G wireless communication network is also presented. In addition, the key applications and latest research of artificial intelligence (AI) in 6G are discussed facilitating the dynamic spectrum allocation mechanism and mobile edge computing. Lastly, an in‐depth study of the existing open issues and challenges in green 6G communication network technology, as well as review of solutions and potential research recommendations are also presented.

Blockchains have profoundly impacted finance and administration, but there are several issues with the current blockchain platforms, including a lack of system interoperability. Currently used blockchain application platforms only work within their networks. Although the underlying concept of all blockchain networks is mainly similar, it involves centralised third-party mediators to transact from other blockchain networks. The current third-party intermediates establish security and trust by keeping track of “account balances” and attesting to the validity of transactions in a centralised ledger. The lack of sufficient inter-blockchain connectivity hinders the mainstream adoption of blockchain. Blockchain technology may be a solid solution for many systems if it grows and works with other systems. For the multi-system blockchain concept to materialise, a mechanism that would connect and communicate with the blockchain systems of various entities in a distributed manner (without any intermediary) while maintaining the property of trust and integrity established by individual blockchains is required. Several methods for verifying cross-chain transactions have been explored in this paper among various blockchains. The efficient verification of cross-chain transactions faces many difficulties, and current research has yet to scratch the surface. In addition to summarising and categorising these strategies, the report also suggests a novel mechanism that gets beyond the existing drawbacks.

In recent times, prediction error expansion (PEE) based reversible data hiding (RDH) schemes have gained significant traction due to their performance in terms of embedding capacity and image quality. However, the major part of their performance is dependent on how good the prediction has been. For a good prediction, various predictors such as median edge detection (MED), rhombus mean, least square, convolution neural network based predictor (CNNP) have been introduced. In this paper, a review of the working predictors being used in PEE-RDH is presented and discussed. In addition, a new predictor using extreme gradient boosting (XGBoost) is introduced in reversible data hiding. The XGBoost predictor makes use of a machine learning algorithm, where several optimization techniques are combined to get accurate results. To evaluate the performance comprehensively, experimental results considering different test images have been used and analyzed. From the analysis, it has been found that the XGBoost provides better prediction accuracy than some of the existing predictors. However, its performance is not up to the level of some other popular predictors such as least square, CNNP.

This paper proposes a conceptual framework for implementing blockchain technology to enhance traceability, transparency, and authenticity of a 3D printed product. An implementation framework is developed using blockchain technologies to record and trace critical attributes during the various life cycle phases of a 3D printing value chain, viz. raw material extraction, chemical processing, polymerization, filament production, 3D printing, and end-of-life recycling of the product. The information on critical attributes of carbon footprint, workers' age, and material flow during the entire value chain is captured to provide authentic output of carbon footprint and labour age during any of the value chain activity. The uniqueness of the current work lies in offering a series of immutable transactions using blockchain technology to comprehend the circularity of 3D printing material and account for the overall carbon footprint produced by a 3D printed product considering its whole value chain. This would improve the traceability and visibility of the material supply chain for 3D printing. On the hindsight, the proposed framework is expected to assist the manufacturing firms to act as responsible manufacturers by providing the authentic data for the computation of environmental assessment as well as social issues of child labour throughout the value chain.

Health care is the most important factor for the quality of life and IoT has provided us with the capabilities to improve the standards of health care. With the advancement in IoT, the capabilities to incorporate the mobility of patients has seen a great leap. With the prior knowledge of a patient's medical records, the doctor can make quick and efficient decisions. However, current electronic health record systems that are used to manage and store medical records of the patients suffer from issues including heavy bookkeeping, security, data integrity, and privacy. There is also no standard inter-connection to store and retrieve medical records of patients among the majority of the hospitals. In this book chapter, we present a permissioned blockchain-based system that could be used to store the patient records in a secure way i.e., the system will be patient-centric. Also in this system the patient's health can be monitored using IoT sensors and that data will be stored in the cloud and accessed using blockchain only. In this system, the patient will have control over the access to their medical records i.e., this system will be patient-centric which is not the case with current electronic health record systems. Hyperledger Fabric Blockchain was used to implement this project and to store medical records of patients and AWS storage has been used. To receive data from IoT sensors and send that to the cloud, mobile devices were used.

Machine-to-Machine (M2M) communication in the Long Term Evolution (LTE) network has recently grown exponentially as the volume of connected devices has increased rapidly in the last decade. M2M traffic can be understood via certain parameters in terms of packet length, packet generation frequency, delay, and data rate requirements, and it typically flows in the uplink direction. Primarily, the LTE network design is optimized for Human-to-Human (H2H) communication. As a result, designing uplink scheduling in LTE networks is fraught with difficulties which restrict the use of potential capacity. In response to the preceding methodologies, focusing on the QCI priority degrades resource utilization and cell throughput. Therefore, a scheduling mechanism needs to optimize the system performance with priority support to use LTE in M2M communication. This paper highlights existing flaws in the optimization process and proposes a scalable priority-based resource allocation scheme for M2M communication in the LTE/LTE-Advance network. The proposed scheme for resource allocation strikes a balance between resource utilization and application priority support. According to the results, the proposed scheduling algorithm outperforms the standard algorithms concerning resource sharing fairness, average resource utilization, QCI priority support, and delay budget violation.

SummaryThe evolution of Internet of Things (IoT) has led to the development of Industrial Internet of Things (IIoT). IIoT is one the widely applied areas to facilitate people in the manufacturing world. The adoption of IIoT automates sensing, capturing, communicating, and processing in real time. To understand how rapidly IoT and IIoT are growing, this article examines the emergence of 5G‐enabled IIoT, current research trends in IIoT, key milestones achieved in IIoT, and IoT applications specific to 5G‐enabled IIoT. The paper presents the state‐of‐the‐art in networking layered framework of IIoT and comparing relationships of technologies of cloud computing as well as edge computing paradigms. We also explored the type of security attacks and their preventive measures in an IIoT‐driven 5G technology. We have also highlighted the revolution of IIoT‐driven 5G framework which satisfies the demands of IIoT applications.

The 5G network is an emerging field of the research community. 5G is a multi-disciplinary network that aims to support a wide range of services. 5G network has an objective to support a massive number of connected devices. Game theory has an extensive role in wireless network management. Game theory is an approach to analyzing and modeling the system where multiple actors have a role in decision-making with independent objectives and actions. The game theory is an exciting methodology to control the strategic behavior of players and generate an efficient outcome. Coalition game theory can play a crucial role in ensuring cooperation among a massive number of devices. This article provides insight into the current research trends in 5G using coalition games. The work presented in the survey is divided into three categories, namely resource management, interference management, and miscellaneous. This article also provides the foundation about 5G and coalition games highlight the scope of future research.

When designing a wireless sensor network several performance metrics should be considered, e.g., network lifetime, target coverage, sensor energy consumption. As a rule, these metrics are in conflict with each other, which means that by optimizing some of them we worsen the others. Designing the network is therefore a problem of multi-objective optimization. In this work, we propose a bi-objective genetic algorithm that optimizes network lifetime and target coverage. We consider two variants of the algorithm, in which the fitness function comprises only the network lifetime, or where it includes both, the network lifetime and target coverage. This makes it possible to find a trade-off between these two objectives. In-depth experimental studies are carried out for both variants of the algorithm.

M2M communication in the LTE network is gaining attention with a growing number of connected devices and adaptation of new emerging technologies. Usually, the traffic generated by M2M devices is heterogeneous in terms of packet size, intensity, strict delay, and throughput requirements. The M2M traffic generally flows in the uplink direction. It imposes several challenges in designing scheduling for uplink in the LTE network. The research community proposed various solutions regarding the QoS handling in M2M communications. Allocating the resources based on the QoS of machine type communications (MTCs) has a challenge in deciding on devices' priority. This work applies game theory to control devices' strategic behavior claiming a false priority. In this paper, a Quality-of-Service aware uplink packet scheduling scheme is proposed using a combinatorial game. The packet scheduling problem is modeled as an Auction game that handles the scheduling in both Time-Domain and Frequency-Domain. The packet scheduler uses the QoS requirement as an allocation metric, and a power control mechanism is applied to control the devices' strategic behavior. Simulation is performed in MATLAB R2019b. The performance of the proposed schemes is evaluated in terms of the total system utility in QoS satisfaction against standard schedulers like Round Robin and Proportional Fair schedulers and some other schedulers proposed in the literature.

Integrating the internet of things (IoT) in medical applications has significantly improved healthcare operations and patient treatment activities. Real-time patient monitoring and remote diagnostics allow the physician to serve more patients and save human lives using internet of medical things (IoMT) technology. However, IoMT devices are prone to cyber attacks, and security and privacy have been a concern. The IoMT devices operate on low computing and low memory, and implementing security technology on IoMT devices is not feasible. In this article, we propose particle swarm optimization deep neural network (PSO-DNN) for implementing an effective and accurate intrusion detection system in IoMT. Our approach outperforms the state of the art with an accuracy of 96% to detect network intrusions using the combined network traffic and patient’s sensing dataset. We also present an extensive analysis of using various Machine Learning(ML) and Deep Learning (DL) techniques for network intrusion detection in IoMT and confirm that DL models perform slightly better than ML models.