Most of the future intelligent transportation services will rely on onboard sensing and communication protocols used in modern vehicles for providing uninterrupted services such as lane change, on demand audio-video entertainment, and emergency services to end users. Most of these services generate a huge amount of big data used for analytics to take intelligent decisions. However, keeping in view of the complex decision making and limited resources, the deployment and use of these services has various challenges and constraints including data safety, intelligent decision making, and route planning. Specifically, handling emergency situations for the end users traveling on road can be considered as an interesting problem which requires an efficient solution resilient to the aforementioned constraints and challenges. Motivated from the above, in this paper, we propose a prioritize route selection strategy using Federated learning (FL). The proposed scheme first envisions a futuristic road network scenario in which vehicles rely on an onboard intelligent route movement algorithm for reaching to its destination. By assigning higher priority to vehicles on emergency duties, the proposed scheme provides an uninterrupted route discovery by facilitating them to reach their destination on time. The proposed scheme has been validated using simulations on benchmark data sets traces using various performance evaluation metrics in comparison to the other existing state-of-the-art proposals. Results obtained prove the efficacy of the proposed solution on comparison with other existing schemes in literature.

In recent years, advancements in vehicular communication has improved road safety along with passenger convenience for many applications. However, to take intelligent and timely decisions, a large number of complex operations need to be get executed on large amount of data base repositories which in turn generates a huge burden on the underlying network infrastructure leading to a large amount of energy consumption. Most of the existing solutions reported for the aforementioned problems are based upon the traditional monolithic solutions which may not be applicable in modern scenarios in this environment. Hence, to mitigate the aforementioned challenges and constraints, in this article, we propose BOOST, a connected dominating set (CDS)-aware energy-efficient clustering scheme for Software Defined Network by integrating V2I and V2V communications for reliable and seamless data delivery. The proposed scheme has been specifically designed for urban scenario to achieve effective data delivery with minimum energy consumption. By leveraging the benefits of CDS on roadside communication infrastructure, BOOST is able to adapt with varying traffic conditions to provide seamless scalability with minimum energy and network overheads. The proposed scheme has been evaluated using various performance evaluation metrics in comparison to the existing benchmark schemes. The results obtained demonstrate its superior performance by 3% to 4% in terms of energy-efficiency, network overhead, packet delivery rate, and network throughput in comparison to the existing benchmark schemes.

In smart communities, Electric vehicles (EVs) have grown in popularity as a key component of the energy ecosystem where the focus has turned to the generation of clean, sustainable energy. The integration of EVs, charging stations (CS), and smart grids (SG), however, poses significant challenges in terms of energy trading (ET) optimization and profit maximization. Next, trust is another challenge in the ET ecosystem among the communicating entities (EVs, CS, and SG) to buy and sell energy. Recent studies have overlooked the fact of ET among CS and SG, and mostly have focused on ET by EVs. However, at peak loads, SG may experience bottlenecks in energy dissipation, and thus excess energy collected by CS from EVs might be traded to SG to manage loads during peak times. So, we propose a framework, StackGrid, that leverages the capabilities of Vehicle-to-Grid (V2G) systems over a blockchain network. We design a Stackelberg game between CS and SG for profit maximization of both parties and to obtain optimal payoff equilibria. The framework is powered over the 5G ultra-reliable low latency communications (uRLLC) service for real-time ET response and data exchange. To address blockchain scalability concerns, we incorporate Interplanetary File Systems (IPFS) as local off-chain ledgers, where only meta-information is stored on-chain to handle blockchain scaling issue. The framework is evaluated on metrics like 5G service latency, optimal payoff scenario, attack probability, and node throughput. The obtained results indicate StackGrid viability in real ET setups, with benefits for sustainable and efficient energy management.

The evolution of telecommunications technology is at the cusp of a major transition from 5G to Beyond 5G networks. With this imminent shift, the demand for robust and efficient mitigation solutions has become increasingly vital. AI-based mitigation solutions for solving B5G network problems are directly pushed into the actual field or manually evaluated by the operator first. With Big Data involved in 5G and Beyond, evaluating them manually or without evaluation, pushing them into the real field might have severe consequences in the actual network. There is no intelligent and proactive platform to test the implications of ML models on the networks. In this paper, we propose a pioneering approach that involves the development of an AI-based 5G network clone to serve as a performance evaluation ground for AI-based mitigation solutions tailored for B5G networks. Our methodology outlines the initial phase of evaluating these mitigation solutions within the simulated environment of the AI-based 5G network clone, followed by their subsequent deployment in real-world network infrastructures. This strategy aims to ascertain the efficacy, reliability, and adaptability of the proposed solutions before their integration into the next-generation B5G networks.

Cloud computing is a computing facility which allows its clients to outsource their information on distant cloud based servers without having the pressure of maintaining it. Of several remote data storage issues, data integrity preservation problem has gained much attention of researchers all over the world. Numerous research work has been done by researchers all over the world in the field of data integrity audit in cloud computing. Proposed work is an efficient and stable data integrity verification scheme based on Schnorr signatures. In Schnorr signatures, the signature verification equation is linear and also batch verification of several blocks is possible. Most existing schemes are based on Boneh Lynn and Shacham (BLS) and RSA signature schemes. However, in contrast with existing schemes, the proposed scheme is highly efficient and safe and pays lower verification computation costs proven experimentally in this paper.

Animal poaching poses a significant threat to wild animals, resulting in a rapid decrease in their populations. Unmanned Aerial Vehicles (UAVs) are extensively used to tackle illegal poaching. However, many potential security threats exist concerning transferring a huge amount of data between UAVs and forest officials. To address these challenges and enable secure transmission of big data from the UAVs, a Thermal Vision-based UAV and Blockchain (TVUB) aided poaching prevention system has been proposed in this paper. The TVUB system deploys a UAV swarm equipped with heat-sensing Thermal Infrared Radiation (TIR) sensors that run a Convolutional Neural Network (CNN)-based YOLOv4 image recognition model. The Deep Learning (DL) model is used to detect the presence of poachers based on their thermal images. Furthermore, the system deploys a novel blockchain-CNN mechanism, incorporating a smart contract that initializes the CNN framework through a serialized version of the YOLOv4 model. The execution of the poacher detection model proceeds in a decentralized manner due to the blockchain mechanism, thereby enhancing the security of big data transmitted. Extensive performance evaluation demonstrated the effective working of the TVUB system, which detected poachers with an accuracy of 96.4%.

Fifth-generation (5G) offers more advanced and promising wireless communication technology as Device-to-device (D2D) communication. It refers to the direct data exchange between two users' equipment in a wireless network without routing their data through the base station. The close proximity of the devices offers a higher data rate with low communication latency and increases spectral efficiency. Despite the advantages mentioned above, there are still some challenges, such as interference, power control, and security, that need to be addressed concerning D2D communication. There exist many game theory-based solutions for efficient resource allocation. However, they face issues when there are many users in the communication environment. Hence, we proposed artificial intelligence (AI) and game theory-based solutions for efficient resource allocation in this paper. Initially, we proposed different machine learning (ML) classifiers, such as isolation forest (IF), support vector machine (SVM), gradient boosting (GB) classifier, K-nearest neighbours (KNN), and Gaussian naive Bayes (GNB) that select best D2D users. Then, we formulate a coalition game that gives efficiently allocates resources to the best-selected D2D users. Further, we considered different performance evaluation parameters, such as accuracy, validation loss, sum rate, and convergence rate. The empirical results represent that the GB classifier achieves the highest accuracy, 98.23%, because it trains faster with the large dataset size, and the coalition game-based approach maximizes the overall system sum rate for efficient resource allocation in D2D communication.

Device-to-device communication (D2D-C) is an leading edge technique in 5G and forthcoming 6G networks due benefits for enhanced spectrum efficiency and energy-efficiency (EE). Despite these potential advantages, co-channel interference (CO-CI), cross-channel interference (CR-CI), and massive connectivity are the major issues in D2D-C. In order to handle these issues, an interference mitigation technique for D2D mobile groups (D2Gs) utilizing up-link Non Orthogonal Multiplexing (NOMA) is presented to improve the EE of the overall network. D2Gs boost the SE by sharing the sub-channels (SCs) to cellular users (CUs), and NOMA links a huge number of D2D users (DUs) to D2D transmitters (DT). The problem is formulated as a mixed-integer nonlinear programming (MINLP) problem with associated SCs and power restrictions of the CUs and DUs. A deep reinforcement learning (DRL) based distributed deep deterministic policy gradient (D3PG) approach is considered to enhance the EE by addressing the resource allocation and power control of DUs. Numerical outcomes showed that the suggested scheme overcomes state-of-the-art techniques in terms of results.

The rapid adoption of Internet of Things (IoT) devices in healthcare has introduced new challenges in preserving data privacy, security and patient safety. Traditional approaches need to ensure security and privacy while maintaining computational efficiency, particularly for resource-constrained IoT devices. This paper proposes a novel hybrid approach by combining federated learning and blockchain technology to provide a secured and privacy-preserved solution for IoT-enabled healthcare applications. Our approach leverages a public-key cryptosystem that provides semantic security for local model updates, while blockchain technology ensures the integrity of these updates and enforces access control and accountability. The federated learning process enables a secure model aggregation without sharing sensitive patient data. We implement and evaluate our proposed framework using EMNIST datasets, demonstrating its effectiveness in preserving data privacy and security while maintaining computational efficiency. The results suggest that our hybrid approach can significantly enhance the development of secure and privacy-preserved IoT-enabled healthcare applications, offering a promising direction for future research in this field.

We present a moving mobile edge computing architecture in which unmanned aerial vehicles (UAV) serve as an equipment, providing computational power and allowing task offloading from mobile devices (MD). By improving user association, resource allocation, and UAV trajectory, we optimizing the energy consumption of all MDs. Towards that purpose, we provide a Trajectory optimization technique for making real-time choices while considering all the situation of the environment, followed by a DRL-based Trajectory control approach (RLCT). The RLCT approach may be adapted to any UAV takeoff point and can find the solution faster. The FL is introduced to address the Optimization problem in a Semi-distributed DRL technique to deal with UAV trajectory constraints. The proposed FRL approach enables devices to rapidly train the models locally while communicating with a local server to construct a network globally. The simulation results in the result section shows that the proposed technique RLCT and FRL in the paper outperforms the existing methods” while the FRL performs best among all.

Unmanned aerial vehicles (UAVs), which can help with high-speed communications and provide better coverage, are an important component of next-generation wireless networks. Because of its high mobility and aerial nature, it is suitable for a wide range of mobile wireless communications-based applications. However, low data rates with limited transmission power constitute a significant difficulty in wireless communication that lowers network performance. To overcome this issue, integrating a UAV with a relay device capable of delivering high data speeds while utilising minimum transmission power is a promising approach. In this research, we presented an edge-cutting framework called UAV-IRS, in which an Intelligent reflective surface (IRS) supports unmanned aerial vehicles (UAVs) that traverse areas with low signal strength. Furthermore, we discussed the applications, challenges and research directions of UAV-IRS in vehicle-to-everything (V2X) communication. We considered a case study of UAV-IRS in V2X communication. The performance evaluation demonstrates how the viable data rate and minimum transmission power decrease with distance as the number of IRS elements increases.

Vehicular networking systems rely on Internet Protocol to exchange information among vehicles. With the increasing number of vehicles, the communication overhead has increased significantly a nd h as b ecome m ore c ontent centric. To resolve this problem, the Named data networking-based communication model has been used. This communication is completely based upon the content rather than the location and provides better network coverage comparatively. The vehicles used for communication purposes in a network are moving in some specific p atterns, b ased o n h aving t he s ame destination, with the same speed parameters etc. These vehicles which have common interests form a platoon. This vehicular platoon helps in various fields such as safe driving, energy efficiency and road safety. This paper provides a scheme for the applicability of NDN to the vehicular platoon. Special design features are proposed for communication purposes in V-NDN-based vehicular platoons. The backbone platoon network is used for data dissemination between the vehicles on the highway. To check the efficiency of the proposed scheme, extensive simulations have been performed on the ndnSim simulator. More precisely, different scenarios have been used and analyzed their efficiency i n t erms o f d elay and throughput.