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The use of aerial base stations, AI cloud, and satellite storage can help manage location, traffic, and specific application-based services for vehicular social networks. However, sharing of such data makes the vehicular network vulnerable to data and privacy leakage. In this regard, this article proposes an efficient and secure data sharing scheme using community segmentation and a blockchain-based framework for vehicular social networks. The proposed work considers similarity matrices that employ the dynamics of structural similarity, modularity matrix, and data compatibility. These similarity matrices are then passed through stacked autoencoders that are trained to extract encoded embedding. A density-based clustering approach is then employed to find the community segments from the information distances between the encoded embeddings. A blockchain network based on the Hyperledger Fabric platform is also adopted to ensure data sharing security. Extensive experiments have been carried out to evaluate the proposed data-sharing framework in terms of the sum of squared error, sharing degree, time cost, computational complexity, throughput, and CPU utilization for proving its efficacy and applicability. The results show that the CSB framework achieves a higher degree of SD, lower computational complexity, and higher throughput. [ABSTRACT FROM AUTHOR]

Fog computing is one of the major components of future 6G networks. It can provide fast computing of different application-related tasks and improve system reliability due to better decision-making. Parallel offloading, in which a task is split into several sub-tasks and transmitted to different fog nodes for parallel computation, is a promising concept in task offloading. Parallel offloading suffers from challenges such as sub-task splitting and mapping of sub-tasks to the fog nodes. In this paper, we propose a novel many-to-one matching-based algorithm for the allocation of sub-tasks to fog nodes. We develop preference profiles for IoT nodes and fog nodes to reduce the task computation delay. We also propose a technique to address the externalities problem in the matching algorithm that is caused by the dynamic preference profiles. Furthermore, a detailed evaluation of the proposed technique is presented to show the benefits of each feature of the algorithm. Simulation results show that the proposed matching-based offloading technique outperforms other available techniques from the literature and improves task latency by 52% at high task loads. [ABSTRACT FROM AUTHOR]

To support the massive connectivity in Internet of Things (IoT), several promising techniques like cognitive radio (CR) and non-orthogonal multiple access (NOMA) enables the user to share spectrum resources. This work aims to achieve fairness among secondary users (SUs) in IoT cooperative NOMA-based CR transmission. We design a power allocation algorithm, an independent battery constraint at each node is considered, and power gap among transmissions of two NOMA users is applied for successive interference cancellation. The simulation results show that the proposed framework provides excellent performance and for sufficient available transmission power perfect fairness is achieved. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is a rapidly emerging paradigm with the capability to improve the spectral efficiency of data-driven, intelligence inspired, and highly digitized sixth-generation (6G) wireless networks. In the backdrop of ever-evolving NOMA techniques, this article presents a novel resource optimization framework for maximizing the spectral efficiency (SE) of the Internet-of-things (IoT) networks using power domain NOMA. The proposed framework considers a limited number of frequency blocks in the IoT network and provides an optimal power and frequency block allocation method. Different practical constraints like successive interference cancellation (SIC) complexity, ensuring the minimum gap of received power among different IoT equipment over the same frequency block for successful SIC operation, quality of services requirements, and IoT equipment's transmit powers have also been taken into account. Accordingly, a non-convex optimization problem has been formulated for resource management where the objective of spectral efficiency is coupled by both frequency block and power allocation. To effectively solve this problem, the resource optimization problem is decoupled into two subproblems for frequency block assignment and power allocation. A suboptimal algorithm has been designed for frequency block assignment and a new optimal sequential quadratic programming (SQP) approach is employed to solve the non-convex power control subproblem. For the sake of fair comparison, a low complexity suboptimal NOMA power allocation scheme, based on Karush-Kuhn-Tucker (KKT) conditions, and conventional orthogonal multiple access (OMA) scheme are also provided. The results demonstrate that the proposed optimal resource management scheme significantly improves the system performance compared to other schemes. [ABSTRACT FROM AUTHOR]

Wireless power transfer (WPT) has emerged as a promising solution for delivering services to low-power Internet of Things (IoT) devices in a demand-driven manner. In this work, we consider the Wireless power-enabled Hybrid Mobile Edge Cloud (WPHMEC) network, which utilizes a dynamic offloading strategy (partial and binary) to maximize computational efficiency while minimizing device energy consumption. To address this challenge, we formulate a convex optimization problem to maximize the number of computational bits and minimize the energy consumption of the IIoT devices in the WPT-based MEC system for Industry 5.0 applications. To achieve this goal, we employ a reformulation approach based on block coordinate descent (BCD) to formulate an optimization problem that addresses the nonconvexity of the problem and propose a solution approach using the Karush–Kuhn–Tucker (KKT) conditions. To validate the effectiveness of the proposed scheme, extensive simulations are carried out using the Matlab software to evaluate the system's performance and components. The results demonstrate that optimal resource allocation maximizes energy efficiency and enhances computational resource utilization, making WPHMEEC an ideal choice for addressing the evolving demands of Industry 5.0 applications. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are one of the fundamental infrastructures for Internet of Things (IoTs) technology. Efficient energy consumption is one of the greatest challenges in WSNs because of its resource-constrained sensor nodes (SNs). Clustering techniques can significantly help resolve this issue and extend the network's lifespan. In clustering, WSN is divided into various clusters, and a cluster head (CH) is selected in each cluster. The selection of appropriate CHs highly influences the clustering technique, and poor cluster structures lead toward the early death of WSNs. In this paper, we propose an energy-efficient clustering and cluster head selection technique for next-generation wireless sensor networks (NG-WSNs). The proposed clustering approach is based on the midpoint technique, considering residual energy and distance among nodes. It distributes the sensors uniformly creating balanced clusters, and uses multihop communication for distant CHs to the base station (BS). We consider a four-layer hierarchical network composed of SNs, CHs, unmanned aerial vehicle (UAV), and BS. The UAV brings the advantage of flexibility and mobility; it shortens the communication range of sensors, which leads to an extended lifetime. Finally, a simulated annealing algorithm is applied for the optimal trajectory of the UAV according to the ground sensor network. The experimental results show that the proposed approach outperforms with respect to energy efficiency and network lifetime when compared with state-of-the-art techniques from recent literature. [ABSTRACT FROM AUTHOR]

Due to unrivaled effectiveness, non-orthogonal multiple access (NOMA) has risen as a promising multiple access scheme for the Internet of things (IoT). In this paper, we provide a new power allocation technique for improving the energy and spectral efficiency of NOMA-enabled IoT devices. The power allocation is performed without compromising the quality of service (QoS) requirements of the network. By considering the transmit power, QoS and successive interference cancellation (SIC) constraints, we use the sequential quadratic programming (SQP) technique to solve the non-convex problem. To assess the performance of our scheme, we compare the proposed SQP-based approach with the conventional KKT-based optimization method. We provide Monte Carlo simulation results to assess our proposed power allocation framework and illustrate the performance improvements against orthogonal multiple access (OMA) scheme. The results uncover that the proposed SQP-based power optimization design substantially improves the performance of the NOMA-enabled IoT network. [ABSTRACT FROM AUTHOR]