The Internet of vehicles currently faces the challenges of spectrum interference in multimode, multiservice communications, and low efficiency in switched-vehicle selection for vehicular cooperative communication. To overcome these, a user-centered cooperative-communication strategy (UCCS) is proposed. A system model of the UCCS algorithm is established for three situations according to the vehicular cooperative-communication process. When there are service roadside units (SRSUs), cooperative RSUs (CRSUs), and cooperative vehicles (CVs) in the service vehicle (SV) communication range, a two-layer game method is proposed, which dynamically adjusts the transmission power, punishment factor, and other parameters of service and cooperative vehicle. When there are only CVs in the SV communication range, a multiparameter voting method is proposed, which selects the best cooperative-communication vehicle by calculating the normalized linear weighted value of CVs. When there are only CRSUs and CVs in the SV communication range, a station-vehicle voting method is proposed, which calculates the cooperative-communication value of CRSUs to select the optimal one, thus comparing it with the best CV to determine the optimal cooperative-communication object. The simulation results show that the average throughput of the UCCS algorithm is increased by 17% and 42% as compared to those based on the repeated game and network utility maximization algorithms, respectively, and its average waiting time decreased by 43% and 24% compared to those of the random selection and shortest path selection algorithms, respectively. The overall system revenue and communication performance of the proposed algorithm are significantly improved.

Limited edge server resources and uneven distribution of traffic density in vehicular networks result in problems such as unbalanced network load and high task processing latency. To address these issues, we proposed an efficient caching and offloading resource allocation (ECORA) strategy in vehicular social networks. First, to improve the utilization of vehicular idle resources, a collaborative computation and storage resource allocation mechanism was designed using mobile social similarity. Next, with the optimization objective of minimizing the average task processing delay, we studied the combined resource allocation optimization problem and decoupled it into two sub-problems. For the service caching subproblem, we designed a stable matching algorithm by mobile social connections to dynamically update the cache resource allocation scheme for improving the task unloading efficiency. For the task offloading subproblem, a discrete cuckoo search algorithm based on differential evolution was designed to adaptively select the best task offloading scheme, which minimized the average task processing delay. Simulation results revealed that the ECORA strategy outperformed the resource allocation strategy based on particle swarm optimization and genetic algorithm, and reduced the average task processing delay by at least 7.59%. Meanwhile, the ECORA strategy can achieve superior network load balancing.

In industrial wireless networks, the decentralized redundant design for sensing, communication, and control, as well as the frequent interaction among massive intelligent machines, result in the challenges such as high communication overhead and low production capacity. To address these problems, we propose a closed-loop control scheme based on integrated sensing, communication and control (ISCC). Firstly, to accurately model the closed-loop control process of automated guided vehicle (AGV) in automobile assembly workshops, we establish a novel ISCC system. Then, to reduce communication overhead while ensuring the system stability, we propose a networked multi-node control method with coordinated active-passive control. Through sensing workshop environment, AGVs flexibly perform different control instructions between active local decision-making and passive reception of base station (BS) decision-making. AGVs can respond quickly to the environment and achieve high-speed movement without the requirement for frequent communication with the BS. Furthermore, we design a novel communication scheme with adaptive closed-loop interaction cycle, by deriving stability conditions for the multi-node control method and upper bound for the closed-loop interaction cycle. Through dynamically adjusting the AGVs closed-loop interaction cycle based on real-time closed-loop control status, it significantly reduces the frequency of AGV accessing BS and communication overhead, extending the controllable AGV quantity, and enhancing production capacity. Simulation results reveal that the proposed scheme improved production capacity by a minimum of 62.6% compared to conventional schemes, significantly improving communication and control performance.

Owing to the limited spectrum of vehicular social networks and the fragmented distribution of caching resources, problems such as low vehicle data transmission rate, poor service quality, and low service content delivery efficiency of streaming media are encountered. To solve these problems, we propose a resource allocation strategy based on tripartite graph in vehicular social networks (RATG). This strategy establishes a mobile vehicular social network model based on vehicle mobility and social similarity. To maximize the content delivery efficiency and transmission rate, one-to-one stable matching based on mobile social connections and one-to-one stable matching based on channel state are established. The proposed strategy incorporates a heuristic optimization algorithm based on the maximum spanning tree, such that the matching results of the two tripartite graphs can reach the approximate global optimal simultaneously and further improve the service quality of vehicles. The simulation results show that the content delivery efficiency of the RATG strategy is 6.05%, 5.00%, and 10.64% higher than those of the randomized resources allocation strategy, local search strategy, and local ratio strategy, respectively, and the average transmission rate is at least 2.7% higher for the RATG strategy, relative to those of the other strategies.

This study proposes a task-offloading and resource allocation strategy in multidomain cooperation (TARMC) for the industrial Internet of Things (IIoT) to resolve the problem of the non-uniform distribution of task computation among various cluster domain networks in the IIoT and the solidification of traditional industrial wireless network architecture, which produces low efficiency of static resource allocation and high delay in closed-loop data processing. Based on the closed-loop process of task interaction of intelligent terminals in wireless networks, the proposed strategy constructs a network model of multidomain collaborative task-offloading and resource allocation in IIoT for flexible and dynamic resource allocation among intelligent terminals, edge servers, and cluster networks. Considering the partial offloading mechanism, various tasks were segmented into multiple subtasks marked at bit-level per demand, which enabled local and edge servers to process all subtasks in parallel. Moreover, this study established a utility function for the closed-loop delay and terminal energy consumption of task processing, which transformed the process of multidomain collaborative task-offloading and resource allocation into the problem of task computing revenue. Furthermore, an improved Cuckoo Search algorithm was developed to derive the optimal offloading position and resource allocation decision through an alternating iterative method. The simulation results revealed that TARMC performed better than strategies.

To solve the problems of unbalanced network loads, poor network throughput, and low user transmission rate in 5th generation (5G) vehicular networks, a user‐centric data communication service (UCDCS) strategy is proposed for 5G vehicular networks. First, the UCDCS model is established. The access roadside unit group (ARSUG) is updated in real‐time according to predictions of vehicle mobility. Then, a data communication service resource allocation algorithm based on game theory that considers the network load cost, throughput cost, and vehicle benefit is developed. Based on the results of the algorithm, roadside unit (RSU) selection based on entropy weight is performed and the best RSU for data communication is selected according to the different service preferences of each vehicle. This improves the transmission rate for users and realizes network load balance to ensure the quality of service for users. The simulation results show that the transmission rate of the UCDCS strategy is 35.16%, 23.46%, and 47.74% higher than those of the traditional handover management (THOM), improved mobility management (IMM), and network‐centric network selection strategies (NCNS), respectively; correspondingly, the link reliability is at least 0.07% higher, and the network delay is at least 5.96% smaller for the UCDCS strategy.