Space-air-ground integrated vehicular network (SAGVN) expands the communication range and satisfy people’s needs for long-distance communication. However, it is difficult to realize low-delay and low-energy task processing by solely relying on the computational resources of terrestrial networks, and limited spectrum resources further limits SAGVN development. To address these problems, an SAGVN task offloading and resource allocation (STOR) algorithm is proposed. First, an SAGVN computing offloading system is designed, which enables vehicle user terminals to offload tasks to the peripheral vehicles or mobile edge computing, unmanned aerial vehicle, and low-orbit satellite server for processing and efficiently use spectrum resources through channel multiplexing. Second, an optimization problem with the objective of minimizing the average delay and energy consumed of all vehicles is established, and the optimization problem is transformed into offloading and computing resource allocation subproblems. Finally, the tanh-genetic and Sine mapping particle swarm optimization (PSO) algorithms are employed to solve subproblems, which satisfy the requirements of vehicles. Simulation results show that the STOR algorithm can reduce the total task processing delay by 7.13%, 3.04%, and 21.08%, and the total task energy consumed by 3.87%, 55.21%, and 15.10%, respectively, compared with the random channel allocation, average resource allocation, and simulated annealing-PSO algorithms.

Space-Air-Ground integrated Vehicular Network (SAGVN) aims to achieve ubiquitous connectivity and provide abundant computational resources to enhance the performance and efficiency of the vehicular networks. Nonetheless, there are still challenges to overcome, including the scheduling of multilayered computational resources and the scarcity of spectrum resources. To address these problems, we propose a joint Task Offloading (TO) and Resource Allocation (RA) strategy in SAGVN (namely JTRSS). This strategy establishes an SAGVN model that incorporates air and space networks to expand the options for vehicular TO, and enhances the edge-computing resources of the system by deploying edge servers. To minimize the system average cost, we use the JTRSS algorithm to decompose the original problem into a number of subproblems. A maximum rate matching algorithm is used to address the channel allocation and the Lagrangian multiplier method is employed for computational RA. To acquire the optimal TO decision, a differential fusion cuckoo search algorithm is designed. Extensive simulation results demonstrate the significant superiority of the JTRSS algorithm in optimizing the system average cost.