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This paper proposed a mobile edge computing offloading strategy based on multi-hop transmission of network connected vehicles. Through the prediction of future vehicle trajectory, it found effectively the best real-time multi-hop transmission path of vehicle network, so as to ensure that the computing task could be successfully unloaded to MEC server within the time delay requirement. The simulation results show that compared with the traditional mobile offloading strategy, the average task delay is lower, the task success rate is higher, and all aspects of performance are better than the traditional edge computing offloading strategy. Among them, the task unloading success rate increases by 10. 06% on average, and the task delay decreases by 8. 62% on average. [ABSTRACT FROM AUTHOR]

Aiming at computer resources was limited and the total overhead of the system processing tasks was too high, this paper proposed an offloading strategy based on dynamic perception-hybrid artificial fish swarm（DP-HAFS） algorithm. First, it constructed a "local-edge-cloud" three-layer network architecture and adopted a fine-grained partial offloading mode; then, for the scenario where a terminal device covered by multiple base stations, this paper proposed a base station selection strategy to select the optimal base station; finally, it used the DP-HAFS algorithm to dynamically modify the unloading decision and got the best uninstall strategy. The simulation results show that the proposed offloading strategy can effectively reduce the total system overhead in a scenario with a large number of tasks. [ABSTRACT FROM AUTHOR]

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