Communication Resource Allocation Strategy of Internet of Vehicles Based on MEC
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The business of Internet of Vehicles (IoV) is growing rapidly, and the large amount of data exchange has causedproblems of large mobile network communication delay and large energy loss. A strategy for resourceallocation of IoV communication based on mobile edge computing (MEC) is thus proposed. First, a model ofthe cloud-side collaborative cache and resource allocation system for the IoV is designed. Vehicles can offloadtasks to MEC servers or neighboring vehicles for communication. Then, the communication model and thecalculation model of IoV system are comprehensively analyzed. The optimization objective of minimizingdelay and energy consumption is constructed. Finally, the on-board computing task is coded, and theoptimization problem is transformed into a knapsack problem. The optimal resource allocation strategy isobtained through genetic algorithm. The simulation results based on the MATLAB platform show that: Theproposed strategy offloads tasks to the MEC server or neighboring vehicles, making full use of systemresources. In different situations, the energy consumption does not exceed 300 J and 180 J, with an averagedelay of 210 ms, effectively reducing system overhead and improving response speed.
Abstract
The business of Internet of Vehicles (IoV) is growing rapidly, and the large amount of data exchange has causedproblems of large mobile network communication delay and large energy loss. A strategy for resourceallocation of IoV communication based on mobile edge computing (MEC) is thus proposed. First, a model ofthe cloud-side collaborative cache and resource allocation system for the IoV is designed. Vehicles can offloadtasks to MEC servers or neighboring vehicles for communication. Then, the communication model and thecalculation model of IoV system are comprehensively analyzed. The optimization objective of minimizingdelay and energy consumption is constructed. Finally, the on-board computing task is coded, and theoptimization problem is transformed into a knapsack problem. The optimal resource allocation strategy isobtained through genetic algorithm. The simulation results based on the MATLAB platform show that: Theproposed strategy offloads tasks to the MEC server or neighboring vehicles, making full use of systemresources. In different situations, the energy consumption does not exceed 300 J and 180 J, with an averagedelay of 210 ms, effectively reducing system overhead and improving response speed.
참고문헌 (18)
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