【摘 要】
：
In recent years,artificial intelligence and automotive industry have developed rapidly,and au-tonomous driving has gradually become the focus of the industry.In road networks,the problem of prox-imity detection refers to detecting whether two mov-ing obje
【机 构】
：
School of Information and Communication Engineering,Beijing University of Posts and Telecommunicatio
论文部分内容阅读
In recent years,artificial intelligence and automotive industry have developed rapidly,and au-tonomous driving has gradually become the focus of the industry.In road networks,the problem of prox-imity detection refers to detecting whether two mov-ing objects are close to each other or not in real time.However,the battery life and computing capa-bility of mobile devices are limited in the actual scene,which results in high latency and energy consump-tion.Therefore,it is a tough problem to determine the proximity relationship between mobile users with low latency and energy consumption.In this article,we aim at finding a tradeoff between latency and energy consumption.We formalize the computation offload-ing problem base on mobile edge computing(MEC)into a constrained multiobjective optimization prob-lem(CMOP)and utilize NSGA-Ⅱ to solve it.The simulation results demonstrate that NSGA-Ⅱ can find the Pareto set,which reduces the latency and energy consumption effectively.In addition,a large number of solutions provided by the Pareto set give us more choices of the offloading decision according to the ac-tual situation.
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