摘要
针对移动终端(mobile terminal, MT)从环境射频源收集能量较少的问题,研究基于混合能量收集的移动边缘计算系统资源分配策略。通过在基站覆盖区域内部署多个磁感应能量快速充电站,当MT从环境射频源收集的能量即将耗尽时,在附近的磁感应能量快速充电站补充能量。MT通过移动边缘计算将计算任务分流到边缘服务器。将资源分配问题建模为优化问题,以最小化MTs总能量消耗为目标,同时满足MT最大计算能力、边缘服务器最大计算资源、任务计算总时延和MT电池能量的约束条件。通过引入量子行为粒子群优化算法,获得次优解。仿真结果表明,与标准粒子群优化算法和相等分配边缘服务器计算资源的方法相比,量子行为粒子群优化算法具有更少的能量消耗。
Aiming at the problem that mobile terminal(MT)harvests less energy from ambient radio frequency(RF)sources,the resource allocation strategy of mobile edge computing system based on hybrid energy harvesting is investigated.By deploying multiple magnetic induction energy quick charging stations within the coverage area of the base station,the MT will supplement energy at nearby magnetic induction energy quick charging stations when the MT is about to run out of the energy harvested from ambient RF sources.The MT offloads computing tasks to edge servers by mobile edge computing.The resource allocation problem is formulated as an optimization problem.The objective is to minimize the total energy consumption of MTs under the constraints of maximal computing capability of MT,maximal computing resource of edge server,computing delay of tasks,and battery energy of MT.The suboptimal solution is obtained by using the quantum-behaved particle swarm optimization algorithm.Simulation results show that the quantum-behaved particle swarm optimization algorithm has less energy consumption compared with the standard particle swarm optimization algorithm and the equal allocation method of computing resources of edge servers.
作者
陈加法
赵宜升
高锦程
陈忠辉
CHEN Jiafa;ZHAO Yisheng;GAO Jincheng;CHEN Zhonghui(Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information,College of Physics and Information Engineering,Fuzhou LDiversity,Fuzhou 350116,P.R.China)
出处
《重庆邮电大学学报（自然科学版）》
CSCD
北大核心
2021年第2期193-201,共9页
Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition)
基金
国家自然科学基金(61871133,61971139,61571129)
福建省自然科学基金(2018J01805)。
关键词
混合能量收集
移动边缘计算
资源分配
hybrid energy harvesting
mobile edge computing
resource allocation
