摘要
高效充电调度问题一直是无线可充电传感器网络研究的关键之一。针对该问题,提出一种基于充电路径预估模型的遗传算法。该算法能够有效地完成充电任务从而使得传感器及时得到充电服务。算法包含了充电路径预估模型、染色体结构、选择、交配以及变异操作这几部分的设计。仿真结果表明,与EDF、REDF和NJNP相比,该算法有效地提高了网络的性能。
This paper studies the problem of obtaining efficient charging scheduling in wireless rechargeable sensor networks combining genetic algorithm with charging path estimation model.The algorithm can complete the charging task effectively so that the sensor can be charged in time without depleting energy,and it includes the design of charging path estimation model and chromosome structure,selection,crossover and mutation operation.The simulation results show that the proposed algorithm,compred with EDF,REDF and NJNP,has better metrics and greatly improves the performance of the network.
作者
陈晶晶
陈虹微
卢振良
黄锦鹏
许珂珺
CHEN Jingjing;CHEN Hongwei;LU Zhenliang;HUANG Jinpeng;XU Kejun(Longyan University,Longyan,Fujian 364000,China)
出处
《龙岩学院学报》
2020年第5期42-48,共7页
Journal of Longyan University
基金
福建省自然基金面上项目(2019J01795)
龙岩学院产学研合作项目(LC2015004)。
关键词
无线可充电传感器网络
充电调度
预估模型
遗传算法
wireless rechargeable sensor network
charging scheduling
estimation model
genetic algorithm
