摘要
通过在传感器网络中一些节点附近部署静态辅助充电器(ACs)的方法,能提升网络中从source节点流向sink节点的最大流量.为此,构建了该问题的混合整数线性规划模型(MILP),并证明该问题为NP-hard问题,提出首先使用BottleNeck算法为遗传算法生成初始种群,该算法以路径为单位,采用能量最低的节点优先的原则部署ACs,然后使用改进的自适应的遗传算法(IAGA)模拟自然进化过程,搜索部署ACs的最优位置,使到达sink节点的流量达到最大.仿真实验结果表明,与其他的几种布属ACs的方法相比,IAGA可以有效提高到达sink节点的最大流量.
A method of deploying static auxiliary chargers （ACs） next to somesensor nodes is used to improve the maximum flow from sources to sinks in the network. So, the research formulates a mixed integer linear program （MILP） for the problem and proves that the problem is NP-hard. Firstly, it proposes to use BottleNeck algorithm whichuses path-by unit and deploys ACs using the lowest energy node^first principleto generate initial population for genetic algorithm. Then the Improved Adaptive Genetic Algorithm （IAGA） is used to simulate the natural evolutionary process and search for the optimal location for deployment of ACs to maximize the flow rate to sinks. The simulation results show that IAGA can effectively increase the maximum flow arriving at the sinks compared with some other algorithms of distributing ACs.
作者
李天旭
肖硕
LI Tian-xu;XIAO Shuo(School of Computer Science and Technology,China University of Mining and Technology,Xuzhou 221116,China)
出处
《微电子学与计算机》
CSCD
北大核心
2018年第10期116-120,126,共6页
Microelectronics & Computer
基金
江苏省自然基金项目(BK20150193)
关键词
可充电无线传感器网络
辅助充电器
最大流
遗传算法
rechargeable wireless sensor networks
auxiliary chargers
maximum flow
genetic algorithm
