中国矿业大学 计算机科学与技术学院,江苏,徐州,221116
纸质出版:2018
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李天旭, 肖硕. 可充电无线传感器网络中的最大流算法研究[J]. 微电子学与计算机, 2018, 35(10): 116-120, 126.
LI Tian-xu, XIAO Shuo. Research on Maximum flow Algorithm in Rechargeable Wireless Sensor Networks[J]. 2018, 35(10): 116-120.
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.
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