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EHWSNs中面向吞吐量和能耗优化的时隙分配算法研究    

Research on time slots allocation algorithm for optimization of throughput and energy consumption in EHWSNs

文献类型:期刊文献

中文题名:EHWSNs中面向吞吐量和能耗优化的时隙分配算法研究

英文题名:Research on time slots allocation algorithm for optimization of throughput and energy consumption in EHWSNs

作者:李怀强[1];衣强[2];张岩[2]

第一作者:李怀强

机构:[1]河南财经政法大学现代教育技术中心,郑州450046;[2]河南省水土保持监督监测总站,郑州450002

第一机构:河南财经政法大学

年份:2020

卷号:32

期号:1

起止页码:121-128

中文期刊名:重庆邮电大学学报:自然科学版

收录:CSTPCD;;北大核心:【北大核心2017】;CSCD:【CSCD_E2019_2020】;

基金:河南省水利科技攻关计划(GG201663)~~

语种:中文

中文关键词:能量收集无线传感器网络;传输周期;混合整数线性规划;时隙;吞吐量

外文关键词:energy harvesting wireless sensor network;transmission cycle;mixed integer linear programming;timeslot;throughput

摘要:针对在具有移动汇聚结点(Sink)的能量收集无线传感器网络中,如何在数据收集时提升网络吞吐量和降低能耗的问题,分析了Sink移动距离与节点数据传输的时间周期之间的关系,将面向吞吐量和能耗优化的数据收集问题建模为基于混合整数线性规划的优化问题,并提出了一种基于有效传输周期的时隙分配算法来对其进行求解。算法主要分2个阶段进行:移动Sink在每个时间周期内识别出可进行数据传输的邻居节点,并为其分配时隙;移动Sink根据数据可用性对节点进行排序,并最终决定哪些节点在各个时隙期间发送数据。理论分析和仿真实验结果表明,所提算法在吞吐量和能耗方面的性能优于当前典型算法,且计算复杂度更低。
The problem of how to improve network throughput and reduce energy consumption in data collection is studied in the energy harvesting wireless sensor network with mobile Sink.The relationship between the distance of Sink movement and the time period of node data transmission is analyzed in this paper,the data collection problem for optimization of throughput and energy consumption is modeled as an optimization problem based on the mixed integer linear programming,and a time slot allocation algorithm based on effective transmission period is proposed to solve it.The algorithm is mainly divided into two stages.Firstly,mobile Sink identifies neighbor nodes that can carry out data transmission in each time period and assigns time slot to them.Then,mobile Sink sorts nodes according to data availability and finally decides which nodes send data during each slot.Theoretical analysis and simulation results show that the proposed algorithm outperforms the current typical algorithm in terms of throughput and energy consumption,and has lower computational complexity.

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