无线充电设备能量受限的WRSNs周期性充电规划

发布时间：2018-01-06 13:34

本文关键词：无线充电设备能量受限的WRSNs周期性充电规划　出处：《电子测量与仪器学报》2017年07期 　论文类型：期刊论文

【摘要】：研究无线充电设备(wireless charging equipment,WCE)的充电能量和行驶能量均有限的情况下使用WCE为传感器节点周期性充电问题。对于每一个充电周期,旨在最大化充电周期时间的同时最小化WCE充电和行驶能量的总消耗值,以最小的WCE能量消耗功率保证无线可充电传感器网络(wireless recharging sensors networks,WRSNs)永久性工作下去。通过分析传感器节点能量约束和WCE行驶及充电约束,建立以最小化WCE能量消耗功率为优化目标的优化模型。充电问题为NP-Complete问题,使用混沌粒子群算法(chaos particle swarm optimization,CPSO)求解优化问题得到WCE充电路径和节点充电时间,并设计了由2种数据路由和3种节点分布类型组合成的6种WRSNs仿真场景,与基本遗传算法(genetic algorithm,GA)对比,其收敛速度至少提升了1倍。
[Abstract]:Wireless charging equipment is studied. WCE is used to periodically charge sensor nodes under the condition of limited charge energy and driving energy. For each charging cycle. The aim is to maximize the charging cycle time while minimizing the total consumption of WCE charging and driving energy. Wireless recharging sensors networks is guaranteed with minimum WCE power consumption. Through the analysis of sensor node energy constraints and WCE driving and charging constraints. An optimization model with the objective of minimizing the power consumption of WCE is established. The charging problem is a NP-Complete problem. Chaos particle swarm optimization is used in this paper. The WCE charging path and node charging time are obtained by solving the optimization problem, and six WRSNs simulation scenarios are designed, which are composed of two data routes and three node distribution types. Compared with basic genetic algorithm (GA), its convergence rate is at least doubled.
【作者单位】：合肥工业大学计算机与信息学院;安全关键工业测控技术教育部工程研究中心;
【基金】：国家自然科学基金(61370088) 国家国际科技合作专项项目(2014DFB10060)资助项目
【分类号】：TN929.5;TP212.9
【正文快照】： 1引言无线传感器网络(wireless sensors networks,WSN)作为信息获取的重要手段之一,受到广泛关注,已有学者提出了WSN检测目标的定位算法[1]、WSN野外监测系统通信协议的研究[2]、WSN中的数据路由策略[3-4]及MAC层协议设计[5]等研究成果。WSN的能量问题一直是限制其广泛应用的

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