摘要
随着无线传感器网络的规模不断扩大,开放信道中的通信干扰与节点能源有限问题严重制约传感器网络性能。研究在星型拓扑结构下可充电无线传感器网络的多用户介入通信干扰管理技术,旨在提高系统吞吐量、频谱和功率利用率,延长网络生命周期。首先针对多用户介入的通信干扰管理技术提出一种启发式的功率分类算法实现并发通信传感器节点的分簇,并对其分类效果进行验证。其次,结合并发无线通信中节点行为特征,引入无线能量补给设备,构建以最大化能量补给设备驻站时间比为目标的跨层优化问题,并将其转化为具有等优性的线性规划问题,进而求解得到无线传感器节点和无线能量补给设备的优化工作策略。仿真和实验证明,在维持传感器网络生命周期的前提下,采用并发通信的干扰管理策略和无线能量补给后,网络的通信能力和功率利用率都得到提升,该仿真场景下充电小车驻站时间占比ηs能达到47%。
As the scale of the wireless sensor network expanding constantly,the sensor node’s battery capacity and communication interference inopen channel has seriously restricted its performance. The communication interference management technology of multiuser intervention for aiming to prolong the network life cycle and improve the spectrum,and power utilizationin rechargeable wireless sensor network of star topology is studied in this paper. Firstly,a heuristic power clustering algorithm in concurrent communication is proposed,then its classification effect is analyzed. Secondly,by introducing wireless energy supply equipment,the ratio of sojourn time in station to recharging circle is taken as the optimization goal to construct optimization problem combined with the feature behavior of nodes in concurrent wireless communication. Then,through converting the optimization problem into an optimal linear programming problem,and in turn,the optimization working strategy of wireless sensor nodes and wireless power supply equipment is obtained.Finally,the simulation shows that the ratio of station time of wireless charge equipment( WCE) with recharging cycle can achieve 47%in the simulation sceneafter integrating the interference management strategy of concurrent communication with wireless energy supply on the premise of maintaining the network life cycle.
出处
《电子测量与仪器学报》
CSCD
北大核心
2018年第5期59-67,共9页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金(61701162,61370088)资助项目
关键词
可充电无线传感器网络
通信干扰管理
单基站单跳
无线能量补给设备
跨层优化
rechargeable wireless sensor network
communication interference management
single base singlehop topology
wireless charge equipment (WCE)
cross layer optimization
