一种射频能量捕获网络移动能量源均衡化充电策略
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摘要
为解决传统电池供电传感器网络存在的电池不易更换、节点能量容易耗尽等问题,射频能量捕获技术已逐步应用于无线可充电传感器网络中.由于不同位置传感器节点的工作负荷不同,捕获能量也有差异,实现节点能量的均衡化分布可以有效地提高节点的存活率.考虑射频能量源移动充电的场景,在已知节点位置信息的条件下,设计合理均衡的路由方案和充电算法.首先将区域基于蜂窝六边形网格划分,分别对网格和节点分层,提出逐层传输的均衡式路由策略,然后给出无线充电小车的移动路径,对相邻两层内节点剩余能量的方差最小化问题建模,由内层向外层依次确定能量源在各停留点的充电时间.仿真结果表明,相比已有的均衡化充电方法,该策略可以明显提高节点剩余能量的均衡性,从而延长网络的生命周期.
In order to solve the problems existing in the traditional battery-powered sensor network that the battery is not available to replace and the energy of nodes is easy to be exhausted,RF energy harvesting technology has been applied to the wireless rechargeable sensor network. Since the workload and harvested energy of sensor nodes in different positions are different,balanced distribution of nodes' energy can effectively improve the survival rate of nodes. Considering the scenario where mobile RF energy transmitter is employed for charging,we design a reasonable balanced routing scheme and charging algorithm under the conditions of known nodes' location information. First,the network area is discretized based on the cellular hexagonal grid where the grids and nodes are stratified respectively. A balanced routing scheme of hierarchical data transmission is proposed. Then we formulate mathematic model with pre-defined energy transmitter's moving path to minimize the variance of the nodes' residual energy in the adjacent two layers and derive the charging time at different charging spots from the inner layer to the outer layer. Simulation results reveal that compared with the existing balanced charging method,our proposed algorithm can significantly improve the balance of node's residual energy and prolong the network lifetime.
In order to solve the problems existing in the traditional battery-powered sensor network that the battery is not available to replace and the energy of nodes is easy to be exhausted,RF energy harvesting technology has been applied to the wireless rechargeable sensor network. Since the workload and harvested energy of sensor nodes in different positions are different,balanced distribution of nodes' energy can effectively improve the survival rate of nodes. Considering the scenario where mobile RF energy transmitter is employed for charging,we design a reasonable balanced routing scheme and charging algorithm under the conditions of known nodes' location information. First,the network area is discretized based on the cellular hexagonal grid where the grids and nodes are stratified respectively. A balanced routing scheme of hierarchical data transmission is proposed. Then we formulate mathematic model with pre-defined energy transmitter's moving path to minimize the variance of the nodes' residual energy in the adjacent two layers and derive the charging time at different charging spots from the inner layer to the outer layer. Simulation results reveal that compared with the existing balanced charging method,our proposed algorithm can significantly improve the balance of node's residual energy and prolong the network lifetime.
引文
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[2]徐骥,朱艺华,田贤忠,等.无线传感器网络中利用随机网络编码的低能耗可靠机会路由[J].电子学报,2016,44(8):1799-1805.Ji X U,Zhu Y H,Tian X Z,et al. Energy-efficient reliable opportunistic routing applying random network coding for wireless sensor network[J]. Acta Electronica Sinica,2016,44(8):1799-1805.(in Chinese)
[3]张德干,赵晨鹏,李光,等.一种基于前向感知因子的WSN能量均衡路由方法[J].电子学报,2014,42(1):113-118.Gan Z D,Peng Z C,Guang L I,et al. A kind of routing protocol based on forward-aare factor for energy balance[J]. Acta Electronica Sinica,2014,42(1):113-118.(in Chinese)
[4]Fu L,Cheng P,Gu Y,et al. Minimizing charging delay in wireless rechargeable sensor networks[A]. International Conference on Computer Communications(INFOCOM)[C]. Turin,Italy:IEEE,2013. 2922-2930.
[5] Xie L,Shi Y,Hou Y T,et al. On renewable sensor networks with wireless energy transfer:The multi-node case[A]. Sensor,Mesh and Ad Hoc Communications and Networks(SECON)[C]. Seoul,South Korea:IEEE,2012. 10-18.
[6]Shi L,Han J,Han D,et al. The dynamic routing algorithm for renewable wireless sensor networks with wireless power transfer[J]. Computer Networks,2014,74(PA):34-52.
[7] Dai H,Jiang L,Wu X,et al. Near optimal charging and scheduling scheme for stochastic event capture with rechargeable sensors[A]. International Conference on M obile Ad-Hoc and Sensor Systems(M ASS)[C]. Hangzhou,China:IEEE,2013. 10-18.
[8] Chen F,Zhao Z,Min G,et al. Speed control of mobile chargers serving wireless rechargeable networks[J]. Future Generation Computer Systems,2018,80:242-249.
[9]He L,Gu Y,Pan J,et al. On-demand charging in wireless sensor networks:theories and applications[A]. International Conference on M obile Ad-Hoc and Sensor Systems(MASS)[C]. Hangzhou,China:IEEE,2013. 28-36.
[10] Lin T L,Li S L,Chang H Y. A power balance aware wireless charger deployment method for complete coverage in wireless rechargeable sensor networks[J]. Energies,2016,9(9):695.
[11]Xia X,Li S,Zhang Y,et al. Towards energy-balanced data transmission for lifetime optimization in wireless sensor networks[A]. IEEE International Conference on Communications(ICC)[C]. Kuala Lumpur,M alaysia:IEEE,2016. 1-7.
[12] Han G,Qian A,Jiang J,et al. A grid-based joint routing and charging algorithm for industrial wireless rechargeable sensor networks[J]. Computer Networks,2016,101(C):19-28.
[13]He S,Chen J,Jiang F,et al. Energy provisioning in wireless rechargeable sensor networks[J]. IEEE Transactions on M obile Computing,2013,12(10):1931-1942.
[14] Younis O,Fahmy S. HEED:a hybrid,energy-efficient,distributed clustering approach for ad hoc sensor networks[J]. IEEE Transactions on Mobile Computing,2004,3(4):366-379.
[15]Yang X,Han G,Liu L,et al. IGRC:An improved gridbased joint routing and charging algorithm for wireless rechargeable sensor networks[J]. Future Generation Computer Systems,2017,doi:10. 1016/j. future. 2017.09. 051.