计算机科学 ›› 2018, Vol. 45 ›› Issue (11A): 315-319.

• 网络与通信 • 上一篇    下一篇

无线可充电传感网的高能效移动充电策略

王自强, 林辉   

  1. 浙江工业大学计算机科学与技术学院 杭州310023
  • 出版日期:2019-02-26 发布日期:2019-02-26
  • 作者简介:王自强(1991-),男,硕士生,主要研究方向为无线传感器网络,E-mail:zqgwang@qq.com;林 辉(1982-),男,博士生,讲师,主要研究方向为无线传感器网络。

High Energy Efficient Mobile Charging Strategy in Wireless Rechargeable Sensor Networks

WANG Zi-qiang, LIN Hui   

  1. School of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,Chin
  • Online:2019-02-26 Published:2019-02-26

摘要: 采用无线能量传输技术的移动充电在无线可充电传感网的能量供给中扮演着重要角色。现有相关研究通常忽略了节点在等待充电时的能耗,简化对节点剩余能量阈值的假设,容易使节点耗尽能量而暂停工作。针对这一问题,文中提出一种新的移动充电策略,建立一种节点剩余能量预测模型以匹配节点的充电需求,分别构建了带权路径最小化以及基于带权路径的能量分配最大化问题,并分别采用遗传算法与线性规划对两者进行求解。通过仿真对所提移动充电策略进行了评估并与现有研究进行了对比。结果显示,所提移动充电策略有更高的移动充电能效,可维持网络长期正常工作。

关键词: 能量分配, 能效, 无线可充电传感网, 移动充电

Abstract: Mobile charging through wireless power transfer technology plays an important role in powering the wireless rechargeable sensor networks (WRSNs).Existing studies usually overlook the energy consumed by nodes during their waiting time before they get charged.These studies also make simplified assumption on the nodes’ residual energy threshold,which can easily lead to the suspension of nodes.A novel mobile charging strategy was proposed in this paper so as to solve this problem.A residual energy prediction model was proposed in order to match the nodes’ actual energy demand.The weighted path minimization problem and the weighted path based energy allocation maximization problem were formulated and solved with genetic algorithm and linear programming respectively.The proposed mobile charging strategy was then evaluated and compared with existing studies through simulations.The results demonstrate that the proposed strategy can increase the charging energy efficiency and can insure the network to operate permanently.

Key words: Energy allocation, Energy efficiency, Mobile charging, Wireless rechargeable sensor networks

中图分类号: 

  • TP393
[1]KURS A,KARALIS A,MOFFATT R,et al.Wireless power transfer via strongly coupled magnetic resonances [J].Science,2007,317(5834):83-86.
[2]KURS A,MOFFATT R,SOLJAČÌC M.Simultaneous mid-range power transfer to multiple devices [J].Applied Physics Letters,2010,96(4):044102.
[3]SHI Y,XIE L,HOU Y T,et al.On renewable sensor networks with wireless energy transfer[C]∥INFOCOM,2011 Procee-dings IEEE.IEEE,2012:1350-1358.
[4]XIE L,SHI Y,HOU Y T,et al.Multi-node wireless energy charging in sensor networks [J].IEEE/ACM Transactions on Networking,2015,23(2):437-450.
[5]SHU Y,YOUSEFI H,CHENG P,et al.Near-optimal Velocity Control for Mobile Charging in Wireless Rechargeable Sensor Networks [J].IEEE Transactions on Mobile Computing,2016,15(7):1699-1713.
[6]HE L,KONG L,GU Y,et al.Evaluating the On-Demand Mobile Charging in Wireless Sensor Networks [J].IEEE Transactions on Mobile Computing,2015,14(9):1861-1875.
[7]FU L,HE L,CHENG P,et al.ESync:Energy Synchronized Mobile Charging in Rechargeable Wireless Sensor Networks [J].IEEE Transactions on Vehicular Technology,2016,65(9):7415-7431.
[8]KHELLADI L,DJENOURI D,ROSSI M,et al.Efficient on-demand multi-node charging techniques for wireless sensor networks [J].Computer Communications,2017,101:44-56.
[9]SHU Y,SHU Y,CHENG P,et al.TOC:Localizing Wireless Rechargeable Sensors with Time of Charge [J].ACM Transactions on Sensor Networks (TOSN),2015,11(3):44-66.
[10]CHANG Z,WU X,WANG W,et al.Localization in Wireless Rechargeable Sensor Networks Using Mobile Directional Char-ger [C]∥IEEE Global Communications Conference.IEEE,2015:1-6.
[11]XU W,LIANG W,JIA X,et al.Maximizing Sensor Lifetime in a Rechargeable Sensor Network via Partial Energy Charging on Sensors [C]∥IEEE International Conference on Sensing,Communication,and Networking.IEEE,2016:1-9.
[12]WANG C,LI J,YE F,et al.A Mobile Data Gathering Framework for Wireless Rechargeable Sensor Networks with Vehicle Movement Costs and Capacity Constraints [J].IEEE Transactions on Computers,2016,65(8):2411-2417.
[1] 陈乐, 高岭, 任杰, 党鑫, 王祎昊, 曹瑞, 郑杰, 王海.
基于自适应码率移动增强现实应用的能效优化研究
Adaptive Bitrate Streaming for Energy-Efficiency Mobile Augmented Reality
计算机科学, 2022, 49(1): 194-203. https://doi.org/10.11896/jsjkx.201100107
[2] 程云飞, 田红心, 刘祖军.
NOMA系统异构网络中联合用户关联和功率控制协同优化
Collaborative Optimization of Joint User Association and Power Control in NOMA Heterogeneous Network
计算机科学, 2021, 48(3): 269-274. https://doi.org/10.11896/jsjkx.191100213
[3] 张昊, 管昕洁, 白光伟.
基于强化学习的无线可充电传感网移动充电路径优化
Optimization of Mobile Charging Path of Wireless Rechargeable Sensor Networks Based on Reinforcement Learning
计算机科学, 2020, 47(11): 316-321. https://doi.org/10.11896/jsjkx.200400075
[4] 陈晓杰,周清雷,李斌.
基于FPGA的7-Zip加密文档高能效口令恢复方法
Energy-efficient Password Recovery Method for 7-Zip Document Based on FPGA
计算机科学, 2020, 47(1): 321-328. https://doi.org/10.11896/jsjkx.190100027
[5] 赵磊, 周金和.
基于复杂网络内容场的ICN能效优化策略
ICN Energy Efficiency Optimization Strategy Based on Content Field of Complex Networks
计算机科学, 2019, 46(9): 137-142. https://doi.org/10.11896/j.issn.1002-137X.2019.09.019
[6] 叶符明, 李雯婷, 王颖.
MC2ETS:移动云计算中一种能效任务调度算法
MC2ETS:An Energy-efficient Tasks Scheduling Algorithm in Mobile Cloud Computing
计算机科学, 2019, 46(6): 135-142. https://doi.org/10.11896/j.issn.1002-137X.2019.06.020
[7] 赵宁博, 刘伟, 罗嵘, 胡顺仁.
无线传感器节点工作模式转换策略优化模型
Optimization Model of Working Mode Transformation Strategies for Wireless Sensor Nodes
计算机科学, 2019, 46(5): 44-49. https://doi.org/10.11896/j.issn.1002-137X.2019.05.006
[8] 王旭, 林志贵, 刘晓峰, 孟德军.
WRSNs中接收线圈间互感对传能的影响分析
Analysis of Influence of Mutual Inductances on Energy Transmitting Between Receiving Coil in WRSNs
计算机科学, 2019, 46(11A): 381-386.
[9] 贾迅, 钱磊, 邬贵明, 吴东, 谢向辉.
FPGA应用于高性能计算的研究现状和未来挑战
Research Advances and Future Challenges of FPGA-based High Performance Computing
计算机科学, 2019, 46(11): 11-19. https://doi.org/10.11896/jsjkx.191100500C
[10] 李廷元, 王博岩.
QoS约束云环境下的工作流能效调度算法
Workflow Energy-efficient Scheduling Algorithm in Cloud Environment with QoS Constraint
计算机科学, 2018, 45(6A): 304-309.
[11] 姚信威,钟礼斌,王万良,杨双华.
基于混合储能结构的能量捕获无线通信信道容量分析
Capacity Analysis of Energy Harvesting Wireless Communication Channel Based on Hybrid Energy Storage
计算机科学, 2018, 45(2): 165-170. https://doi.org/10.11896/j.issn.1002-137X.2018.02.029
[12] 朱江, 雷云, 王雁.
认知无线传感器网络中基于稳定性的能效路由协议
Stability Based Energy-efficient Routing Protocol in Cognitive Wireless Sensor Networks
计算机科学, 2018, 45(11): 97-102. https://doi.org/10.11896/j.issn.1002-137X.2018.11.014
[13] 邢文凯,高雪霞,侯小毛,翟萍.
云计算环境下的模糊解耦能效优化算法研究
Research on Fuzzy Decoupling Energy Efficiency Optimization Algorithm in Cloud Computing Environment
计算机科学, 2017, 44(12): 75-79. https://doi.org/10.11896/j.issn.1002-137X.2017.12.015
[14] 郭荣佐,郭 进,黎 明.
绿色计算与绿色嵌入式系统
Green Computing and Green Embedded Systems
计算机科学, 2015, 42(8): 13-21.
[15] 马晨明,王万良,洪榛.
无线传感器网络中一种改进的能效数据收集协议
Improved Energy Efficient Data Gathering Protocol in Wireless Sensor Network
计算机科学, 2015, 42(2): 65-69. https://doi.org/10.11896/j.issn.1002-137X.2015.02.014
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!