面向能量受限传感网的最优数据收集策略

doi:10.3969/j.issn.0372-2112.2017.08.035

电子学报 ›› 2017, Vol. 45 ›› Issue (8): 2049-2056.DOI: 10.3969/j.issn.0372-2112.2017.08.035

面向能量受限传感网的最优数据收集策略

陈朋朋, 高守婉, 黄丹, 牛强

中国矿业大学计算机科学与技术学院, 江苏徐州 221116

收稿日期:2016-08-21 修回日期:2017-03-29 出版日期:2017-08-25
- 通讯作者:
- 高守婉
- 作者简介:
- 陈朋朋,男,1983年11月出生于山东省济宁市.现为中国矿业大学计算机科学与技术学院副教授、硕士生导师,研究方向为传感器网络、大数据等.E-mail:chenp@cumt.edu.cn;黄丹,女,1991年10月出生于江西省上饶市.现为中国矿业大学计算机科学与技术学院硕士研究生,研究方向为传感器网络.E-mail:791843924@qq.com;牛强,男,1974年7月出生于辽宁省沈阳市.现为中国矿业大学计算机科学与技术学院教授、博士生导师,研究方向为物联网、智能信息处理等.E-mail:niuq@cumt.edu.cn
- 基金资助:
- 国家重点研发计划 (No.2016YFC060908）; 国家自然科学基金 (No.51674255）; 江苏省自然科学基金 (No.BK20160274）; 中国博士后科学基金特别资助项目 (No.2016T90523）; 江苏省产学研前瞻性联合研究项目 (No.BY2016026-03）

Optimal Data Collection Strategies for Energy-Constrained Sensor Networks

CHEN Peng-peng, GAO Shou-wan, HUANG Dan, NIU Qiang

School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China

Received:2016-08-21 Revised:2017-03-29 Online:2017-08-25 Published:2017-08-25
- Supported by:
- National Key Research and Development Program of China (No.2016YFC060908); National Natural Science Foundation of China (No.51674255); Natural Science Foundation of Jiangsu Province, China (No.BK20160274); Supported by China Postdoctoral Science Foundation (No.2016T90523); Prospective Project of Industry and University of Jiangsu Province (No.BY2016026-03)

摘要/Abstract

摘要： 本文研究了能量受限条件下无线传感器网络（wireless sensor networks，WSNs）的最优数据收集策略问题.首先，传感器节点周期性采集数据并通过卡尔曼滤波器（Kalman filter，KF）对信息进行预处理以滤除噪声.其次，考虑到通信为主要耗能环节，设计最优数据发送策略令节点在特定轮内发送数据，使得满足网络生存周期前提下，基站获得的数据精度最高.具体来说，针对单跳网络，给出可使基站误差方差最小化的数据发送策略；在此基础上，进一步提出面向多跳网络的改进数据发送策略.最后，利用仿真和原型实验验证所提策略的有效性.

关键词: 无线传感器网络, 数据收集, 能量效率, 数据精度, 卡尔曼滤波

Abstract: This paper investigates the problem of optimal data collection strategies for wireless sensor networks (WSNs) with limited resources.First,a sensor node periodically collects data and utilizes the Kalman filter (KF) to filter noise.Then,considering that communication is a primary source of energy consumption,we develop data transmission strategies to decide at which times the sensors should transmit their data.The proposed strategies can maximize the accuracy of data gathered by the base station for a required network lifetime.To be more specific,for single-hop networks,the optimal data transmission strategy is obtained which can minimize the estimation error.Based on this single-hop optimal strategy,the modified data transmission strategy is proposed for multi-hop networks.Simulation and prototype experiment results show the effectiveness of the presented strategies.

Key words: wireless sensor networks, data collection, energy efficiency, data accuracy, Kalman filtering

中图分类号:

TP393

陈朋朋, 高守婉, 黄丹, 等. 面向能量受限传感网的最优数据收集策略[J]. 电子学报, 2017, 45(8): 2049-2056.

CHEN Peng-peng, GAO Shou-wan, HUANG Dan, et al. Optimal Data Collection Strategies for Energy-Constrained Sensor Networks[J]. Acta Electronica Sinica, 2017, 45(8): 2049-2056.

参考文献

[1] Al-Turjman F,Hassanein H,Ibnkahla M.Towards prolonged lifetime for deployed WSNs in outdoor environment monitoring[J].Ad Hoc Networks,2015,24(PA):172-185.
[2] Cao N,Brahma S,Varshney P K.Target tracking via crowdsourcing:A mechanism design approach[J].IEEE Transactions on Signal Processing,2015,63(6):1464-1476.
[3] Xie M,Hu J,Guo S.Segment-based anomaly detection with approximated sample covariance matrix in wireless sensor networks[J].IEEE Transactions on Parallel and Distributed Systems,2015,26(2):574-583.
[4] Harb H,Makhoul A,Tawil R,et al.A suffix-based enhanced technique for data aggregation in periodic sensor networks[A].Proceedings of the 10th International Wireless Communications and Mobile Computing Conference[C].New York:IEEE,2014:494-499.
[5] Luo D,Zhu X,Wu X,et al.Maximizing lifetime for the shortest path aggregation tree in wireless sensor networks[A].Proceedings of the 2011 IEEE INFOCOM[C].Piscataway,NJ:IEEE,2011:1566-1574.
[6] Imon S K A,Khan A,Di Francesco M,et al.Maximizing lifetime for the shortest path aggregation tree in wireless sensor networks[J].IEEE/ACM Trans on Networking,2015,23(5):1401-1415.
[7] Abdulla A E A A,Fadlullah Z M,Nishiyama H,et al.Toward fair maximization of energy efficiency in multiple UAS-Aided networks:A game-Theoretic methodology[J].IEEE Transactions on Wireless Communications,2015,14(1):305-316.
[8] Tang X,Xu J.Adaptive data collection strategies for lifetime-constrained wireless sensor networks[J].IEEE Transactions on Parallel and Distributed Systems,2008,19(6):721-734.
[9] Makhoul A,Harb H,Laiymani D.Residual energy-based adaptive data collection approach for periodic sensor networks[J].Ad Hoc Networks,2015,35:149-160.
[10] Feng J,Lian B,Zhao H.Coordinated and adaptive information collecting in target tracking wireless sensor networks[J].IEEE Sensors Journal,2015,15(6):3436-3445.
[11] Villas L A,Boukerche A,Guidoni D L,et al.An energy-aware spatio-temporal correlation mechanism to perform efficient data collection in wireless sensor networks[J].Computer Communications,2013,36(9):1054-1066.
[12] Villas L A,Boukerche A,De Oliveira H A,et al.A spatial correlation aware algorithm to perform efficient data collection in wireless sensor networks[J].Ad Hoc Networks,2014,12(1):69-85.
[13] Shi L,Cheng P,Chen J.Sensor data scheduling for optimal state estimation with communication energy constraint[J].Automatica,2011,47(8):1693-1698.
[14] Zhang H,Cheng P,Shi L.Optimal denial-of-service attack scheduling with energy constraint[J].IEEE Transactions on Automatic Control,2015,60(11):3023-3028.
[15] Ren Z,Cheng P,Chen J,et al.Optimal periodic sensor schedule for steady-state estimation under average transmission energy constraint[J].IEEE Transactions on Automatic Control,2013,58(12):3265-3271.
[16] Jain A,Chang E Y,Wang Y F.Adaptive stream resource management using Kalman Filters[A].Proceedings of the ACM SIGMOD International Conference on Management of Data[C].New York:ACM,2004:11-22.
[17] Min J-K,Chung C-W.EDGES:Efficient data gathering in sensor networks using temporal and spatial correlations[J].Journal of Systems and Software,2010,83(2):271-282.
[18] Anderson B D O,Moore J.Optimal Filtering[M].Englewood Cliffs,NJ,USA:Prentice-Hall,1979.

面向能量受限传感网的最优数据收集策略

Optimal Data Collection Strategies for Energy-Constrained Sensor Networks

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