LEDFD: A Low Energy Consumption Distributed Fault Detection Algorithm for Wireless Sensor Networks

被引:32
作者
Xu, Xiaolong [1 ,2 ]
Geng, Weijian [1 ]
Yang, Geng [3 ]
Bessis, Nik [4 ]
Norrington, Peter [5 ]
机构
[1] Nanjing Univ Posts & Telecommun, Coll Comp, Nanjing 210003, Peoples R China
[2] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing 210046, Jiangsu, Peoples R China
[3] Nanjing Univ Posts & Telecommun, Jiangsu High Technol Res Key Lab WSNs, Nanjing 210003, Peoples R China
[4] Univ Derby, Sch Comp & Math, Derby DE22 1GB, England
[5] Univ Bedfordshire, Inst Res Applicable Comp, Luton LU1 3JU, Beds, England
来源
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS | 2014年
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
RANGE-FREE LOCALIZATION; GRAPH;
D O I
10.1155/2014/714530
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Detection of faulty nodes and network energy saving have become the hottest research topics. Furthermore, current fault detection algorithms always pursue high detection performance but neglect energy consumption. In order to obtain good fault detection performance and save the network power, this paper proposes a low energy consumption distributed fault detection algorithm (LEDFD), which takes full advantage of temporally correlated and spatially correlated characteristics of the sensor nodes. LEDFD utilizes the temporally correlated information to examine some faulty nodes and then utilizes the spatially correlated information to examine the nodes that have not been detected as faulty through exchanging information among neighbor nodes to determine those nodes' state. Because LEDFD takes the data produced by nodes themselves to detect certain types of faults, which means nodes need not exchange information with their neighbor nodes during the entire detection process, the energy consumption of networks is efficiently reduced. Experimental results show that the algorithm has good performance and low energy consumption compared with current algorithms.
引用
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页数:10
相关论文
共 16 条
[1]  
Chen J., 2006, P 2006 WORKSH DEP IS, P65, DOI [DOI 10.1145/1160972.1160985, 10.1145/1160972.1160985]
[2]  
Ding M, 2005, IEEE INFOCOM SER, P902
[3]  
Gao Jian-Liang, 2007, Journal of Software, V18, P1208, DOI 10.1360/jos181208
[4]  
[季赛 JI Sai], 2009, [传感器与微系统, Transducer and Microsystem Technology], V28, P117
[5]   A New Method for Node Fault Detection in Wireless Sensor Networks [J].
Jiang, Peng .
SENSORS, 2009, 9 (02) :1282-1294
[6]  
Kapoor N., 2011, INT J COMPUTER SCI T, V2, P211
[7]   Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks [J].
Krishnamachari, B ;
Iyengar, S .
IEEE TRANSACTIONS ON COMPUTERS, 2004, 53 (03) :241-250
[8]   Fault detection of wireless sensor networks [J].
Lee, Myeong-Hyeon ;
Choi, Yoon-Hwa .
COMPUTER COMMUNICATIONS, 2008, 31 (14) :3469-3475
[9]   A dynamic neural network approach for solving nonlinear inequalities defined on a graph and its application to distributed, routing-free, range-free localization of WSNs [J].
Li, Shuai ;
Qin, Feng .
NEUROCOMPUTING, 2013, 117 :72-80
[10]   Using Laplacian Eigenmap as Heuristic Information to Solve Nonlinear Constraints Defined on a Graph and Its Application in Distributed Range-Free Localization of Wireless Sensor Networks [J].
Li, Shuai ;
Wang, Zheng ;
Li, Yangming .
NEURAL PROCESSING LETTERS, 2013, 37 (03) :411-424