doi:

DOI:

Journal of Computer Research and Development (计算机研究与发展) 2009/2009:12 PP.2134-2142

A Distributed Target Detection Algorithm Based on Credit-Degree in Wireless Sensor Network


Abstract:
Target detection in wireless sensor network is widely used in many fields, such as military, ecological, medical and security, and it has highly practical research significance. Traditional centralized algorithm relies on fusion nodes so much that the network built is not robust enough and high false alarm rate is caused by its binary decision. Traditional centralized algorithm's dependence on network coverage will cause "blind holes" of detection alarm in the network. To solve these problems, a distributed target detection algorithm based on credit degree—k-CD algorithm is proposed. k-CD algorithm runs as follows: First, the algorithm adjusts each node's credit degree using the neighbor automata with all its neighbors'credit degrees as the input; then, the nodes which have detected the target form a virtual group and make decision fusions using the method of credit degree matching; Finally, the algorithm solves the "blind hole" problem caused by network coverage through triggered mobile nodes. The simulation results show that compared with the majority voting algorithm (MV), k-CD algorithm can increase an average of 35% of detection probability while reducing the false alarm rate by 62% and with different network coverage degrees, network life-cycle can be prolonged by 44% on the average.

Key words:WSN,neighbor automata,k-CD matching decision fusion,triggered mobile node,target detection

ReleaseDate:2014-07-21 15:00:34



[1] Amaldi E, Capone A, Cesana M, et al. Coverage planning of wireless sensors for mobile target detection [C] //Proc of Int Conf of IEEE MASS'08. Piscataway, NJ: IEEE, 2008: 48-57

[2] Katenka N, Levina E, Michailidis G. Local vote decision fusion for target detection in wireless sensor network [J]. IEEE Trans on Signal Processing, 2008, 56(1): 329-338

[3] Liu Benyuan, Brass P, Dousse O, et al. Mobility improves coverage of sensor networks [C] //Proc of the 6th ACM Int Symp on Mobile ad hoc Networking and Computing. New York: ACM, 2005: 300-308

[4] Clouqueur T, Saluja K K, Ramanathan P. Fault tolerance in collaborative sensor networks for target detection. Computers [J]. IEEE Trans on Computers, 2004, 53(3): 320-333

[5] Niu R, Varshney P K, Moore M, et al. Decision fusion in a wireless sensor network with a random number of sensors [C] //Proc of Int Conf of IEEE ICASSP05. Piscataway, NJ: IEEE, 2005: 861-864

[6] Tan Rui, Xing Guoliang, Wang Jianping, et al. Collaborative target detection in wireless sensor network with reactive mobility [C] //Proc of Int Workshop on Quality of Service. Piscataway, NJ: IEEE, 2008: 150-159

[7] Clouqueur T, Phipatanasuphorn V, Ramanathan P, et al. Sensor deployment strategy for detection of targets traversing a region [J]. Mobile Networks and Applications, 2003, 8(4): 453-461

[8] Wang Xue, Ma Junjie, Wang Sheng, et al. Cluster-based dynamic energy management for collaborative target tracking in wireless sensor network [J]. Sensors, 2007, 7(7): 1193-1215

[9] Chen Weipeng, Hou J C, Sha L. Dynamic clustering for acoustic target tracking in wireless sensor network [J]. IEEE Trans on Mobile Computing, 2004, 3(3): 258-271

[10] Varshney P K, Burrus C S. Distributed detection and data fusion [M]. Berlin: Spinger, 1996

[11] Duarte M, Hu Y. Distance based decision fusion in a distributed wireless sensor network [J]. Telecommunica-tion Systems, 2004, 26(2): 339-350

[12] Srinivasan W W V, Chua K C. Trade-offs between mobility and density for coverage in wireless sensor network [C] //Proc of the 13th Annual ACM Int Conf on Mobile Computing and Networking. New York: ACM, 2007: 39-50

[13] Wang Jichun, Huang Liusheng, Xu Hongli. A novel range free localization scheme based on Voronoi diagrams in wireless sensor network [J]. Journal of Computer Research and Development, 2008, 45(1): 119-125 (in Chinese)(王继春, 黄刘生, 徐宏力. 基于Voronoi图的无需测距的无线传感器网络节点定位算法 [J]. 计算机研究与发展, 2008, 45(1): 119-125)

[14] Maltz D A, Johnson D B. Random Waypoint Model [EB/OL]. (2006-04-12) [2009-08-08]. http://www.tct.hut.fi/~esa/java/rwp/rwp-model.shtml

[15] Sun T, Chen L J, Han C C, et al. Reliable sensor networks for planet exploration [C] //Proc of IEEE Networking, Sensing and Control. Piscataway, NJ: IEEE, 2005: 816-821

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