DocumentCode :
3107542
Title :
A Coverage Enhancement Method of Directional Sensor Network Based on Genetic Algorithm for Occlusion-Free Surveillance
Author :
Jiang, Yibo ; Yang, Jinwei ; Chen, Weijie ; Wang, Wanliang
Author_Institution :
Coll. of Comput. Sci. & Technol., Zhejiang Univ. of Technol., Hangzhou, China
fYear :
2010
fDate :
26-28 Sept. 2010
Firstpage :
311
Lastpage :
314
Abstract :
Coverage enhancement is one of the hot research topics in area surveillance using wireless sensor network. In this paper, the characteristics of coverage model for directional sensor networks are analyzed, and the prototype of view orientation of wireless sensor node in surveillance area with obstacles is discussed. Then, the concept of weighted coverage modal with different weight is provided, and a novel coverage optimizing algorithm based on genetic algorithm for occlusion-free surveillance model is proposed. The dynamic optimization process is constructed for adapting the real-time changing and a discrete model of sub-regions is presented for simplifying calculation. The result of experiment show that the proposed method can optimize the coverage rate of occlusion-free surveillance in wireless directional sensor network, and make coverage rate more stable when obstacles are increased.
Keywords :
genetic algorithms; video surveillance; wireless sensor networks; coverage enhancement method; coverage optimizing algorithm; directional sensor network; dynamic optimization process; genetic algorithm; occlusion-free surveillance; weighted coverage modal; wireless sensor network; Cameras; Gallium; Optimization; Sensors; Surveillance; Wireless communication; Wireless sensor networks; Coverage Enhancement; Occlusion-Free Surveillance; Wireless Directional Sensor Networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Aspects of Social Networks (CASoN), 2010 International Conference on
Conference_Location :
Taiyuan
Print_ISBN :
978-1-4244-8785-1
Type :
conf
DOI :
10.1109/CASoN.2010.76
Filename :
5636905
Link To Document :
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