Issue No. 12 - December (2010 vol. 21)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPDS.2010.41
Guoliang Xing , Michigan State University, East Lansing
Jianping Wang , City University of Hong Kong, Hong Kong
Zhaohui Yuan , City University of Hong Kong, Hong Kong
Rui Tan , Michigan State University, East Lansing
Limin Sun , Chinese Academy of Sciences, Beijing
Qingfeng Huang , C8 MediSensors Inc., Los Gotos
Xiaohua Jia , City University of Hong Kong, Hong Kong
Hing Cheung So , City University of Hong Kong, Hong Kong
Wireless sensor networks (WSNs) deployed for mission-critical applications face the fundamental challenge of meeting stringent spatiotemporal performance requirements using nodes with limited sensing capacity. Although advance network planning and dense node deployment may initially achieve the required performance, they often fail to adapt to the unpredictability and variability of physical reality. This paper explores efficient use of mobile sensors to address limitations of static WSNs for target detection. We propose a data-fusion-based detection model that enables static and mobile sensors to effectively collaborate in target detection. An optimal sensor movement scheduling algorithm is developed to minimize the total moving distance of sensors while achieving a set of spatiotemporal performance requirements including high detection probability, low system false alarm rate, and bounded detection delay. The effectiveness of our approach is validated by extensive simulations based on real data traces collected by 23 sensor nodes.
Data fusion, algorithm/protocol design and analysis, wireless sensor networks.
L. Sun et al., "Mobile Scheduling for Spatiotemporal Detection in Wireless Sensor Networks," in IEEE Transactions on Parallel & Distributed Systems, vol. 21, no. , pp. 1851-1866, 2010.