Mobile Scheduling for Spatiotemporal Detection in Wireless Sensor Networks

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 requ...

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Veröffentlicht in:IEEE transactions on parallel and distributed systems Jg. 21; H. 12; S. 1851 - 1866
Hauptverfasser: Xing, Guoliang, Wang, Jianping, Yuan, Zhaohui, Tan, Rui, Sun, Limin, Huang, Qingfeng, Jia, Xiaohua, So, Hing Cheung
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.12.2010
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
algorithm/protocol design and analysis
Algorithms
Collaboration
Computer simulation
Data fusion
Delay
Face detection
Mission critical systems
Networks
Object detection
Optimization
Scheduling
Scheduling algorithm
Sensor fusion
Sensor systems
Sensors
Spatiotemporal phenomena
Target detection
Wireless sensor networks
ISSN:1045-9219, 1558-2183
Online-Zugang:Volltext
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Zusammenfassung: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.
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2010.41