Impact of mobile node density on detection performance measures in a hybrid sensor network
IEEE Transactions on Wireless Communications
Coverage properties of clustered wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
Detecting intra-room mobility with signal strength descriptors
Proceedings of the eleventh ACM international symposium on Mobile ad hoc networking and computing
EURASIP Journal on Advances in Signal Processing
Mobile Networks and Applications
Low cost data gathering using mobile hybrid sensor networks
ADHOC-NOW'12 Proceedings of the 11th international conference on Ad-hoc, Mobile, and Wireless Networks
MDiag: Mobility-assisted diagnosis for wireless sensor networks
Journal of Network and Computer Applications
Poster abstract: connected wireless camera network deployment with visibility coverage
Proceedings of the 12th international conference on Information processing in sensor networks
Detect smart intruders in sensor networks by creating network dynamics
Computer Networks: The International Journal of Computer and Telecommunications Networking
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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 of physical reality. This paper explores efficient use of mobile sensors to address the limitations of static WSNs in target detection. We propose a data fusion 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.