The coverage problem in a wireless sensor network
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
A Bidding Protocol for Deploying Mobile Sensors
ICNP '03 Proceedings of the 11th IEEE International Conference on Network Protocols
Integrated coverage and connectivity configuration in wireless sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Coverage Issue in Sensor Networks with Adjustable Ranges
ICPPW '04 Proceedings of the 2004 International Conference on Parallel Processing Workshops
Selection and Navigation of Mobile Sensor Nodes Using a Sensor Network
PERCOM '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications
DCTC: dynamic convoy tree-based collaboration for target tracking in sensor networks
IEEE Transactions on Wireless Communications
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Target tracking is an important issue in wireless sensor network applications. In this paper, we design a Coverage-Hole Trap Model (CTM) based on a system that contains one moving target, one moving pursuer and a distributed relay robot network with sensing coverage holes. Usually, the coverage hole is harmful for target tracking in wireless mobile robot network (WMRN). Many algorithms have been proposed to detect and avoid coverage holes. In this paper, we try to use coverage holes as traps to point out the region where the target is moving into, and help the pursuer to catch the target. After the coverage holes are discovered by multiple relay robots in the initialization phase, the pursuer calculates the target position and predicts where it should move to. We propose Distributed Coverage-Hole Detection Algorithm (DCDA), which is based on 3MeSH method to discover coverage holes and tackle this challenge by introducing the Coverage-Hole Based Pursuer Algorithm (CBPA). CBPA is a prediction-based algorithm for the pursuer using the information about the target and coverage holes obtained from the relays. Simulation results show that our methods address the limitation of the previous work, considerably improve the required tracking time and reduce the average total traveling distance of target.