Target tracking based on multiagent and game theory in wireless sensor network

The increasing capabilities and declining cost of computation and communication devices has led to an increase in the number of applications of wireless sensor networks (WSNs). One such application is target tracking. Due to the severe resource constraints in WSNs, the design of an energyefficient target tracking algorithm with high accuracy and low computational complexity becomes a highly challenging problem. In this paper, we propose an auction-based adaptive sensor activation algorithm (AASA) for target tracking in WSNs. The cluster formation process consists of a prediction method and an auction mechanism. Based on prediction, only the nodes in the predicted region (PR) are activated and the rest of the nodes remain in sleeping mode. Through the auction mechanism, appropriate sensor nodes are chosen to form a cluster and a sensor with the biggest bid in the cluster is selected as cluster head, which guarantees load balancing. To make a trade-off between energy efficiency and tracking quality, the radius of PR and the number of members in a cluster are dynamically adjusted according to current tracking quality. Simulation results show that AASA obtains significant energy savings, decreases the target missing rate and prolongs the network lifetime.

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Target tracking based on multiagent and game theory in wireless sensor network

Cited by 2 publications

References 8 publications

Optimal strategies for searching a mobile object using mobile sensors in a grid environment

Optimal strategies for searching a mobile object using mobile sensors in a grid environment

Auction-Based Adaptive Sensor Activation Algorithm for Target Tracking in WSNs

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