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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.