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Because of densely deployment of sensors and limited energy supply in wireless sensor networks, it is not necessary for every sensor to be active at any time instant. Many researches have been focused on this issue, while less emphasis was placed on the optimal sleep time of each node. This paper proposed an adaptive energy conservation strategy for target tracking based on a clustering network, where cluster head autonomously determines when and if to sleep for its cluster members. During surveillance stage, nodes adjust the sleep intervals by their positions. And in tracking stage, they can adaptively decide their sleeping time using the information from the neighborhood cluster head with considering the different influence of each neighbor. The presented approach allows sensor nodes that are far away from targets to sleep more. And each node can choose an optimal sleep time so as to make system adaptive and energy-efficient. We show the performance of our approach in terms of energy drop, comparing it to a naive approach and dynamic PM with fixed sleep time. From the experimental results, it is readily seen that the efficiency of the proposed approach.