This paper proposes a particle filter based target localization (PFTL) algorithm, and a sampling aware tracking cluster formation (SAC) scheme for organizing the sensor nodes, which maximizes the coverage area of target's trajectory in each cluster. The key idea of PFTL is to represent the possible locations of mobile target with a number of weighted particles, and to estimate the particles for computing the position of the object when the range measurements are available at next sampling time step. The motivation behind PFTL is that if the sink is willing to tolerate a small error, regarding the position of the target, the in-network communication can be greatly decreased, as well as the consumed energy, which is the most precious resource in wireless sensor networks. To balance the computation and communication overhead of network, this study designed a node scheduling scheme SAC, which dynamically clusters the sensors aimed at minimizing the times of tracking data hand-offs, so as to save the energy expenditure. Extensive simulations are conducted to verify the proposed methodologies, and the results reveal that PFTL and SAC not only reduce the localization error, but also efficiently extend the network lifetime.