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The authors propose a sequential quasi-Monte Carlo (SQMC)-based algorithm for joint estimation of sensor-node locations and target trajectory in a wireless sensor network. The sensor nodes are randomly deployed with no prior knowledge about their positions. A predictive entropy-based information utility is used to select the leader node at each stage, and all other nodes are kept in standby mode to save power. The Bayesian estimates required to track the systems's nonlinear dynamics are computed using the powerful SQMC method, which naturally integrates sensor collaboration with optimal leader node selection. Extensions of the algorithm to other interesting scenarios such as missing observations and non-Gaussian noise are also presented, which are very relevant to the unreliable environments encountered in hostile territories. The authors demonstrate through simulations that even with a very small fraction of the total number of nodes acting as beacon nodes, the proposed method can not only track the moving target, but can also obtain fairly accurate estimates of the (unknown) locat p(z(t)|z(i(j))(t - 1))ions of the sensor nodes.