Distributed tracking with energy management in wireless sensor networks



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We consider a wireless sensor network (WSN) tasked with tracking a process using a set of distributed nodes. Here multiple remote sensor nodes estimate the physical process (viz., a moving object) and transmit quantized estimates to a fusion center for processing. At the fusion node a BLUE (best linear unbiased estimation) approach is used to combine the sensor estimates and to create a final estimate of the state. In this framework the uncertainty of the overall estimate is derived and shown to depend on the individual sensor transmit energy and quantization levels, as well as the Kalman tracker uncertainty at the node. Since power and bandwidth are critically constrained resources in battery operated sensor nodes, we attempt to quantify the trade-off between the lifetime of the network and the estimation quality over time. Three different convex formulations of the underlying nonconvex mixed integer nonlinear optimization problem are presented. Unlike previous work this effort incorporates the operating state of the nodes into the decisions of the optimum bits and the transmission power levels based on a heuristic. Simulation results for all formulations demonstrate the quality of the state estimate as well as the extended lifetime of the WSN.



Distributed tracking, Distributed estimation, Wireless sensor networks, Convex optimization