Optimizing network lifetime for distributed tracking with wireless sensor networks

Abstract

We consider the challenging problem of distributed tracking using wireless sensor networks (WSN). In our scenario, multiple spatially distributed sensor nodes estimate a physical process (viz. a moving object) and transmit quantized state estimates to a central fusion node for processing. The fusion node utilizes a BLUE (Best Linear Unbiased Estimation) approach to combine the individual sensor estimates. In this paradigm the uncertainty of the final estimate is dependent on the quantization and transmit energy levels. This makes the problem particularly challenging since power and bandwidth are critically constrained resources in WSNs. Thus, the trade-off between estimation quality and network lifetime is inherent. This work optimizes resource utilization while constraining estimation performance. Two convex formulations of the resulting Mixed-Integer Non-Linear program (MINLP) are given. Unlike most previous work, this effort heuristically incorporates the operating states of the nodes into the optimal decisions. The heuristic accomplishes a redistribution of effort, tasking healthier nodes to contribute more resources to the estimation process. Simulation results are presented for the given formulations which demonstrate the effectiveness of the heuristic for extending network lifetime.