Energy-aware distributed tracking in wireless sensor networks

Abstract

We consider a wireless sensor network engaged in the task of distributed tracking. Here, multiple remote sensor nodes estimate a physical process (for example, 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 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. 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. A unique feature of this work is that instead of merely allowing a greedy minimization of uncertainty in each time instance, the lifetime of the wireless sensor network is improved by incorporating a heuristic scaling on the operating capability of each node. This heuristic in turn depends on the remaining energy, equivalent to the past history of power and quantization decisions. Simulation results demonstrate the quality of the state estimate as well as the extended lifetime of the network when power and quantization levels are dynamically updated.