Lifetime Maximization in Wireless Sensor Networks with an Estimation Mission

We study the problem of maximum lifetime in wireless sensor networks that are entitled with the task of estimating an unknown parameter or process. Sensors take measurements and transfer them in multi-hop fashion to a fusion center (FC) for Maximum Likelihood (ML) estimation. To engineer the network for lifetime maximization while adhering to estimation error specifications, the number of measurements by each sensor per unit of time (namely, sensor measurement rate) and the routes to the FC are controlled. Sensor spatial correlation, measurement accuracies, link qualities and energy reserves affect sensor measurement rates and the routes to the FC, and, in turn, measurement rates and sensor characteristics impact estimation error. We show that the problem can be decomposed into separate optimization problems where each sensor autonomously takes its measurement rate and routing decisions, and we propose an iterative primal-dual algorithm with low-overhead signaling for solving it. Our work optimally captures the fundamental tradeoff between network lifetime and estimation quality and yields a solution based on distributed sensor coordination.