Efficient State Estimation with Energy Harvesting and Fairness Control Using Stochastic Optimization

Wireless sensor networks (WSN) intended for critical applications often rely on energy harvesting batteries, yet also require backup batteries to ensure uninterrupted operation. An optimal algorithm for resource allocation in such WSN used for state estimation is presented. The resulting state estimate approaches a minimum accuracy constraint with efficient use of the harvesting capability to minimize the use of the backup power. Power usage is fairly distributed among the sensors to prolong system lifetime. The algorithm is based on a stochastic Lyapunov optimization used with a standard Kalman filter estimator. The framework can achieve arbitrarily close to optimal power efficiency over time without requiring knowledge of either the channel or harvesting statistics. Asymptotically optimal efficiency is obtained at the expense of an increase in latency for the system to converge to the desired estimation accuracy.