Dynamic Resource Optimization for Decentralized Estimation in Energy Harvesting IoT Networks

We study decentralized estimation of time-varying signals at a fusion center, when energy harvesting sensors transmit sampled data over rate-constrained links. propose dynamic strategies to select radio parameters, sampling set, and harvested each node, with the aim estimating signal while ensuring: 1) accuracy recovery procedure 2) stability batteries around prescribed operating level. The approach is based on stochastic optimization tools, which enable adaptive without need apriori knowledge statistics channels arrivals processes. Numerical results validate proposed for under communication constraints typical Internet-of-Things scenarios.