Dynamic Resource Optimization for Decentralized Estimation in Energy Harvesting IoT Networks

TL;DR

This paper introduces a dynamic resource management strategy for energy harvesting IoT sensors to optimize decentralized signal estimation, balancing accuracy and battery stability without prior statistical knowledge.

Contribution

It presents an adaptive stochastic optimization approach for joint selection of radio parameters, sampling, and energy harvesting in IoT networks.

Findings

Effective in maintaining estimation accuracy under energy constraints.

Ensures battery stability around desired operating levels.

Validated through numerical simulations in IoT scenarios.

Abstract

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