Remote Estimation of Correlated Sources Under Energy Harvesting Constraints

Abstract

Remote estimation with an energy harvesting sensor with a limited data and energy buffer is considered. The sensor node observes an unknown temporally correlated field and communicates its observations to a remote fusion center using the energy it harvested. The fusion center employs linear minimum mean-square error (LMMSE) estimation to reconstruct the unknown field. We provide performance guarantees for the estimation error under a block transmission scheme, where at each transmission block, data and energy buffers are completely emptied. Our bounds provide insights into how statistical properties of the energy harvesting process and buffer sizes may affect the estimation error. In particular, these bounds suggest insensitivity of the performance to buffer sizes for signals with low degree of freedom and suggest performance improvements with increasing buffer sizes for signals with relatively higher degree of freedom. Depending only on the mean, variance and finite support of the energy arrival process, these results provide insights for the energy and data buffer sizes for deployment in future energy harvesting wireless sensing systems.

TDMA scheduling schemes targeting high channel utilization for energy‐harvesting wireless sensor networks

“…The drawback of inconvenient replacing battery in the traditional WSNs is remedied by energy-harvesting technique, which brings in various Energy-Harvesting WSNs (EH-WSNs) [1]. Nodes in the EH-WSNs can gain sustainable operation by scavenging energy from ambient energy sources [2][3][4], including radio frequency, sun light, wind, etc.…”