Optimal Event-Based Policy for Remote Parameter Estimation in Wireless Sensing Architectures Under Resource Constraints

Jerome P Lynch,Katherine A Flanigan

doi:10.1109/twc.2021.3139289

Abstract

Energy is a resource bottleneck in wireless sensing networks (WSNs) relying on energy harvesting for their operations. This is pronounced in WSNs whose data is used for remote parameter estimation because only a subset of the measured information can be transmitted to the estimator. While much attention has been separately paid to communication schemes for energy-aware data transmission in WSNs under resource constraints and controlled parameter estimation, there has yet to emerge a censoring policy that minimizes the variance of a measured process’ estimated component parameters subject to realistic constraints imposed by the WSN. Consequently, this paper presents the derivation of an optimal event-based policy governing data collection and transmission that accounts for energy and data buffer sizes, stochastic models of harvested energy and event arrivals, value of information of measured data, and temporal death. The policy is optimal in the sense that it maximizes the information rate of transmitted data, thereby producing the best possible estimates of the process parameters using the modified maximum likelihood estimation given the system constraints. Experimental and simulation-based results reflect these objectives and illustrate that the framework is robust against significant uncertainty in the initial parameter estimates.

Highlights

T HE proliferation of low-cost and miniature, yet highperforming, sensors has enabled sensing in the natural and built environments across a broad range of applications, including infrastructure, transportation systems, health care, surveillance, industrial control, and environmental systems, to name just a few
This paper proves that the measured process’ parameter component estimates can be reconstructed from the subset of data transmitted according to the optimal policy
It is proven that the modified maximum likelihood estimator (MLE) developed, which accounts for the missingness of data not transmitted, is the maximizer, a Cramer-Rao bound (CRB) on the covariance matrix of the estimator exists, and the MLE is consistent, asymptotically unbiased, and asymptotically normal

Summary

Introduction

T HE proliferation of low-cost and miniature, yet highperforming, sensors has enabled sensing in the natural and built environments across a broad range of applications, including infrastructure, transportation systems, health care (e.g., wearable devices), surveillance, industrial control, and environmental systems, to name just a few. Sensing platforms within these domains have been aided by advancements in wireless communication to yield wireless sensor networks (WSNs) that enjoy low costs, low communication latency, and access to cloud computing services for more sophisticated data analysis.

Objectives

Results

Conclusion