Remote Estimation under Energy-Aware Multisensor Admission Control Over Unreliable Channels

Abstract

We study remote state estimation in wireless networked control systems with energy-constrained sensors sharing an unreliable channel. Unlike traditional approaches assuming fixed sensor participation, we introduce a population-based framework for dynamic sensor admission control, modeling active, idle, and depleted sensors and accounting for collision-induced packet losses. We formulate a finite-horizon optimal control problem, solved analytically for a shared channel with constant reliability via a linear-quadratic admission policy, highlighting trade-offs between communication effort and estimation accuracy. When multiple sensors transmit simultaneously, collisions increase packet error probability. Motivated by this, we numerically derive an optimal admission policy to show how adaptive admission control improves estimation under energy constraints. Simulations demonstrate that the framework balances estimation accuracy, communication effort, and energy consumption, prolonging sensor lifetime.