Sensors coordinate transmission to improve remote estimation accuracy
Resource-Constrained Semantic-Aware Remote Estimation with Overlapping Sensor Coverage
Information Theory
Summary
This paper tackles the problem of how multiple sensors with overlapping coverage can send data efficiently to estimate certain processes remotely. The authors consider limitations like how often each sensor can send data, delays, and reliability. They develop a mathematical method to decide which sensor should send updates and when, to best reduce errors in estimating the monitored processes. Their approach also describes how to handle constraints on overall and per-sensor transmissions, often requiring mixing between several strategies. Their results help explain the value of coordinated sensor scheduling under resource limits.
What this means in practice
- •For network schedulers: Optimize sensor uplink schedules in wireless networks with overlapping coverage to minimize estimation errors under transmission limits.
- •For industrial automation teams: Improve remote monitoring accuracy in plants where multiple sensors observe the same processes but have different communication constraints.
Authors
Bowen Sun, Nikolaos Pappas
Abstract
We study semantic-aware remote estimation of multiple finite-state Markov sources observed by K sensors with overlapping coverage. The sensors share a time-division multiple-access uplink and differ in transmission reliability, delivery delay, and transmission budget. In each slot, the scheduler jointly selects a source and one of its monitoring sensors, or remains idle, to minimize the long-run average cost of actuation error subject to global and per-sensor transmission-frequency constraints. We formulate this problem as a finite average-cost constrained Markov decision process. We show that the transmission resource functions have rank at most K, although the global constraint may still restrict the feasible region. Consequently, the Lagrangian depends only on K effective transmission costs, and an optimal constrained solution can be represented using at most K+1 deterministic policy-recurrent-class components. We further characterize the piecewise-affine concave Lagrangian value function and derive projected dual subgradient ascent over an explicitly bounded multiplier set. Numerical results illustrate the value-function structure, the need for policy randomization in a representative instance, and the interaction between global and per-sensor transmission budgets.