On the Value of Base Station Motion Knowledge for Goal-Oriented Remote Monitoring with Energy-Harvesting Sensors

2026-02-19

AI Summary

This work addresses the challenge of goal-oriented remote monitoring of an unobservable Markov source by mobile receivers—such as LEO satellites or drones—operating under time-varying channels and energy constraints. The channel is modeled as a finite-state Markov process, and the problem is formulated as a partially observable Markov decision process (POMDP), which is then transformed into a belief-state MDP. An optimal sampling and transmission policy is derived via relative value iteration, supporting both maximum-likelihood and minimum mean-square distortion estimation objectives. Notably, the receiver’s mobility is explicitly incorporated into the communication optimization framework, departing from conventional assumptions of static base stations. Experimental results demonstrate that leveraging knowledge of receiver motion yields 10%–42% lower average distortion compared to baseline approaches that ignore mobility or assume stationary channels, underscoring its critical role in enhancing monitoring performance.

Technology Category

• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper investigates goal-oriented remote monitoring of an unobservable Markov source using energy-harvesting sensors that communicate with a mobile receiver, such as a Low Earth Orbit (LEO) satellite or Unmanned Aerial Vehicle (UAV). Unlike conventional systems that assume stationary base stations, the proposed framework explicitly accounts for receiver mobility, which induces time-varying channel characteristics modeled as a finite-state Markov process. The remote monitoring problem is formulated as a partially observable Markov decision process (POMDP), which is transformed into a tractable belief-state MDP and solved using relative value iteration to obtain optimal sampling and transmission policies. Two estimation strategies are considered: Maximum Likelihood (ML) and Minimum Mean Distortion (MMD). Numerical results demonstrate that incorporating receiver mobility and channel state information into the optimization reduces the average distortion by 10% to 42% compared to baseline policies and constant-channel assumptions, highlighting the importance of base station motion knowledge for effective goal-oriented communication.

Problem

• goal-oriented remote monitoring
• energy-harvesting sensors
• mobile receiver
• time-varying channel
• base station motion

Innovation

• receiver mobility
• goal-oriented communication
• energy-harvesting sensors
• POMDP
• time-varying channel