Exploring Performance Tradeoffs in Age-Aware Remote Monitoring with Satellites

2026-02-16

AI Summary

This study addresses the integration of satellite data into a multimodal remote monitoring system comprising terrestrial IoT devices, unmanned aerial vehicles (UAVs), and periodic satellites, specifically under high-latency and low-reliability conditions. The work proposes a graph-based spatial partitioning framework that synergistically combines fixed sensing, mobile coverage, and wide-area observation to optimize information freshness, quantified by weighted Age of Information (AoI). For the first time, it systematically evaluates the value of satellite observations in AoI-aware monitoring. By deriving a closed-form expression and a theoretical lower bound for weighted AoI, and integrating a maximum-weight scheduling policy, the paper reveals the fundamental scheduling dynamics in such heterogeneous multi-source systems. Simulations delineate the utility boundary of satellite data, offering theoretical grounding and actionable insights for real-world deployment.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Security and Privacy: Data transparency and provenance

Abstract

We investigate a remote monitoring framework with multiple sensing modalities including IoT sensors on the ground, mobile UAVs in the air, and a periodically available satellite constellation. While the IoT sensors cover small areas and remain fixed, the UAVs can move between locations and cover larger areas, and the satellites can observe the entire region but have high latency and low reliability. We divide the deployment region into cells and model it as a graph, with the nodes representing individual cells and edges representing possible UAV mobility patterns. To evaluate the freshness of collected information from this graph, we adopt the Age of Information (AoI) metric, measured separately for each cell. Under a given deployment of IoT nodes and UAV mobility patterns, our objective is to ascertain whether the system should actually utilize monitoring updates from satellites - a seemingly simple yet surprisingly elusive question. For stationary randomized scheduling policies, we develop closed-form expressions and lower bounds for the weighted-sum AoI and utilize this analysis to explore performance tradeoffs as system parameters vary. We also provide a Lyapunov style max-weight policy and detailed simulations that provide crucial insights for deploying such systems in practice.

Problem

• Age of Information
• remote monitoring
• satellite
• UAV
• IoT

Innovation

• Age of Information (AoI)
• multi-modal remote monitoring
• satellite-UAV-IoT integration
• max-weight scheduling
• graph-based mobility modeling