Exploring Performance Tradeoffs in Age-Aware Remote Monitoring with Satellites

TL;DR

This work tackles timely information gathering in a heterogeneous remote-monitoring system comprising IoT sensors, mobile UAVs, and intermittently available satellites. Modeling the region as a graph and using AoI as the freshness metric per cell, the authors derive a universal lower bound on weighted AoI, analyze stationary randomized schedules, and introduce a Lyapunov-based max-weight policy to manage multi-packet updates under stochastic satellite availability. Key contributions include closed-form EWSPAoI expressions for both satellite-free and satellite-assisted regimes, a decision boundary concept for satellite usefulness, and a scalable max-weight framework with throughput-targeting to ensure stability. The results offer practical design guidance for deploying and scheduling heterogeneous sensing modalities in large-scale remote monitoring with intermittent satellite coverage, supported by simulations with idealized and trace-driven satellite models.

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.

Figures (2)

• Figure 1: System model for satellite-assisted remote monitoring we consider in this paper, consisting of ground IoT sensors, mobile UAVs, and a satellite constellation.
• Figure 2: Age evolution at cell $\omega_m$.