Exploring Performance Tradeoffs in Age-Aware Remote Monitoring with Satellites

TL;DR

This paper analyzes a multi-modal remote monitoring system combining ground IoT sensors, UAVs, and satellites, focusing on optimizing information freshness and understanding performance tradeoffs through mathematical modeling and simulations.

Contribution

It introduces a framework for evaluating Age of Information in a hybrid satellite-UAV-IoT system and develops policies to optimize monitoring performance.

Findings

Closed-form expressions for AoI under randomized policies

Performance tradeoff insights as system parameters change

Simulation results guiding practical deployment strategies

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.