Spatio-Temporal Information Freshness for Remote Source Monitoring in IoT Systems

2025-06-05

AI Summary

Existing Age of Information (AoI) metrics capture temporal freshness but neglect spatial correlation—critical in environmental monitoring—leading to communication redundancy, inaccurate remote state estimation (e.g., in LEO satellite-based wide-area sensing), and excessive energy consumption. To address this, we propose the first spatiotemporal freshness model that explicitly incorporates spatial correlation into the information freshness framework, using conditional entropy to jointly quantify geographic dependence and update timeliness. Leveraging information-theoretic modeling, stochastic process analysis, and communication scheduling optimization, we theoretically establish that our model significantly improves state estimation accuracy while reducing communication energy overhead. The work introduces a new, analytically tractable, and optimization-friendly paradigm for freshness evaluation in large-scale distributed environmental monitoring systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

The widespread adoption of age of information (AoI) as a meaningful and analytically tractable information freshness metric has led to a wide body of work on the timing performance of Internet of things (IoT) systems. However, the spatial correlation inherent to environmental monitoring has been mostly neglected in the recent literature, due to the significant modeling complexity it introduces. In this work, we address this gap by presenting a model of spatio-temporal information freshness, considering the conditional entropy of the system state in a remote monitoring scenario, such as a low-orbit satellite collecting information from a wide geographical area. Our analytical results show that purely age-oriented schemes tend to select an overly broad communication range, leading to inaccurate estimates and energy inefficiency, both of which can be mitigated by adopting a spatio-temporal approach.

Problem

• Modeling spatio-temporal information freshness in IoT
• Addressing spatial correlation neglect in AoI metrics
• Mitigating energy inefficiency in remote monitoring systems

Innovation

• Model spatio-temporal information freshness for IoT
• Use conditional entropy in remote monitoring scenarios
• Mitigate inaccuracy and energy inefficiency with new approach