Ergodicity-Informed Adaptive Sensing for Energy-Constrained Urban IoT Networks
Name
Ergodicity_Informed_Adaptive_Sensing_for_Energy_Constrained_Urban_IoT_Networks[4].pdf
Description
Accepted version
Size
9.8 MB
Format
Adobe PDF
Checksum (MD5)
328f70679e11f6955b5df0f956f04b26
Name
Supplementary.pdf
Description
Supplementary materials
Size
257.89 KB
Format
Adobe PDF
Checksum (MD5)
6711320686900c82f86d4d60bbafdf4f
Author(s)
Ariss, Mayar
Oyinlola, Oluwatobi
Mora, Simone
Santi, Paolo
Ratti, Carlo
Date Issued
December 15, 2025
Journal
IEEE Internet of Things Journal
Publisher
Institute of Electrical and Electronics Engineers
Citation
M. Ariss, O. Oyinlola, S. Mora, P. Santi and C. Ratti, "Ergodicity-Informed Adaptive Sensing for Energy-Constrained Urban IoT Networks," in IEEE Internet of Things Journal, vol. 12, no. 24, pp. 54804-54815, 15 Dec.15, 2025,.
Version
Author's final manuscript
Abstract
Urban environments pose invisible yet pervasive health hazards, including exposure to air pollutants, noise, and temperature extremes. Monitoring these hazards requires high-resolution data to identify hot spots and support timely interventions. However, conventional stationary monitoring networks lack spatial resolution, while mobile drive-by sensing systems suffer from uneven data distribution and energy constraints. Here, we show that an ergodicity-informed adaptive sensing framework can significantly improve both the coverage uniformity and energy efficiency of mobile environmental monitoring in cities. Using solar-powered sensors mounted on taxis in Stockholm, Sweden, our system regulates sensor activation based on recent sampling history and air quality guidelines, reducing redundant measurements in oversampled street segments, resulting in a natural redistribution of sensing counts toward undersampled regions. Drawing from the ergodic properties of urban mobility, we formally demonstrate—through the random waypoint (RWP) model—that temporally aggregated sensor data converge to a stable spatial distribution governed by long-term mobility patterns. Our approach achieves a 10.74% increase in energy efficiency and 72.14% reduction in spatial variability of sensing coverage. Validation through statistical tests confirms significantly improved uniformity, and a clustering optimization revealed coherent regions of coverage enhancement. These findings position adaptive sensing as a scalable and theoretically sound solution for equitable, resource-efficient environmental monitoring in complex urban systems.
MIT Department
Senseable City Laboratory
Terms of Use
Creative Commons Attribution-NonCommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/jiot.2025.3621047
