Age-Aware Status Update Control for Energy Harvesting IoT Sensors via Reinforcement Learning

We consider an IoT sensing network with multiple users, multiple energy harvesting sensors, and a wireless edge node acting as a gateway between the users and sensors. The users request for updates about the value of physical processes, each of which is measured by one sensor. The edge node has a cache storage that stores the most recently received measurements from each sensor. Upon receiving a request, the edge node can either command the corresponding sensor to send a status update, or use the data in the cache. We aim to find the best action of the edge node to minimize the average long-term cost which trade-offs between the age of information and energy consumption. We propose a practical reinforcement learning approach that finds an optimal policy without knowing the exact battery levels of the sensors. Simulation results show that the proposed method significantly reduces the average cost compared to several baseline methods.

Authors:
Hatami Mohammad, Jahandideh Mojtaba, Leinonen Markus, Codreanu Marian

Publication type:
A4 Article in conference proceedings

Place of publication:
2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications

Keywords:
6G Publication

Published:
8 October 2020

Full citation:
M. Hatami, M. Jahandideh, M. Leinonen and M. Codreanu, “Age-Aware Status Update Control for Energy Harvesting IoT Sensors via Reinforcement Learning,” 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications, London, United Kingdom, 2020, pp. 1-6, doi: 10.1109/PIMRC48278.2020.9217302

DOI:
https://doi.org/10.1109/PIMRC48278.2020.9217302

Read the publication here:
http://urn.fi/urn:nbn:fi-fe2020111992015