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
Age-aware status update control for energy harvesting IoT sensors via reinforcement learning
|Author:||Hatami, Mohammad1; Jahandideh, Mojtaba1; Leinonen, Markus1;|
1Centre for Wireless Communications, University of Oulu, Finland
2Department of Science and Technology, Link¨oping University, Sweden
|Online Access:||PDF Full Text (PDF, 0.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020111992015
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-11-19
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.
IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops
|Pages:||1 - 6|
2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications
IEEE International Symposium on Personal, Indoor and Mobile Radio Communications
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This research has been financially supported by the Infotech Oulu, the Academy of Finland (grant 323698), and Academy of Finland 6Genesis Flagship (grant 318927). The work of M. Leinonen has also been financially supported in part by the Academy of Finland (grant 319485). M. Codreanu would like to acknowledge the support of the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 793402 (COMPRESS NETS).
|Academy of Finland Grant Number:||
323698 (Academy of Finland Funding decision)
318927 (Academy of Finland Funding decision)
319485 (Academy of Finland Funding decision)
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