V. Toro-Betancur, G. Premsankar, C. -F. Liu, M. Słabicki, M. Bennis and M. D. Francesco, "Learning How to Configure LoRa Networks With No Regret: A Distributed Approach," in IEEE Transactions on Industrial Informatics, vol. 19, no. 4, pp. 5633-5644, April 2023, doi: 10.1109/TII.2022.3187721
Learning how to configure LoRa networks with no regret : a distributed approach
|Author:||Toro-Betancur, Verónica1; Premsankar, Gopika2; Liu, Chen-Feng3;|
1Department of Computer Science, Aalto University, Espoo, Finland
2Department of Computer Science, University of Helsinki in Helsinki, Helsinki, Finland
3Technology Innovation Institute, Masdar City, Abu Dhabi, United Arab Emirates
4Institute of Theoretical and Applied Informatics, Polish Academy of Sciences in Gliwice, Gliwice, Poland
5Centre of Wireless Communications, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 4.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023040334696
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-04-03
Long range (LoRa) is one of the most popular technologies for low-power wide area networks. It offers long-range communication with a low energy consumption, which makes it ideal for many applications in the Internet of Things. The performance of LoRa networks depends on the communication parameters used by individual nodes. Several works have proposed different solutions, typically running on a central network server, to select these parameters. However, existing approaches have not addressed the need to (re-)assign parameters when channel conditions suddenly vary due to additional traffic, changes in the weather or the presence of obstacles. Moreover, allocation strategies that require a central entity to decide communication parameters do not scale due to the large number of configuration packets that must be sent to the nodes. To address these issues, this article proposes NoReL, a distributed game-theoretic approach that allows nodes to autonomously update their parameters and maximize their packet delivery ratio. NoReL is based on a stochastic variant of no-regret learning, which is proven to reach an ϵ -coarse correlated equilibrium in LoRa networks. Extensive simulations show that NoReL achieves a higher delivery ratio than the state of the art in both static and dynamic environments, with an improvement up to 12%.
IEEE transactions on industrial informatics
|Pages:||5633 - 5644|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the Academy of Finland under Grant 319710, Grant 319758, Grant 326346, and Grant 338854; and in part by the Polish National Center for Research and Development under Grant POIR.04.01.04-00-1414/20.
|Academy of Finland Grant Number:||
319758 (Academy of Finland Funding decision)
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