Abstract
LoRaWAN is a low-power wide-area network standard widely used for long-range machine-to-machine communications in the Internet of Things ecosystem. While enabling ultra-low-power communication links, its open nature impulsed exponential market growth in the last years. Given its Aloha-like medium access nature, several scalability-oriented improvements were proposed in the last years, with time-slotted communications having raised special interest. However, how to efficiently allocate resources in a network where the cost of downlink communication is significantly higher than that of the uplink represents a significant challenge. To shed light on this matter, this work proposes the use of multi-agent systems for network planning in time-slotted communications. To do so, a predictive network planning agent is designed and validated as part of an end-to-end multi-agent network management system, which is based on multi-output regression that predicts the resulting network scalability for a given set of joining devices and setup scenarios being considered. A preliminary evaluation of network-status predictions showed a mean absolute error lower than 3% and pointed out different lessons learned, in turn validating the feasibility of the proposed agent for LoRaWAN-oriented network planning.
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Acknowledgments
Grant 2023-GRIN-34056 funded by Universidad de Castilla-La Mancha. Grant 2019-PREDUCLM-10703 funded by Universidad de Castilla-La Mancha and by “ESF Investing in your future”. Grant DIN2018-010177 funded by MCIN/AEI/ 10.13039/501100011033. Grant PID2021-123627OB-C52 funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way to make Europe”.
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Garrido-Hidalgo, C., Fürst, J., Roda-Sanchez, L., Olivares, T., Fernández-Caballero, A. (2023). Lessons Learned on the Design of a Predictive Agent for LoRaWAN Network Planning. In: Mathieu, P., Dignum, F., Novais, P., De la Prieta, F. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection. PAAMS 2023. Lecture Notes in Computer Science(), vol 13955. Springer, Cham. https://doi.org/10.1007/978-3-031-37616-0_8
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DOI: https://doi.org/10.1007/978-3-031-37616-0_8
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