Lessons learned on the design of a predictive agent for LoRaWAN network planning

Garrido-Hidalgo, Celia; Fürst, Jonathan; Roda-Sanchez, Luis; Olivares, Teresa; Fernández-Caballero, Antonio

doi:10.1007/978-3-031-37616-0_8

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-28544

Full metadata record

DC Field	Value	Language
dc.contributor.author	Garrido-Hidalgo, Celia	-
dc.contributor.author	Fürst, Jonathan	-
dc.contributor.author	Roda-Sanchez, Luis	-
dc.contributor.author	Olivares, Teresa	-
dc.contributor.author	Fernández-Caballero, Antonio	-
dc.date.accessioned	2023-08-30T14:42:49Z	-
dc.date.available	2023-08-30T14:42:49Z	-
dc.date.issued	2023-07-12	-
dc.identifier.isbn	978-3-031-37615-3	de_CH
dc.identifier.isbn	978-3-031-37616-0	de_CH
dc.identifier.issn	0302-9743	de_CH
dc.identifier.issn	1611-3349	de_CH
dc.identifier.uri	https://digitalcollection.zhaw.ch/handle/11475/28544	-
dc.description.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.	de_CH
dc.language.iso	en	de_CH
dc.publisher	Springer	de_CH
dc.relation.ispartofseries	Lecture Notes in Computer Science	de_CH
dc.rights	Licence according to publishing contract	de_CH
dc.subject	Multi-agent system (MAS)	de_CH
dc.subject	LoRaWAN	de_CH
dc.subject	Network prediction	de_CH
dc.subject	Scheduling	de_CH
dc.subject.ddc	004: Informatik	de_CH
dc.title	Lessons learned on the design of a predictive agent for LoRaWAN network planning	de_CH
dc.type	Konferenz: Paper	de_CH
dcterms.type	Text	de_CH
zhaw.departement	School of Engineering	de_CH
zhaw.organisationalunit	Institut für Informatik (InIT)	de_CH
zhaw.publisher.place	Cham	de_CH
dc.identifier.doi	10.1007/978-3-031-37616-0_8	de_CH
dc.identifier.doi	10.21256/zhaw-28544	-
zhaw.conference.details	21st International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS), Guimarães, Portugal, 12th-14th July 2023	de_CH
zhaw.funding.eu	No	de_CH
zhaw.originated.zhaw	Yes	de_CH
zhaw.publication.status	acceptedVersion	de_CH
zhaw.series.number	13955	de_CH
zhaw.volume	13955	de_CH
zhaw.embargo.end	2024-07-12	de_CH
zhaw.publication.review	Peer review (Publikation)	de_CH
zhaw.title.proceedings	Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics : the PAAMS Collection	de_CH
zhaw.webfeed	Intelligent Information Systems	de_CH
zhaw.author.additional	No	de_CH
zhaw.display.portrait	Yes	de_CH
Appears in collections:	Publikationen School of Engineering

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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 [Conference paper]. Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics : The PAAMS Collection, 13955. https://doi.org/10.1007/978-3-031-37616-0_8

Garrido-Hidalgo, C. et al. (2023) ‘Lessons learned on the design of a predictive agent for LoRaWAN network planning’, in Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics : the PAAMS Collection. Cham: Springer. Available at: https://doi.org/10.1007/978-3-031-37616-0_8.

C. Garrido-Hidalgo, J. Fürst, L. Roda-Sanchez, T. Olivares, and A. Fernández-Caballero, “Lessons learned on the design of a predictive agent for LoRaWAN network planning,” in Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics : the PAAMS Collection, Jul. 2023, vol. 13955. doi: 10.1007/978-3-031-37616-0_8.

GARRIDO-HIDALGO, Celia, Jonathan FÜRST, Luis RODA-SANCHEZ, Teresa OLIVARES und Antonio FERNÁNDEZ-CABALLERO, 2023. Lessons learned on the design of a predictive agent for LoRaWAN network planning. In: Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics : the PAAMS Collection. Conference paper. Cham: Springer. 12 Juli 2023. ISBN 978-3-031-37615-3

Garrido-Hidalgo, Celia, Jonathan Fürst, Luis Roda-Sanchez, Teresa Olivares, and Antonio Fernández-Caballero. 2023. “Lessons Learned on the Design of a Predictive Agent for LoRaWAN Network Planning.” Conference paper. In Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics : The PAAMS Collection. Vol. 13955. Cham: Springer. https://doi.org/10.1007/978-3-031-37616-0_8.

Garrido-Hidalgo, Celia, et al. “Lessons Learned on the Design of a Predictive Agent for LoRaWAN Network Planning.” Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics : The PAAMS Collection, vol. 13955, Springer, 2023, https://doi.org/10.1007/978-3-031-37616-0_8.