Lessons Learned on the Design of a Predictive Agent for LoRaWAN Network Planning

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

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

Cited by 1 publication

(1 citation statement)

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“…Paper [ 178 ] proposed a multi-agent regression model to improve network planning in time-slotted communications for LoRaWAN. The proposed agent is based on multi-output regression responsible for predicting the network scalability for a given set of joining EDs.…”

Section: Lorawan Meets MLmentioning

confidence: 99%

LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine Learning

Farhad

Pyun

2023

Sensors

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The Internet of Things is rapidly growing with the demand for low-power, long-range wireless communication technologies. Long Range Wide Area Network (LoRaWAN) is one such technology that has gained significant attention in recent years due to its ability to provide long-range communication with low power consumption. One of the main issues in LoRaWAN is the efficient utilization of radio resources (e.g., spreading factor and transmission power) by the end devices. To solve the resource allocation issue, machine learning (ML) methods have been used to improve the LoRaWAN network performance. The primary aim of this survey paper is to study and examine the issue of resource management in LoRaWAN that has been resolved through state-of-the-art ML methods. Further, this survey presents the publicly available LoRaWAN frameworks that could be utilized for dataset collection, discusses the required features for efficient resource management with suggested ML methods, and highlights the existing publicly available datasets. The survey also explores and evaluates the Network Simulator-3-based ML frameworks that can be leveraged for efficient resource management. Finally, future recommendations regarding the applicability of the ML applications for resource management in LoRaWAN are illustrated, providing a comprehensive guide for researchers and practitioners interested in applying ML to improve the performance of the LoRaWAN network.

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Section: Lorawan Meets MLmentioning

confidence: 99%