Machine Learning Applied to LoRaWAN Network for Improving Fingerprint Localization Accuracy in Dense Urban Areas

Piroddi, Andrea; Torregiani, Maurizio

doi:10.3390/network3010010

Cited by 3 publications

(1 citation statement)

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“…In addition, LoRaWAN offers features such as adaptive data rate (ADR) for efficient resource management, bi-directional communication, and strong security, making it a robust and scalable solution for IoT deployments [ 40 ]. With their long range, ultra-low power consumption, and efficient network management, LoRa and LoRaWAN are revolutionizing the IoT landscape, empowering businesses and industries with seamless connectivity and enabling innovative IoT applications across various sectors [ 41 ].…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%