Is LoRaWAN Really Wide? Fine-grained LoRa Link-level Measurement in An Urban Environment

Ren, Yidong; Liu, Li; Li, Chenning; Cao, Zhichao; Chen, Shigang

doi:10.1109/icnp55882.2022.9940375

Cited by 15 publications

(1 citation statement)

References 33 publications

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“…The dataset in [ 230 , 231 ] was generated using two GW and six mobile EDs in a 6 × 6

urban area in Tsinghua University, Beijing, China. The dataset [ 232 ] was collected over four months comprising RSSI, PDR of the transmitted packets, locations of the ED and GW, and timestamps of each measurement.…”

Section: Lorawan Datasetsmentioning

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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“…The dataset in [ 230 , 231 ] was generated using two GW and six mobile EDs in a 6 × 6

Section: Lorawan Datasetsmentioning

confidence: 99%