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![Fig. 1 The communication protocal and system architecture of LoRaWAN [26]](profile/Basem-Elhalawany/publication/370510570/figure/fig1/AS:11431281157069174@1683703661680/The-communication-protocal-and-system-architecture-of-LoRaWAN-26_Q320.jpg)
![Fig. 2 Long-Range Wide Area Networks (LoRaWAN) architecture [26]](profile/Basem-Elhalawany/publication/370510570/figure/fig2/AS:11431281157049359@1683703661929/Long-Range-Wide-Area-Networks-LoRaWAN-architecture-26_Q320.jpg)
Low-power wide-area networks (LPWANs) are a new
class of wireless communications technology aimed at the Internet
of Things [29]. Long-range wide-area network (LoRa) is a promising LPWAN network standard that allows for long-distance wireless communication while conserving energy. LoRa has been used
in many applications such as healthcare, smart me...
Context in source publication
Context 1
... and system architecture are defined by LoRaWAN as shown in Fig. 1. This protocol is bidirectional, allowing a confirmation packet to be received. Data encryption, device-to-network registration, and multicast data transmission are all supported by LoRaWAN [21]. While LoRa physical layer allows for long-range communication [26] as shown in Fig. ...
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Citations
... There are several studies that evaluate the use of Machine Learning (ML) on LoRaWAN operation from different perspectives, such as: Energy Optimization, Localization, Intelligent Resource Allocation, Mobility Classification of nodes, and Security, among others [1][2][3][4][5][6][7][8][9][10]. The trend is to use artificial neural networks (ANNs), in spite of the fact that RF is deemed robust, flexible, and relatively fast to train compared to ANNs. ...
... The trend is to use artificial neural networks (ANNs), in spite of the fact that RF is deemed robust, flexible, and relatively fast to train compared to ANNs. In this sense, it was found that out of 34 research documents examined, only four of them relied on RF, representing approximately 12% of the total ML-based studies [6]. In addition, a survey was conducted to assess the ML algorithms for estimating path loss in urban environments, being most of them of the ANNs type, and did not consider a network of nodes [11]. ...
... [65] 2020 Performance review of LoRa Discussed the LoRa technology and reviewed performance. [66] 2021 Use of ML in LoRa Surveyed the general issues related to LoRaWAN, overviewed the ML solutions, and highlighted key future research directions. [67] 2021 LoRaWAN optimizations Presented existing solutions in five aspects: coexistence, resource allocation, MAC layer, network planning, and mobility support. ...
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.
