Title: Machine learning approaches for LoRa networks: a survey

Authors: Seham Ibrahem Abd Elkarim; Basem M. ElHalawany; Ola Mohammed Ali; M.M. Elsherbini

Addresses: Department of Electrical Engineering, Faculty of Engineering, Benha University, Shoubra, 11614, Cairo, Egypt ' Department of Electronics and Communication Engineering, College of Science and Technology, Kuwait College, Doha, 35001, Doha, Kuwait; Department of Electrical Engineering, Faculty of Engineering, Benha University, Shoubra, 11614, Cairo, Egypt ' Department of Electrical Engineering, The Egyptian Academy of Engineering and Advanced Technology (EAEAT), El Salam, 11785, Cairo, Egypt ' Department of Electrical Engineering, Faculty of Engineering, Benha University, Shoubra, 11614, Cairo, Egypt; Department of Electrical Engineering, The Egyptian Academy of Engineering and Advanced Technology (EAEAT), El Salam, 11785, Cairo, Egypt

Abstract: 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 meters and traffic analysis. However, LoRa still faces several challenges in terms of reliability and scalability because it has limited number of choices for power and spreading factor (SF). These challenges have encouraged the researchers to use machine learning techniques to propose optimum solutions to these challenges and enhance the performance of LoRa networks. This paper provides an overall survey on using machine learning in LoRa networks and demonstrates the recent solutions to enhance the performance of LoRa network. The purpose of this survey is to encourage greater efforts to improve the performance of LoRa.

Keywords: internet of things; IoT; low-power wide-area network; LPWAN; long-range wide area networks; LoRaWAN.

DOI: 10.1504/IJSCC.2023.133928

International Journal of Systems, Control and Communications, 2023 Vol.14 No.4, pp.357 - 390

Received: 13 Dec 2022
Accepted: 03 May 2023
Published online: 05 Oct 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article