A Survey on LoRaWAN MAC schemes: from conventional solutions to AI-driven protocols

Abstract

Long Range (LoRa) networks have emerged as a vital solution catering to applications requiring coverage over relatively long distances in the world of Internet of Things (IoT), where connectivity and efficient data transmission are paramount. LoRa’s utilization of the unlicensed Industrial, Scientific, and Medical (ISM) band not only underscores its cost-effectiveness but also positions it favorably against licensed technologies in terms of deployment cost. Consequently, LoRa has been used as the underlying communication technology for applications across various industrial IoT scenarios. Despite its immense promise in reshaping IoT connectivity, LoRa does have some shortcomings and challenges that the research community has yet to address to unleash its full potential. These limitations have triggered substantial attention from diverse entities, including research institutions, organizations, and industry stakeholders. This paper reviews the literature aimed at improving the capacity and scalability of LoRa networks specifically at the Medium Access Control (MAC) and data link layers. Unlike other surveys, this study focuses on these layers because they play a pivotal role in managing collision rates, which significantly impact network scalability. The paper suggests a comprehensive review of the literature, organizing it based on key limitations that could hinder the network’s ability to meet its performance objectives, including scalability, Packet Delivery Ratio (PDR), and energy efficiency. In addition, the paper provides a summary of these research efforts and offers insight into potential directions for future research in this area