A Decentralized Dynamic Relaying-Based Framework for Enhancing LoRa Networks Performance


Haif H., Arous A., ARSLAN H.

IEEE Internet of Things Journal, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Publication Date: 2024
  • Doi Number: 10.1109/jiot.2024.3379568
  • Journal Name: IEEE Internet of Things Journal
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Compendex, INSPEC
  • Keywords: genetic algorithm, Internet of Things, LoRa, LoRaWAN, Relays, relays, Reliability, Resource management, scalability, Sensors, Signal to noise ratio, spreading factor (SF), time-on-air, Topology
  • Istanbul Medipol University Affiliated: Yes

Abstract

Long-Range (LoRa) technology holds tremendous potential for regulating and coordinating communication among Internet-of-Things (IoT) devices due to its low power consumption and cost-effectiveness. However, LoRa faces significant obstacles such as reduction in coverage area, a high packet drop ratio (PDR), and an increased likelihood of collisions, all of which result in substandard data rates. In this paper, we present a novel approach that employs a relaying node capable of allocating resources dynamically based on signal parameters. In particular, the geometric placement of the relay node is determined by a genetic algorithm that maximizes signal-to-noise ratio (SNR) and signal-to-interference ratio (SIR) success probabilities. Using equal-area based (EAB) spreading factor (SF) distance allocation scheme, the coverage area is sliced into distinct regions in order to derive the success probabilities for different communication stages. Furthermore, we present a frequency channel shuffling algorithm to prevent collisions between end devices (EDs) without increasing the complexity of the relaying nodes. Through extensive simulations, we demonstrate that our proposed scheme effectively expands the coverage area, conserves transmission resources, and enhances the system’s throughput. Specifically, our approach extends the range by up to 40%, increases the throughput by up to 50% compared to conventional methods, and achieves a 40% increase in success probability. To validate the practicality of our approach, we implement our algorithm in an active LoRa network utilizing an ESP32 LoRa SX1276 module, showcasing its compatibility in real-world scenarios.