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Field Application of Global Positioning System01:28

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A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges.

Rachel Kufakunesu1, Gerhard P Hancke1,2, Adnan M Abu-Mahfouz1,3

  • 1Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0002, South Africa.

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This review examines Adaptive Data Rate (ADR) algorithms for Long-Range Wide Area Networks (LoRaWAN). It analyzes various schemes to improve Internet of Things (IoT) performance, addressing challenges in LoRaWAN rate adaptation.

Keywords:
Internet of thingsLPWANLoRaLoRaWANadaptive data ratealgorithm

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Area of Science:

  • Wireless Communication Networks
  • Internet of Things (IoT)
  • Network Optimization

Background:

  • Long-Range Wide Area Network (LoRaWAN) is a key technology for Low Power Wide Area Networks (LPWAN) in IoT.
  • LoRaWAN aims to optimize battery life, capacity, range, and cost.
  • Adaptive Data Rate (ADR) is crucial for optimizing data rate, airtime, and energy consumption in LoRaWAN.

Purpose of the Study:

  • To provide a comprehensive review of research on ADR algorithms for LoRaWAN.
  • To analyze existing ADR schemes and their suitability for diverse IoT applications and conditions.
  • To identify research gaps and future directions in LoRaWAN ADR optimization.

Main Methods:

  • Literature review of LoRaWAN network performance.
  • Focus on recent ADR solutions for throughput, energy efficiency, and scalability.
  • Distinguishing, highlighting strengths/drawbacks, and comparing different ADR approaches.

Main Results:

  • Numerous ADR schemes exist due to the lack of a standardized LoRaWAN rate adaptation command.
  • These schemes present challenges in reliability and suitability across various IoT deployments.
  • The review categorizes and compares existing ADR strategies.

Conclusions:

  • The diversity of ADR schemes highlights the need for standardized or more robust solutions.
  • Further research is needed to address reliability and suitability challenges.
  • Future directions include optimizing ADR for enhanced LoRaWAN performance and scalability.