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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.
Sensors (Basel, Switzerland)
|September 9, 2020
Summary
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.
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.
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