Enhanced LoRaWAN Adaptive Data Rate for Mobile Internet of Things Devices

Area of Science:

• Wireless Communication
• Internet of Things (IoT)
• Network Protocols

Background:

• Long-Range Wide Area Network (LoRaWAN) is a key technology for IoT applications.
• Network Server (NS)-managed Adaptive Data Rate (ADR) optimizes Spreading Factor (SF) and Transmit Power (TP) for end devices (EDs).
• Current ADR lacks agility, leading to slow convergence (hours/days) in variable channel conditions.

Purpose of the Study:

• To propose novel NS-managed ADR schemes to accelerate convergence.
• To enhance communication reliability and energy efficiency in LoRaWAN.
• To address the limitations of existing ADR mechanisms in dynamic environments.

Main Methods:

• Introduced Gaussian filter-based ADR (G-ADR) and Exponential Moving Average-based ADR (EMA-ADR).
• Both G-ADR and EMA-ADR function as low-pass filters to mitigate signal-to-noise ratio fluctuations.
• Evaluated schemes in static and mobility scenarios for LoRaWAN confirmed mode.

Main Results:

• G-ADR reduced convergence time by 16% (static) and 17% (mobility) compared to typical ADR.
• EMA-ADR reduced convergence time by 68% (static) and 81% (mobility) compared to typical ADR.
• Proposed schemes demonstrated reduced energy consumption and improved packet success ratios.

Conclusions:

• G-ADR and EMA-ADR offer significant improvements in LoRaWAN ADR convergence speed.
• These methods enhance the performance of IoT applications by optimizing resource allocation.
• The proposed filters provide a more agile and efficient approach to managing LoRaWAN communication parameters.