Contention-Aware Adaptive Data Rate for Throughput Optimization in LoRaWAN

Area of Science:

• Wireless communication networks
• Internet of Things (IoT) protocols
• Network performance optimization

Background:

• Long Range Wide Area Network (LoRaWAN) devices adjust data rates via spreading factors to optimize throughput.
• Current adaptive data rate methods in LoRaWAN primarily rely on link quality, neglecting device contention.
• Increased spreading factor usage by multiple devices elevates collision probability, reducing overall network throughput.

Purpose of the Study:

• To design a contention-aware adaptive data rate mechanism for LoRaWAN to enhance throughput.
• To address the limitations of link-quality-only criteria for data rate adjustment.
• To optimize LoRaWAN system performance by considering device-to-device collisions.

Main Methods:

• Formulating the data rate optimization as a constrained optimization problem by limiting devices per data rate.
• Employing the gradient projection method to find the optimal data rate allocation.
• Implementing an adaptive data rate adjustment strategy based on the derived optimal solution.

Main Results:

• The proposed contention-aware method significantly improves system performance compared to existing approaches.
• The method demonstrates effectiveness across various numbers of devices in the network.
• Achieved throughput closely approximates the theoretical upper bound, validating the optimization strategy.

Conclusions:

• Contention-aware adaptive data rate is crucial for maximizing LoRaWAN throughput.
• The gradient projection method provides an effective solution for this constrained optimization problem.
• The proposed approach offers a significant advancement in LoRaWAN network efficiency and performance.