Nature inspired optimization of IoT network for delay resistant and energy efficient applications

Area of Science:

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

Background:

• LoRaWAN is a popular open standard for IoT applications, offering low power, long range, and scalability.
• However, LoRaWAN's duty cycle limitations and increasing device density degrade network performance, hindering low-latency and long-lifetime applications.
• Existing approaches struggle to balance network efficiency with device constraints.

Purpose of the Study:

• To propose a nature-inspired MAC algorithm optimizing LoRaWAN duty cycles using the golden ratio (GR) approach.
• To enhance network performance for IoT applications requiring low latency and extended operational life.
• To validate the proposed algorithm's efficacy against existing methods and PSO algorithms.

Main Methods:

• Development of a novel low duty cycle MAC algorithm incorporating the golden ratio (GR) concept.
• Optimization of the duty cycle within the LoRaWAN framework.
• Comparative performance analysis using Particle Swarm Optimization (PSO) algorithms and DC constraint approaches.

Main Results:

• The proposed GR-based algorithm significantly reduces network latency by 26%.
• Power consumption is decreased by 12% compared to the DC constraint approach.
• Network lifetime is extended by 14% through optimized duty cycle management.

Conclusions:

• The proposed golden ratio-based MAC algorithm effectively optimizes LoRaWAN duty cycles for improved network performance.
• This approach offers a viable solution for IoT deployments demanding low latency and extended network longevity.
• The GR algorithm demonstrates superior performance over PSO and DC constraint methods in key metrics.