Learning Automata-Based Enhancements to RPL: Pioneering Load-Balancing and Traffic Management in IoT

TL;DR

This paper introduces LALARPL, a novel routing protocol enhancement using learning automata to improve load balancing and traffic management in IoT networks, significantly boosting performance and energy efficiency.

Contribution

It presents a new learning automata-based method integrated into RPL to dynamically optimize routing decisions for better load distribution in IoT environments.

Findings

Improved packet delivery rates

Reduced end-to-end delay

Enhanced energy efficiency

Abstract

The Internet of Things (IoT) signifies a revolutionary technological advancement, enhancing various applications through device interconnectivity while introducing significant challenges due to these devices' limited hardware and communication capabilities. To navigate these complexities, the Internet Engineering Task Force (IETF) has tailored the Routing Protocol for Low-Power and Lossy Networks (RPL) to meet the unique demands of IoT environments. However, RPL struggles with traffic congestion and load distribution issues, negatively impacting network performance and reliability. This paper presents a novel enhancement to RPL by integrating learning automata designed to optimize network traffic distribution. This enhanced protocol, the Learning Automata-based Load-Aware RPL (LALARPL), dynamically adjusts routing decisions based on real-time network conditions, achieving more effective load balancing and significantly reducing network congestion. Extensive simulations reveal that this approach outperforms existing methodologies, leading to notable improvements in packet delivery rates, end-to-end delay, and energy efficiency. The findings highlight the potential of our approach to enhance IoT network operations and extend the lifespan of network components. The effectiveness of learning automata in refining routing processes within RPL offers valuable insights that may drive future advancements in IoT networking, aiming for more robust, efficient, and sustainable network architectures.