Article
Version 1
This version is not peer-reviewed
Learning Automata-Based Enhancements to RPL: Pioneering Load-Balancing and Traffic Management in IoT
Version 1
: Received: 16 July 2024 / Approved: 17 July 2024 / Online: 17 July 2024 (13:52:31 CEST)
How to cite: Homaei, M. H. Learning Automata-Based Enhancements to RPL: Pioneering Load-Balancing and Traffic Management in IoT. Preprints 2024, 2024071396. https://doi.org/10.20944/preprints202407.1396.v1 Homaei, M. H. Learning Automata-Based Enhancements to RPL: Pioneering Load-Balancing and Traffic Management in IoT. Preprints 2024, 2024071396. https://doi.org/10.20944/preprints202407.1396.v1
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.
Keywords
Internet of Things; RPL; Load Balancing; Routing; Congestion Control
Subject
Computer Science and Mathematics, Computer Networks and Communications
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment
