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Optimizing RPL for Load Balancing and Congestion Mitigation in IoT Network

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Abstract

In this study, we address a critical aspect of the routing protocol for low-power and lossy Networks (RPL), namely the selection of the parent node, which plays a pivotal role. As IoT networks expand rapidly, tackling data congestion becomes increasingly crucial. The conventional RPL algorithm, initially designed for smaller networks, lacks mechanisms for balancing loads during parent–child node assignment and does not consider congestion scenarios. To overcome these limitations, we propose a novel objective function (OF) tailored specifically for the RPL algorithm. This OF integrates network load and congestion conditions using an Adaptive fuzzy multi-criteria decision-making approach, combining fuzzy analytic hierarchy process and technique for order of preference by similarity to ideal solution (TOPSIS) techniques. By redefining the process of selecting parent nodes, our approach enhances the efficiency of data transmission, alleviates congestion, and optimizes the performance of IoT networks. Our method introduces a multi-criteria decision-making framework for parent node selection, ensuring that the chosen parent node is both free of congestion and balanced in load, resulting in efficient forwarding of data packets. We prioritize parent node selection based on five crucial criteria, directly addressing load and congestion challenges in IoT networks. Through fuzzy AHP, we determine the relative importance of each criterion, while the TOPSIS method aids in ranking alternatives. This comprehensive approach provides a robust solution to mitigate network congestion, optimize load distribution, and enhance IoT network performance amidst dynamic growth. Implementing the algorithm using Contiki OS and the Cooja simulator, our results demonstrate 0–15% reduction in delay, 20–30% lesser energy consumption, and 10–25% reduction in packet overflow rate while maintaining network throughput by 15% as compare to CQARPL, CAFOR and QHCA and enhancing overall performance.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jaypee Institute of Information Technology (JIIT), Noida, India

    Aastha Maheshwari

  2. School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India

    Karthick Panneerselvam

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  1. Aastha Maheshwari
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  2. Karthick Panneerselvam
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Correspondence to Aastha Maheshwari.

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Maheshwari, A., Panneerselvam, K. Optimizing RPL for Load Balancing and Congestion Mitigation in IoT Network. Wireless Pers Commun 136, 1619–1636 (2024). https://doi.org/10.1007/s11277-024-11346-2

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