RPL-eSOA: Enhancing IoT network sustainability with RPL and enhanced sandpiper optimization algorithm
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.3.31Keywords:
Cluster Head Selection, Dynamic Optimization Algorithm, Internet of Things, Network Lifetime ExtensionDimensions Badge
Issue
Section
License
Copyright (c) 2024 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The internet of things (IoT) encompasses extensive networks of interconnected devices, playing a crucial role in various applications. However, managing these networks presents significant challenges, particularly in cluster head selection, which is critical for energy efficiency and sustainability. To eradicate these challenges, this paper combines the capability of routing protocol for low-power and lossy networks (RPL) with an enhanced sandpiper optimization algorithm (e-SOA) to dynamically optimize network configurations. This combination, termed RPL-eSOA, improves energy management and extends network longevity while maintaining robust communication pathways. Through simulation and comparative analysis, RPL-eSOA demonstrates superior performance in enhancing network lifetime and operational efficiency compared to traditional methods. It achieved a 100% packet delivery ratio (PDR) and significantly reduced latency to 475 ms.Abstract
How to Cite
Downloads
Similar Articles
- Mohamed Azharudheen A, Vijayalakshmi V, Improvement of data analysis and protection using novel privacy-preserving methods for big data application , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- J. Fathima Fouzia, M. Mohamed Surputheen, M. Rajakumar, Hybrid pigeon optimization-based feature selection and modified multi-class semantic segmentation for skin cancer detection (HPO-MMSS) , The Scientific Temper: Vol. 16 No. 05 (2025): The Scientific Temper
- G. Chitra, Hari Ganesh S., Cultural algorithm based principal component analysis (CA-PCA) approach for handling high dimensional data , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Josephine Theresa S, Graph Neural Network Ensemble with Particle Swarm Optimization for Privacy-Preserving Thermal Comfort Prediction , The Scientific Temper: Vol. 16 No. 12 (2025): The Scientific Temper
- C. Muruganandam, V. Maniraj, A Self-driven dual reinforcement model with meta heuristic framework to conquer the iot based clustering to enhance agriculture production , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- R. Prabhu, P. Archana, S. Anusooya, P. Anuradha, Improved Steganography for IoT Network Node Data Security Promoting Secure Data Transmission using Generative Adversarial Networks , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Shaik Chanbasha, N. Jayakumar, N. Bupesh Kumar, A self-regulating optimization algorithm for locating and sizing a local power generation source for a radial structured distribution system in deregulated environment , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- S V Arulvani, Dr. C. Jayanthi, Logistic Elitist Liquid Neural Network For Student Dropout Prediction , The Scientific Temper: Vol. 17 No. 02 (2026): The Scientific Temper
- R.R. Jenifer, V.S.J. Prakash, Detecting denial of sleep attacks by analysis of wireless sensor networks and the Internet of Things , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- D. Prabakar, Santhosh Kumar D.R., R.S. Kumar, Chitra M., Somasundaram K., S.D.P. Ragavendiran, Narayan K. Vyas, Task offloading and trajectory control techniques in unmanned aerial vehicles with Internet of Things – An exhaustive review , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.

