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
- G Vanitha, M Kasthuri, A robust feature selection approach for high-dimensional medical data classification using enhanced correlation attribute evaluation , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- Rajeev P. R., K. Aravinthan, A novel approach for metrics-based software defect prediction using genetic algorithm , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Syed Amin Jameel, Abdul Rahim Mohamed Shanavas, Deep-Ultranet: Diabetic Retinopathy Grading System Using Ultra-Widefield Retinal Images , The Scientific Temper: Vol. 16 No. 12 (2025): The Scientific Temper
- B. S. E. Zoraida, J. Jasmine Christina Magdalene, Smart grid precision: Evaluating machine learning models for forecasting of energy consumption from a smart grid , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Raja Selvaraj, Manikandasaran S Sundaram, ECM: Enhanced confidentiality method to ensure the secure migration of data in VM to cloud environment , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Abhishek Pandey, V Ramesh, Puneet Mittal, Suruthi, Muniyandy Elangovan, G.Deepa, Exploring advancements in deep learning for natural language processing tasks , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- S. Sindhu, L. Arockiam, A lightweight selective stacking framework for IoT crop recommendation , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- C. Premila Rosy, Clustering of cancer text documents in the medical field using machine learning heuristics , The Scientific Temper: Vol. 16 No. 05 (2025): The Scientific Temper
- J. M. Aslam, K. M. Kumar, Enhancing security of cloud using static IP techniques , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Temesgen A. Asfaw, Batch size impact on enset leaf disease detection , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
<< < 11 12 13 14 15 16 17 18 19 20 > >>
You may also start an advanced similarity search for this article.

