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
- S. Srinithiya, K. Menaka, Optimized Hybrid Feature Selection Techniques for Detecting Iron Deficiency Anemia , The Scientific Temper: Vol. 16 No. 12 (2025): The Scientific Temper
- P. Vinnarasi, K. Menaka, Advanced hybrid feature selection techniques for analyzing the relationship between 25-OHD and TSH , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- R. Rita Jenifer, V. Sinthu Janita, Energy-aware Security Optimized Elliptic Curve Digital Signature Algorithm for Universal IoT Networks , The Scientific Temper: Vol. 16 No. 09 (2025): The Scientific Temper
- Pravin P. Adivarekar1, Amarnath Prabhakaran A, Sukhwinder Sharma, Divya P, Muniyandy Elangovan, Ravi Rastogi, Automated machine learning and neural architecture optimization , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- M. Prabhu, A. Chandrabose, Optimization based energy aware scheduling in wireless sensor networks , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- V. Seethala Devi, N. Vanjulavalli, K. Sujith, R. Surendiran, A metaheuristic optimisation algorithm-based optimal feature subset strategy that enhances the machine learning algorithm’s classifier performance , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- V Babydeepa, K. Sindhu, A hybrid feature selection and generative adversarial network for lung and uterus cancer prediction with big data , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- M. Ragul, A. Aloysius, V. Arul Kumar, Enhancing IoT blockchain scalability through the eepos consensus algorithm , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- S. Hemalatha, N. Vanjulavalli, K. Sujith, R. Surendiran, Chaotic-based optimization, based feature selection with shallow neural network technique for effective identification of intrusion detection , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- 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
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.

