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
- R. Sakthiraman, L. Arockiam, RRFSE: RNN biased random forest and SVM ensemble for RPL DDoS in IoT-WSN environment , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- Chandrasekaran M, Rajesh P K, Optimization of cost to customer of power train in commercial vehicle using knapsack dynamic programming influenced by vehicle IoT data , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- S. Aasha, R. Sugumar, Lightweight Feature Selection Method using Quantum Statistical Ranking and Hybrid Beetle-Bat Optimization for Smart Farming , The Scientific Temper: Vol. 16 No. 09 (2025): The Scientific Temper
- Jayalakshmi K., M. Prabakaran, Feature selection in HR analytics: A hybrid optimization approach with PSO and GSO , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- S. Munawara Banu, M. Mohamed Surputheen, M. Rajakumar, Enhanced AOMDV-based multipath routing approach for mobile ad-hoc network using ETX and ant colony optimization , The Scientific Temper: Vol. 16 No. 06 (2025): The Scientific Temper
- P. Rathinabhagya, J. Merline Vinotha, Fuzzy vehicle routing problem for a municipal solid waste management system with greenhouse gas emission at various disposal stages , The Scientific Temper: Vol. 16 No. 04 (2025): The Scientific Temper
- L. Vamsi Narasimha Rao, P.S.Prakash, M.Veera Kumari, Improvement of power system operation using a novel hybrid optimization method for optimal allocation of facts devices in radial transmission line , The Scientific Temper: Vol. 15 No. 04 (2024): 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
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.

