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
- A. Sandanasamy, P. Joseph Charles, Distributed SDN control for IoT networks: A federated meta reinforcement learning solution for load balancing , The Scientific Temper: Vol. 16 No. 06 (2025): The Scientific Temper
- A. Sathya, M. S. Mythili, MOHCOA: Multi-objective hermit crab optimization algorithm for feature selection in sentiment analysis of Covid-19 Twitter datasets , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Bommaiah Boya, Premara Devaraju, Integrating clinical and ECG data for heart disease prediction: A hybrid deep learning approach based on two modalities with particle swarm optimization , The Scientific Temper: Vol. 16 No. 05 (2025): The Scientific Temper
- A. Kalaiselvi, A. Chandrabose, Fuzzy logic-driven scheduling for cloud computing operations: a dynamic and adaptive approach , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- O. Devipriya, K. Kungumaraj, Enhancing cloud efficiency: an intelligent virtual machine selection and migration approach for VM consolidation , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- M. Menaha, J. Lavanya, Crop yield prediction in diverse environmental conditions using ensemble learning , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- M. Prabhu, A. Chandrabose, Improving the resource allocation with enhanced learning in wireless sensor networks , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- M. Deepika, I Antonitte Vinoline, Optimization of an Advanced Integrated Inventory Model Considering Shortages and Deterioration across Varying Demand Functions , The Scientific Temper: Vol. 16 No. 09 (2025): The Scientific Temper
- Kirti Gupta, Parul Goyal, Modified-multi objective firefly optimization algorithm for object oriented applications test suites optimization , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Lakshminarayani A, A Shaik Abdul Khadir, A blockchain-integrated smart healthcare framework utilizing dynamic hunting leadership algorithm with deep learning-based disease detection and classification model , The Scientific Temper: Vol. 15 No. 04 (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.

