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
- Nitika, Kuldeep Chaudhary, A critical review of social media advertising literature: Visualization and bibliometric approach , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Rupesh Mandal, Bobby Sharma, Dibyajyoti Chutia , Smart flood monitoring in Guwahati city: A LoRa-based AIoT and edge computing sensor framework , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Neeraj, Anita Singhrova, A critical review of blockchain-based authentication techniques , The Scientific Temper: Vol. 16 No. 04 (2025): The Scientific Temper
- Rajesh Kumar Singh, Genetic Variability in Aromatic Rice , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- V Vijayaraj, M. Balamurugan, Monisha Oberai, Machine learning approaches to identify the data types in big data environment: An overview , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Sangeeta Modi, P Usha, Fault analysis in hybrid microgrid for developing a suitable protection scheme , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- N.S.G. Ganesh, V Arulkumar, R. Lathamanju, Priscilla Joy , Energetic and highly reliable photovoltaic power source assisted water pump control system design using IoT , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Karan Berry, Shiv Kumar, Exploring the mediating role of gastronomic experience in tourist satisfaction: A multigroup analysis , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Sahaya Jenitha A, Sinthu J. Prakash, A general stochastic model to handle deduplication challenges using hidden Markov model in big data analytics , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Ravikiran K, Neerav Nishant, M Sreedhar, N.Kavitha, Mathur N Kathiravan, Geetha A, Deep learning methods and integrated digital image processing techniques for detecting and evaluating wheat stripe rust disease , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
<< < 12 13 14 15 16 17 18 19 20 21 > >>
You may also start an advanced similarity search for this article.

