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
- Ambica Batas, Udayakumara Ramakrishna B.N, Abuse of Dominant Position in the Realm of the Professional Sports Industry , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Priya Nandhagopal, Jayasimman Lawrence, ETTG: Enhanced token and tag generation for authenticating users and deduplicating data stored in public cloud storage , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Anli Suresh, Sandhiya M., Investment model on the causation of inclining attributes towards bank investment options in the investor’s portfolio , The Scientific Temper: Vol. 16 No. Spl-2 (2025): The Scientific Temper
- V. Mahalakshmi, M. Manimekalai, Location Specific Paddy Yield Prediction using Monte Carlo Simulation incorporated Long Short-Term Memory , The Scientific Temper: Vol. 16 No. 10 (2025): The Scientific Temper
- M. Kohila, S. Rethinavalli, A P2ECAM: A Trust-Preserving Cross-Cloud Data Migration Model For Resource-Constrained Mobile Devices Using Certificate-Free Elliptic Curve Cryptography , The Scientific Temper: Vol. 17 No. 02 (2026): The Scientific Temper
- Nitin Chandel, Lalsingh Khalsa, Sunil Prayagi, Vinod Varghese, Three‑phase‑lags thermoelastic infinite medium model with a spherical cavity via memory-dependent derivatives , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Divya R., Vanathi P. T., Harikumar R., An optimized cardiac risk levels classifier based on GMM with min- max model from photoplethysmography signals , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- U. Perachiselvi, R. Balasubramani, Funding agencies in Tamil Nadu State Universities: A scientometric perspective , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Dharmendra Singh, Surabhi Singh, Identification of Microsatellite DNA for Population Genetic Analysis in Tor tor , The Scientific Temper: Vol. 11 No. 1&2 (2020): The Scientific Temper
- Syam Sundar. S, Direct reuse of scour and bleach effluent water for cotton knitted fabrics , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
<< < 26 27 28 29 30 31 32 33 34 > >>
You may also start an advanced similarity search for this article.

