Energy-efficient location-based routing protocol for wireless sensor networks using teaching-learning soccer league optimization (TLSLO)
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.spl.05Keywords:
Wireless sensor networks, Energy efficiency, Modified K-means clustering, Teaching-learning soccer league optimization, Recurrent artificial neural network.Dimensions 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.
Energy efficiency in wireless sensor networks (WSNs) is a crucial and fundamental design consideration. These networks typically consist of numerous small, resource-constrained sensor nodes, frequently placed in isolated or difficult-to-reach areas. This research presents a comprehensive methodology for improving the performance and energy efficiency of WSNs deployed in a designated target area. The research begins with the deployment of sensor nodes equipped with location information and the initialization of critical network parameters. Novel techniques are introduced for efficient node clustering using a Haversine-based K-means Clustering algorithm (HKMC) and an advanced hybrid optimization model, teaching-learning soccer league optimization (TLSLO), for optimal cluster head selection within clusters. Data aggregation at cluster heads is crucial for conserving energy, and data compression techniques, including the novel weighted discrete wavelet transform (WDWT)), are employed to reduce data transmission size. Furthermore, deep learning models in the form of recurrent artificial neural networks (RANN) predict energy consumption patterns, enabling the optimization of node sleep-wake schedules for a prolonged network lifetime. Simulated using Python, the proposed protocol’s performance is evaluated, demonstrating its superiority in terms of energy efficiency, latency, network lifetime, and data delivery ratio compared to existing routing protocols. This research offers a holistic approach to improving WSNs enhancing their efficiency and sustainability in resource-constrained environments.Abstract
How to Cite
Downloads
Similar Articles
- Dileep Pulugu, Shaik K. Ahamed, Senthil Vadivu, Nisarg Gandhewar, U D Prasan, S. Koteswari, Empowering healthcare with NLP-driven deep learning unveiling biomedical materials through text mining , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Archana G, Vijayalakshmi V, Improving classification precision for medical decision systems through big data analytics application , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Archana Verma, Role of artificial intelligence in evaluating autism spectrum disorder , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- N Sasirekha, Jayakumar Karuppaiah, Yuvaraja Thangavel, KG Parthiban , Classification of mammograms by breast density , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- D. Padma Prabha, C. Victoria Priscilla, A combined framework based on LSTM autoencoder and XGBoost with adaptive threshold classification for credit card fraud detection , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Vijetna Singh, Alka Thakur, ECOLOGICAL ENGINEERING OF MICROALGAE FOR ENHANCED ENERGY PRODUCTION , The Scientific Temper: Vol. 8 No. 1&2 (2017): The Scientific Temper
- Vishal Panghal, Asha Singh, Dinesh Arora, Nidhi Ahlawat, Sunder S. Arya, Sunil Kumar, Horizontal flow biochar amended constructed wetlands as a sustainable approach for rural wastewater treatment , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Hemang Shah, Archana Gadekar, Artificial intelligence and intellectual property rights with special reference to patent and copyright , The Scientific Temper: Vol. 15 No. spl-2 (2024): The Scientific Temper
- Vijaykumar S. Kamble, Prabodh Khampariya, Amol A. Kalage, Application of optimization algorithms in the development of a real-time coordination system for overcurrent relays , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- Gomathi Ramalingam, Logeswari S, M. D. Kumar, Manjula Prabakaran, Neerav Nishant, Syed A. Ahmed, Machine learning classifiers to predict the quality of semantic web queries , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
<< < 13 14 15 16 17 18 19 20 21 22 > >>
You may also start an advanced similarity search for this article.
Most read articles by the same author(s)
- A. Tamilmani, K. Muthuramalingam, An enhanced support vector machine bbased multiclass classification method for crop prediction , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Amudavalli L, K. Muthuramalingam, Integrated energy-efficient routing and secure data management for location-aware wireless sensor networks with PFO leveraged improved fuzzy unequal clustering algorithm (IFUC) , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper