Intelligent Forecasting of Energy Depletion in Underwater Wireless Sensor Networks: A Machine Learning Paradigm for Energy Hole Prediction

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 4

Year of Publication : 2024

Authors : Anandkumar Pandya, Tanmay Pawar

10.14445/23488379/IJEEE-V11I4P121

How to Cite?

Anandkumar Pandya, Tanmay Pawar, "Intelligent Forecasting of Energy Depletion in Underwater Wireless Sensor Networks: A Machine Learning Paradigm for Energy Hole Prediction," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 4, pp. 198-205, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I4P121

Abstract:

Underwater Wireless Sensor Networks (UWSNs) play a pivotal role in aquatic environments, facilitating data collection and communication for various applications. However, the limited energy resources of sensor nodes pose a critical challenge, leading to the emergence of energy holes that can adversely impact network performance and longevity. This research proposes a novel two-part approach to address this challenge by leveraging Neural Networks for both energy hole classification and prediction in UWSNs. The study begins with an in-depth literature review covering energy management in UWSNs and the application of deep learning techniques, particularly neural networks, in predicting network-related issues. Through this exploration, the unique challenges associated with underwater environments are identified, forming the foundation for the proposed neural network-based solution. 1. Energy Hole Classification: Extensive simulations of various scenarios are conducted to classify instances of energy holes. These simulations generate a rich dataset featuring crucial columns such as residual energy, hop distance from the surface sink, zone, source address, destination address, etc. This dataset is meticulously prepared and preprocessed to ensure its suitability for training the neural network model for energy hole classification. 2. Energy Hole Prediction: The prepared dataset from the classification phase is then utilized to train a neural network model for predicting energy holes. The model is designed to capture dependencies among features such as residual energy, hop distance, and network addresses. The trained model is evaluated on a distinct test dataset, using metrics such as accuracy, precision, recall, and F1 score to measure its success in predicting energy holes. The results showcase the model’s ability to learn and generalize from the extensive dataset, providing valuable insights into potential energy hole occurrences based on the specified features. The proposed neural network-based paradigm, incorporating features such as residual energy and hop distance, offers a promising solution to enhance energy management in UWSNs, ultimately improving network longevity and performance. The study concludes with discussions on the implications of the results, potential real-world applications, and avenues for future research in the intersection of deep learning and underwater sensor networks.

Keywords:

Underwater Wireless Sensor, Energy hole, Machine Learning, Neural Network, Castalia, Multipath routing, Energy efficient routing.

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