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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Govindaswamy, Poornima
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Computer Science & IT Engineering

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Journal : IAES International Journal of Artificial Intelligence (IJ-AI)

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Enhancing energy efficiency and accuracy in IoT-based wireless sensor networks using machine learning Shankar Sollapure, Naganna; Govindaswamy, Poornima
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3869-3878

Abstract

This study presents a novel sensor data fusion framework designed to improve accuracy and energy efficiency in internet of things (IoT)-driven wireless sensor networks (WSNs). The proposed approach combines machine learning techniques with the Kalman filter, addressing the limitations of traditional methods, such as high computational overhead and limited precision. By utilizing machine learning algorithms for pattern recognition and the Kalman filter for precise state estimation, the framework optimizes data processing while minimizing energy consumption. MATLAB-based simulations validate the model’s effectiveness, demonstrating a significant improvement in key performance metrics, including F1-score, recall, and precision, with an overall accuracy of 98.36%. The results highlight the framework’s ability to enhance fault tolerance, accelerate convergence rates, extend network lifespan, and optimize energy utilization, making it highly suitable for real-time data fusion applications in complex sensor environments. Furthermore, the proposed hybrid model is scalable and adaptable, allowing it to be implemented across various fields, including environmental surveillance, industrial automation, and healthcare monitoring. With integration of intelligent data processing techniques, this research contributes to the development of sustainable and efficient IoT-based monitoring systems capable of handling dynamic and resource-constrained environments.
An energy-efficient and secure framework for wireless sensor networks Chandrappa, Maruthi Hanumanthappa; Govindaswamy, Poornima
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4151-4161

Abstract

In wireless sensor networks (WSNs), achieving energy efficiency, security, and minimizing route change propagation time is essential for maintaining optimal performance. This paper introduces a new approach that combines Bray Jaccard Curtis-based Calinski Harabasz k-means (BJC-CHKMeans) for clustering and Karl Pearson correlation-based egret swarm optimization algorithm (KPC-ESOA) for selecting the best cluster head (CH) and path, along with classifying long short-term memory with gated recurrent units (CLE-GRU) for detecting harmful nodes. The methodology aims to enhance energy usage, improve routing efficiency, and strengthen security by identifying malicious nodes. Additionally, it integrates a secure routing table using elbow de-swinging k-anonymity (EDS-KA) and employs digital signature algorithm-based Zeta Bernoulli Merkle tree (DSA-ZBMT) to ensure secure communication with sink nodes. The WSN-DS dataset was used for training and testing, with rigorous preprocessing, feature extraction, and selection to maintain data integrity. Experimental results revealed that the proposed BJC-CHKMeans and CLE-GRU models outperform traditional methods in power consumption, latency, and accuracy. The system achieved a power consumption of 2.1 mW for clustering and 1.9 mW for classification, while also providing near-perfect accuracy in detecting harmful nodes. These findings demonstrate that the framework significantly enhances the energy efficiency and security of WSNs, making it a highly effective solution for large, dynamic sensor networks.
Co-Authors Chandrappa, Maruthi Hanumanthappa Shankar Sollapure, Naganna Sollapure, Naganna Shankar