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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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An innovative machine learning optimization-based data fusion strategy for distributed wireless sensor networks Sollapure, Naganna Shankar; Govindaswamy, Poornima
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1012-1022

Abstract

Self-sufficient sensors scattered over different regions of the world comprise distributed wireless sensor networks (DWSNs), which track a range of environmental and physical factors such as pressure, temperature, vibration, sound, motion, and pollution. The use of data fusion becomes essential for combining information from various sensors and system performance. In this study, we suggested the multi-class support vector machine (SDF-MCSVM) with synthetic minority over-sampling techniques (SMOTE) data fusion for wireless sensor network (WSN) performance. The dataset includes 1,334 instances of hourly averaged answers for 12 variables from an AIR quality chemical multisensor device. To create a balanced dataset, the unbalanced data was first pre-processed using the SMOTE. The grey wolf optimization (GWO) approach is then used to reduce features in an effort to improve the efficacy and efficiency of feature selection procedures. This method is applied to classify the fused feature vectors into multiple categories at once to improve classification performance in WSNs and address unbalance datasets. The result shows the proposed method reaches high precision, accuracy, F1-score, recall, and specificity. The computational complexity and processing time were decreased in the study by using the proposed method. This is great potential for accurate and timely data fusion in dispersed WSNs with the successful integration of data fusion technologies.
Fuzzy based energy efficient cluster head selection with balanced clusters formation in wireless sensor networks Chandrappa, Maruthi Hanumanthappa; Govindaswamy, Poornima
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp928-939

Abstract

The importance of energy conservation presents a considerable challenge in wireless sensor networks (WSNs), where the sensor nodes (SNs) that constitute the network depend on battery power. Recharging the batteries of SNs in the field is challenging. The clustering technique is a commonly employed method for attaining energy efficiency. In this article, we are proposing a fuzzy-based energy efficient cluster head (CH) selection with the balanced cluster formation (FEECH-BCF) technique. It is a hybrid of the k-means algorithm, low energy adaptive clustering hierarchy- uniform size cluster (LEACH-USC) technique, and fuzzy logic technique. To create the clusters, the k-means approach is employed. The idea of LEACH-USC is used for load balancing to produce clusters with uniform size by assigning member nodes (MNs) from larger clusters to smaller clusters. Optimized CHs are selected using fuzzy based CH selection technique. The k-means algorithm is simple and quick to set up, assigning the membership of SNs to the next best cluster based on centroid locations of clusters reduces intra-cluster distance among clusters, and with the help of fuzzy logic, optimized CHs will be selected. The proposed algorithm performs exceptionally well in attaining uniform energy consumption amongst clusters and extends the network’s lifetime to a greater extent.
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