Enhancing Energy Efficiency and Accuracy in IoT-Based Wireless Sensor Networks Using Machine Learning

Aug 4, 2026

This study presents a hybrid sensor data fusion framework for IoT-driven wireless sensor networks that improves both accuracy and energy efficiency. The method combines machine learning for pattern recognition and feature extraction with a Kalman filter for precise state estimation, reducing the computational overhead and precision limits of traditional approaches. MATLAB simulations validate performance gains, including higher F1-score, recall, and precision and an overall accuracy of 98.36%. Results also show improved fault tolerance, faster convergence, extended network lifespan, and better energy utilization in real-time scenarios. The scalable design supports deployment in environmental surveillance, industrial automation, and healthcare monitoring.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

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Abstract

Introduction

Background

Enhancing energy efficiency and accuracy in IoT-based wireless sensor networks using machine learning Naganna Shankar Sollapure1,2, Poornima Govindaswamy2 1Department of Electronics and Communication Engineering, Government Engineering College, Talakal-Koppal, India 2Department of Electronics and Communication Engineering, B.M. S. College of Engineering, Bengaluru, India Article Info ABSTRACT Article history: Received Jun 17, 2024 Revised Jul 3, 2025 Accepted Jul 13, 2025 Keywords: Energy efficiency Kalman filter Real-time target tracking Sensor data fusion Wireless sensor networks Corresponding Author: 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. This is an open access article under the CC BY-SA license. Naganna Shankar Sollapure Department of Electronics and Communication Engineering, Government Engineering College Talakal-Koppal, Karnataka, India [Email: naganna.sollapure@gmail.com](Email: naganna.sollapure@gmail.com) 1. INTRODUCTION Sensor networks are integral to numerous modern applications, including environmental monitoring, healthcare, smart cities, and industrial automation [1] . These networks consist of multiple sensor nodes that work together to collect, process, and transmit data, offering a detailed understanding of the monitored environment. However, the efficiency of these networks largely depends on effective data management and utilization [2] . Sensor data fusion plays a critical role in this process by integrating information from multiple sensors, thereby improving data accuracy, reliability, and overall system performance. By addressing the shortcomings of individual sensors such as interference, limited coverage, and environmental disruptions, data fusion enhances the quality and interpretability of collected data [3], [4] . Additionally, it minimizes redundancy, streamlines data transmission, and boosts the overall efficiency of sensor networks. With wireless sensor networks (WSNs) getting increasingly complex and a need to become more accurate in real-time operations, data fusion methods become an increasingly necessary advanced component. Although conventional methods such as Kalman filter present solid theoretical basis in regards to state estimation [5], they are generally ineffective in application to the nonlinear data found in high dimensions that is characteristic of contemporary internet of things (IoT)-based WSNs. Machine learning, specifically deep neural networks (DNNs), has shown to be successful when it comes to extracting features and coping with complex s

FAQs

What problem does the proposed framework address in IoT-based wireless sensor networks?

It addresses limitations of traditional sensor fusion methods, including high computational overhead and limited precision under nonlinear, high-dimensional IoT sensor data.

How does the framework combine machine learning with the Kalman filter?

Machine learning supports pattern recognition and feature extraction, while the Kalman filter performs accurate state estimation and noise reduction.

What evidence shows the approach improves energy efficiency and accuracy?

MATLAB-based simulations report improved metrics such as F1-score, recall, precision, and an overall accuracy of 98.36%, along with benefits like extended network lifespan and better energy utilization.

Aug 4, 2026

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