Open Access

ARTICLE

Energy-Efficient Internet of Things-Based Wireless Sensor Network for Autonomous Data Validation for Environmental Monitoring

by Tabassum Kanwal¹, Saif Ur Rehman^1,*, Azhar Imran², Haitham A. Mahmoud³

1 University Institute of Information Technology, PMAS-Arid Agriculture University, Rawalpindi, 46000, Pakistan
2 School of Software Engineering, Beijing University of Technology, Beijing, 100124, China
3 Industrial Engineering Department, College of Engineering, King Saud University, Riyadh, 11421, Saudi Arabia

* Corresponding Author: Saif Ur Rehman. Email:

Computer Systems Science and Engineering 2025, 49, 185-212. https://doi.org/10.32604/csse.2024.056535

Received 24 July 2024; Accepted 24 October 2024; Issue published 10 January 2025

Abstract

This study presents an energy-efficient Internet of Things (IoT)-based wireless sensor network (WSN) framework for autonomous data validation in remote environmental monitoring. We address two critical challenges in WSNs: ensuring data reliability and optimizing energy consumption. Our novel approach integrates an artificial neural network (ANN)-based multi-fault detection algorithm with an energy-efficient IoT-WSN architecture. The proposed ANN model is designed to simultaneously detect multiple fault types, including spike faults, stuck-at faults, outliers, and out-of-range faults. We collected sensor data at 5-minute intervals over three months, using temperature and humidity sensors. The ANN was trained on 70% of the 26,280 data points per sensor, with 15% each for validation and testing. Our framework demonstrated a 97.1% improvement in fault detection accuracy (measured by F1 score) compared to existing methods, including rule-based, moving average, and statistical outlier detection approaches. The energy efficiency of the system was evaluated through 24-h power consumption tests, showing significant savings over traditional WSN architectures. Key contributions include a multi-fault detection ANN model balancing accuracy and computational efficiency, an energy-optimized IoT-WSN architecture for remote deployments, and a comprehensive performance evaluation framework. While our approach offers improvements in both data validation and energy efficiency, we acknowledge limitations such as potential scalability issues and the need for further real-world testing. This research advances the field of remote environmental monitoring by providing a robust, energy-efficient solution for ensuring data reliability in challenging deployment scenarios. Future work will explore more advanced machine learning techniques and extended field testing to further validate and improve the system’s performance.

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Keywords

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