Abstract: The proliferation of Internet of Things technologies has revolutionized the landscape of indoor environmental monitoring, offering opportunities to enhance comfort, health, and energy efficiency. This paper presents the development and implementation of a low-cost IoT sensor system designed for indoor monitoring with a Machine Learning-driven prediction-based data collection approach. Leveraging deep learning algorithms, the IoT device predicts significant environmental changes and dynamically adjusts the data collection frequency to optimize energy consumption and data transmission. Experimental results demonstrate the system’s ability to accurately predict environ-mental variations, resulting in a reduction in data transmission and power usage up to 96% without compromising the monitoring quality. The findings highlight the potential of prediction-based data collection as a viable solution for sustainable and effective indoor environment monitoring on low-cost IoT devices.

Paolo Capellacci, Lorenzo Calisti and Emanuele Lattanzi, “A Low-Cost IoT Sensor for Indoor Monitoring with Prediction-Based Data Collection” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151106

@article{Capellacci2024,
title = {A Low-Cost IoT Sensor for Indoor Monitoring with Prediction-Based Data Collection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151106},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151106},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Paolo Capellacci and Lorenzo Calisti and Emanuele Lattanzi}
}