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Smart city article details

Title	Enhancing Urban Iot Temperature Sensing Accuracy Through Machine Learning-Driven Dynamic Ventilation
ID_Doc	24059
Authors	Sudhakar K.; Lurdhumary J.; Bathrinath S.; Howard E.; Vijayakumar G.N.S.; Anusuya M.; Robin C.R.R.
Year	2024
Published	AIP Conference Proceedings, 3193, 1
DOI	http://dx.doi.org/10.1063/5.0232810
Abstract	To monitor and comprehend the thermal environment, it is vital to deploy Internet of Things (IoT) temperature sensors in metropolitan areas. Overheating and erroneous readings can occur when temperature sensors are exposed to intense sunlight, which can damage their accuracy. We offer a Machine Learning-based solution to this problem, which involves controlling the airflow around the sensors to ensure precise and efficient temperature readings while minimizing power consumption. Predicting steady-state temperature from a small window of starting temperature readings and studying the effect of ventilation duration are the two interrelated topics of this work. We use multivariate time series from several sensors to forecast temperature, and then we compare the accuracy of different DNNs that are well-suited for low-power Internet of Things sensor devices. We also discuss the trade-off between prediction accuracy and energy consumption, which is dependent on ventilation time. The former is connected with the length of the observed input sequence. We can create effective Internet of Things (IoT) systems for smart city environmental monitoring that are both accurate and energy efficient by utilising advanced prediction algorithms. © 2024 Author(s).
Author Keywords	Energy Optimization; Internet of Things.; Machine Learning; Smart Sensors; Temperature Monitoring

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