Machine Learning-based Energy Optimisation in Smart City Internet of Things

The deployment of Internet of Things (IoT) temperature sensors in urban areas is essential for the monitoring and understanding of the thermal environment. However, accurate temperature measurements can be compromised by factors such as direct sunlight, leading to overheating and inaccurate readings. We propose a Machine Learning-based approach that addresses this challenge by dynamically ventilating the sensor environment using small fans, enabling accurate and energy-efficient temperature measurements. This paper focuses on two interconnected problems: predicting steady-state temperature using a limited window of initial temperature measurements and investigating the impact of ventilation time. We employ various DNNs suitable for low-power IoT sensor devices to predict temperature using multivariate time series from different sensors and compare their accuracy. Furthermore, we highlight the tradeoff between prediction accuracy, which is correlated to the length of the observed input sequence, and energy consumption dependent on ventilation time. By adopting advanced prediction techniques, we can develop efficient IoT systems for accurate and energy-efficient environment monitoring in smart cities.