Smart City Gnosys

Smart city article details

Title	Lstm-Based Citizens' Qol Provisioning In Smart Cities Using Iot-Enabled Environmental Sensors And Graphical Tools
ID_Doc	35815
Authors	Bai Z.; Yang X.; Xiang J.; Attar H.; Lu L.; Khosravi M.; Qin H.; Zhao X.; Zhu Z.; Li D.
Year	2024
Published	IEEE Transactions on Consumer Electronics
DOI	http://dx.doi.org/10.1109/TCE.2024.3512875
Abstract	This study examines the role of Cyber-Physical Systems (CPS) through Internet of Things (IoT) devices, and advanced machine learning in monitoring and managing air pollution in future smart cities, with a focus on enhancing Quality of Life (QoL). CPS, integrated with IoT sensors, enables real-time data collection and analysis to understand the impact of power plant locations and operations on air quality. The study utilizes the Topographic Position Index (TPI) to assess how topography affects pollution, and regression analysis to explore the relationship between power plant output and pollution levels. Long Short-Term Memory (LSTM) networks forecast air pollution trends, while the CA-Markov chain method predicts spatial-temporal patterns. Graphical tools, such as heat maps and trend charts, visualize pollutant distribution and progression, making data more accessible for decision-makers. Findings indicate that flat areas are more susceptible to higher pollution, with February being the peak month for most pollutants. The results of this study also showed that CO and CO2 in spring have a higher correlation with power plant production (R2 =0.85) than other pollutants. The LSTM model shows high accuracy (R2 = 0.84) in predicting air quality trends within the CPS and IoT-enabled framework. © 1975-2011 IEEE.
Author Keywords	Air Pollutants; CA-Markov Chain; Cyber-Physical Systems (CPS); LSTM; Machine Learning; Smart Cities; Topographic Position Index (TPI)

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