TY - JOUR
T1 - Air Quality Decentralized Forecasting
T2 - Integrating IoT and Federated Learning for Enhanced Urban Environmental Monitoring
AU - Kulkarni, Vibha
AU - Lakshmi, Adepu Sree
AU - Lakshmi, Chaganti B.N.
AU - Panneerselvam, Sivaraj
AU - Kanan, Mohammad
AU - Flah, Aymen
AU - Elnaggar, Mohamed F.
N1 - Publisher Copyright:
© by the authors.
PY - 2024/8
Y1 - 2024/8
N2 - Air quality forecasting is a critical environmental challenge with significant implications for public health and urban planning. Conventional machine learning models, although quite effective, require data collection, which can be hampered by issues relating to privacy and data security. Federated Learning (FL) overcomes these limitations by enabling model training across decentralized data sources without compromising data privacy. This study describes a federated learning approach to predict the Air Quality Index (AQI) based on data from several Internet of Things (IoT) sensors located in different urban locations. The proposed approach trains a model using data from different sensors while preserving the privacy of each data source. The model uses local computational resources at the sensor level during the initial data processing and training, sharing only the model updates to the central location. The results show that the performance of the proposed FL model is comparable to a centralized model and ensures better data privacy with reduced data transmission requirements. This study opens new doors to real-time, scalable, and efficient air quality monitoring systems. The proposed method is quite significant for smart city initiatives and environmental monitoring, as it provides a solid framework for using IoT technology while preserving privacy.
AB - Air quality forecasting is a critical environmental challenge with significant implications for public health and urban planning. Conventional machine learning models, although quite effective, require data collection, which can be hampered by issues relating to privacy and data security. Federated Learning (FL) overcomes these limitations by enabling model training across decentralized data sources without compromising data privacy. This study describes a federated learning approach to predict the Air Quality Index (AQI) based on data from several Internet of Things (IoT) sensors located in different urban locations. The proposed approach trains a model using data from different sensors while preserving the privacy of each data source. The model uses local computational resources at the sensor level during the initial data processing and training, sharing only the model updates to the central location. The results show that the performance of the proposed FL model is comparable to a centralized model and ensures better data privacy with reduced data transmission requirements. This study opens new doors to real-time, scalable, and efficient air quality monitoring systems. The proposed method is quite significant for smart city initiatives and environmental monitoring, as it provides a solid framework for using IoT technology while preserving privacy.
KW - IoT
KW - air quality index
KW - decentralization
KW - federated learning
KW - smart city
UR - http://www.scopus.com/inward/record.url?scp=85203243733&partnerID=8YFLogxK
U2 - 10.48084/etasr.7869
DO - 10.48084/etasr.7869
M3 - Article
AN - SCOPUS:85203243733
SN - 2241-4487
VL - 14
SP - 16077
EP - 16082
JO - Engineering, Technology and Applied Science Research
JF - Engineering, Technology and Applied Science Research
IS - 4
ER -