■ Evaluating the Health State of Urban Areas Using Multi-source Heterogeneous Data

Abstract.

In recent years we are witnessing a growing interest in identifying various aspects affecting the quality of life in smart cities, such as traffic congestion and pollution levels, in order to provide services that enhance the public welfare. In smart cities, sensor infrastructures are deployed around the city combined with data analytics, to monitor and detect in real-time possible anomalies or events of interest. One major challenge that arise in smart-cities is to evaluate the health state of an urban city using heterogeneous multi-source urban data, i.e., pollution and traffic data. Existing works in the literature are limited since they analyze a single source of data, either inferring the air quality or estimating traffic congestion. However, none of these works considers both data sources in concert for estimating the city’s health state. In this work, we present “HELIoS” (HEalthy LIving Smart), a framework that combines multiple heterogeneous sources of data, i.e., urban traffic and pollution data, to diagnose the health state of urban areas in a smart city. Our experimental evaluation provides valuable insights into identifying the health state of an urban area, and shows that our approach is both practical and efficient.

BibTex Entry.

@article{Tomaras2018EvaluatingTH,
  title={Evaluating the Health State of Urban Areas Using Multi-source Heterogeneous Data},
  author={Dimitrios Tomaras and Vana Kalogeraki and Nikolas Zvgouras and Nikolaos Panagiotou and Dimitrios Gunopulos},
  journal={2018 IEEE 19th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)},
  year={2018},
  pages={14-22}
}