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Efficient Integration of Heterogeneous Mobility-Pollution Big Data for Joint Analytics at Scale with QoS Guarantees

Author

Listed:
  • Isam Mashhour Al Jawarneh

    (Department of Computer Science, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates)

  • Luca Foschini

    (Dipartimento di Informatica—Scienza e Ingegneria, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy)

  • Paolo Bellavista

    (Dipartimento di Informatica—Scienza e Ingegneria, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy)

Abstract

Numerous real-life smart city application scenarios require joint analytics on unified views of georeferenced mobility data with environment contextual data including pollution and meteorological data. particularly, future urban planning requires restricting vehicle access to specific areas of a city to reduce the adverse effect of their engine combustion emissions on the health of dwellers and cyclers. Current editions of big spatial data management systems do not come with over-the-counter support for similar scenarios. To close this gap, in this paper, we show the design and prototyping of a novel system we term as EMDI for the enrichment of human and vehicle mobility data with pollution information, thus enabling integrated analytics on a unified view. Our system supports a variety of queries including single geo-statistics, such as ‘mean’, and Top-N queries, in addition to geo-visualization on the combined view. We have tested our system with real big georeferenced mobility and environmental data coming from the city of Bologna in Italy. Our testing results show that our system can be efficiently utilized for advanced combined pollution-mobility analytics at a scale with QoS guarantees. Specifically, a reduction in latency that equals roughly 65%, on average, is obtained by using EMDI as opposed to the plain baseline, we also obtain statistically significant accuracy results for Top-N queries ranging roughly from 0.84 to 1 for both Spearman and Pearson correlation coefficients depending on the geo-encoding configurations, in addition to significant single geo-statistics accuracy values expressed using Mean Absolute Percentage Error on the range from 0.00392 to 0.000195.

Suggested Citation

  • Isam Mashhour Al Jawarneh & Luca Foschini & Paolo Bellavista, 2023. "Efficient Integration of Heterogeneous Mobility-Pollution Big Data for Joint Analytics at Scale with QoS Guarantees," Future Internet, MDPI, vol. 15(8), pages 1-28, August.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:8:p:263-:d:1212205
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    References listed on IDEAS

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    1. Mohammed Obaid & Arpad Torok & Jairo Ortega, 2021. "A Comprehensive Emissions Model Combining Autonomous Vehicles with Park and Ride and Electric Vehicle Transportation Policies," Sustainability, MDPI, vol. 13(9), pages 1-15, April.
    2. Rassarin Chinnachodteeranun & Kiyoshi Honda, 2016. "Sensor Observation Service API for Providing Gridded Climate Data to Agricultural Applications," Future Internet, MDPI, vol. 8(3), pages 1-16, August.
    3. Marianne Silva & Gabriel Signoretti & Julio Oliveira & Ivanovitch Silva & Daniel G. Costa, 2019. "A Crowdsensing Platform for Monitoring of Vehicular Emissions: A Smart City Perspective," Future Internet, MDPI, vol. 11(1), pages 1-20, January.
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    Cited by:

    1. Massimo Cafaro & Italo Epicoco & Marco Pulimeno, 2024. "State-of-the-Art Future Internet Technology in Italy 2022–2023," Future Internet, MDPI, vol. 16(2), pages 1-4, February.

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