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Gotcha: a mobile urban sensing system

Published: 03 November 2014 Publication History

Abstract

Urban environment has significant impacts on the health of city dwellers. To understand these impacts, city planners have to obtain fine-grained environmental information, however such information is not available with traditional environmental systems. To address this problem, we present Gotcha, a taxi-based mobile sensing system for fine-grained environmental data acquisition. Gotcha utilizes taxi cabs to serve as a sensor that collects a variety of environmental information (such as concentrations of carbon-dioxide, carbon-monoxide, ozone, particulate matter, etc.). We aim to deploy our system in the city of Shenzhen on a fleet of 100 taxi cabs, and we present here our results from our initial deployment.

References

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B. Hull, V. Bychkovsky, Y. Zhang, K. Chen, M. Goraczko, A. Miu, E. Shih, H. Balakrishnan, and S. Madden. Cartel: A distributed mobile sensor computing system. In Proceedings of the 4th International Conference on Embedded Networked Sensor Systems, SenSys '06, pages 125--138, New York, NY, USA, 2006. ACM.
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A. Kirby. Pollution: A life and death issue. http://news.bbc.co.uk/2/hi/science/nature/4086809.stm. Accessed: 2014-08-02.
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L. Li, Y. Zheng, and L. Zhang. Demonstration abstract: Pimi air box: A cost-effective sensor for participatory indoor quality monitoring. In Proceedings of the 13th International Symposium on Information Processing in Sensor Networks, IPSN '14, pages 327--328, Piscataway, NJ, USA, 2014. IEEE Press.
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  • (2020)Enhancing the Data Learning With Physical Knowledge in Fine-Grained Air Pollution InferenceIEEE Access10.1109/ACCESS.2020.29936108(88372-88384)Online publication date: 2020
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cover image ACM Conferences
SenSys '14: Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems
November 2014
380 pages
ISBN:9781450331432
DOI:10.1145/2668332
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 03 November 2014

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Overall Acceptance Rate 174 of 867 submissions, 20%

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  • (2020)(M)ad to See Me?Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33973244:2(1-26)Online publication date: 15-Jun-2020
  • (2020)Fine-Grained Air Pollution Inference with Mobile Sensing SystemsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33973224:2(1-21)Online publication date: 15-Jun-2020
  • (2020)Enhancing the Data Learning With Physical Knowledge in Fine-Grained Air Pollution InferenceIEEE Access10.1109/ACCESS.2020.29936108(88372-88384)Online publication date: 2020
  • (2019)Wirtinger holography for near-eye displaysACM Transactions on Graphics10.1145/3355089.335653938:6(1-13)Online publication date: 8-Nov-2019
  • (2019)Unsupervised Approaches for Textual Semantic Annotation, A SurveyACM Computing Surveys10.1145/332447352:4(1-45)Online publication date: 30-Aug-2019
  • (2019)Personalized Travel Time Prediction Using a Small Number of Probe VehiclesACM Transactions on Spatial Algorithms and Systems10.1145/33176635:1(1-27)Online publication date: 21-May-2019
  • (2018)Generative Model Based Fine-Grained Air Pollution Inference for Mobile Sensing SystemsProceedings of the 16th ACM Conference on Embedded Networked Sensor Systems10.1145/3274783.3275216(426-427)Online publication date: 4-Nov-2018
  • (2018)Cooperative Target Tracking and Signal Propagation Learning Using Mobile SensorsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/32649462:3(1-21)Online publication date: 18-Sep-2018
  • (2018)Augmenting User Identification with WiFi Based Gesture RecognitionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/32649442:3(1-27)Online publication date: 18-Sep-2018
  • (2018)Guiding the Data Learning Process with Physical Model in Air Pollution Inference2018 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2018.8622381(4475-4483)Online publication date: Dec-2018
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