Collaborative Computing: Networking, Applications and Worksharing. 15th EAI International Conference, CollaborateCom 2019, London, UK, August 19-22, 2019, Proceedings

Research Article

A Mobile and Web-Based Approach for Targeted and Proactive Participatory Sensing

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  • @INPROCEEDINGS{10.1007/978-3-030-30146-0_15,
        author={Navid Tonekaboni and Lakshmish Ramaswamy and Sakshi Sachdev},
        title={A Mobile and Web-Based Approach for Targeted and Proactive Participatory Sensing},
        proceedings={Collaborative Computing: Networking, Applications and Worksharing. 15th EAI International Conference, CollaborateCom 2019, London, UK, August 19-22, 2019, Proceedings},
        proceedings_a={COLLABORATECOM},
        year={2019},
        month={8},
        keywords={Participatory sensing Mobile caching Citizen science Crowdsensing},
        doi={10.1007/978-3-030-30146-0_15}
    }
    
  • Navid Tonekaboni
    Lakshmish Ramaswamy
    Sakshi Sachdev
    Year: 2019
    A Mobile and Web-Based Approach for Targeted and Proactive Participatory Sensing
    COLLABORATECOM
    Springer
    DOI: 10.1007/978-3-030-30146-0_15
Navid Tonekaboni1,*, Lakshmish Ramaswamy1,*, Sakshi Sachdev1,*
  • 1: University of Georgia
*Contact email: navidht@uga.edu, laks@cs.uga.edu, sss11759@uga.edu

Abstract

Participatory sensing applications have gained popularity due to the increased use of mobile phones with embedded sensors. One of the main issues in participatory sensing applications is the uneven coverage of areas, i.e., some areas might be covered by multiple participants while there is no data for other areas. In this paper, we design mobile and web-based infrastructure to enable domain scientists to effectively acquire crowd-sensed data from specific areas of interest (AOIs) to support the goal of even coverage for data collection. Scientists can mark the AOIs on a web-portal, then volunteers will be proactively informed about the participatory sensing opportunities near their current location. We presented a caching algorithm to increase the performance of our proposed system and studied the performance of the caching algorithm for different real-world scenarios on different mobile phones. We observed that prefetching data improves the performance to some extent; however, it starts to degrade after a certain point depending upon the number of nearby AOIs.