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.
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Acknowledgment
This research has been partially funded by the National Science Foundation (NSF) under grants CCF-1442672 and SCC-1637277 and gifts from Accenture Research Labs. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF or other funding agencies and companies mentioned above.
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Tonekaboni, N.H., Ramaswamy, L., Sachdev, S. (2019). A Mobile and Web-Based Approach for Targeted and Proactive Participatory Sensing. In: Wang, X., Gao, H., Iqbal, M., Min, G. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 292. Springer, Cham. https://doi.org/10.1007/978-3-030-30146-0_15
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