@inproceedings{648e9c5929f84d17a775710967ae6489,
title = "Whom to Query? Spatially-Blind Participatory Crowdsensing under Budget Constraints",
abstract = "The ubiquity of sensors has introduced a variety of new opportunities for data collection. In this paper, we attempt to answer the question: Given M workers in a spatial environment and N probing resources, where N < M, which N workers should be queried to answer a specific question? To solve this research question, we propose two querying algorithms: one that exploits worker feedback (DispNN) and one that does not rely on worker feedback (DispMax). We evaluate DispNN and DispMax algorithms on two different event distributions: clustered and complete spatial randomness. We then apply the algorithms to a dataset of actual street harassment events provided by Hollaback. The proposed algorithms outperform a random selection approach by up to 30\%, a random selection approach with feedback by up to 35\%, a greedy heuristic by up to 5x times, and cover up to a median of 96\% of the incidents.",
keywords = "Budget constraints, Event detection, Participatory crowdsensing",
author = "Mai ElSherief and Ramya Raghavendra and Morgan Vigil-Hayes and Elizabeth Belding",
note = "Funding Information: This work was funded in part by the NSF Graduate Research Fellowship Program under Grant No. DGE-1144085, and in part by US Army Research laboratory and the UK Ministry of Defence under Agreement Number W911NF-15-R-0003. The authors would like to thank Hollaback for sharing their dataset.; 1st ACM Workshop on Mobile Crowdsensing Systems and Applications, CrowdSenSys 2017 ; Conference date: 05-11-2017",
year = "2017",
month = nov,
day = "6",
doi = "10.1145/3139243.3139249",
language = "English (US)",
series = "CrowdSenSys 2017 - Proceedings of the 1st ACM Workshop on Mobile Crowdsensing Systems and Applications, Part of SenSys 2017",
publisher = "Association for Computing Machinery, Inc",
pages = "31--37",
editor = "Rasit Eskicioglu",
booktitle = "CrowdSenSys 2017 - Proceedings of the 1st ACM Workshop on Mobile Crowdsensing Systems and Applications, Part of SenSys 2017",
}