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Task selection in spatial crowdsourcing from worker’s perspective

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Abstract

With the progress of mobile devices and wireless broadband, a new eMarket platform, termed spatial crowdsourcing is emerging, which enables workers (aka crowd) to perform a set of spatial tasks (i.e., tasks related to a geographical location and time) posted by a requester. In this paper, we study a version of the spatial crowdsourcing problem in which the workers autonomously select their tasks, called the worker selected tasks (WST) mode. Towards this end, given a worker, and a set of tasks each of which is associated with a location and an expiration time, we aim to find a schedule for the worker that maximizes the number of performed tasks. We first prove that this problem is NP-hard. Subsequently, for small number of tasks, we propose two exact algorithms based on dynamic programming and branch-and-bound strategies. Since the exact algorithms cannot scale for large number of tasks and/or limited amount of resources on mobile platforms, we propose different approximation algorithms. Finally, to strike a compromise between efficiency and accuracy, we present a progressive algorithms. We conducted a thorough experimental evaluation with both real-world and synthetic data on desktop and mobile platforms to compare the performance and accuracy of our proposed approaches.

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Notes

  1. Field Agent (http://www.fieldagent.net/) is a spatial crowdsourcing application.

  2. It is reported that more than 100 milliseconds response time makes the experience non-interactive [11].

  3. In the path-TSP problem, i.e., the traveling salesmen can start from any city, and are not particularly interested in returning to the starting city of their tours.

  4. R contains |𝑄| tasks, and is not necessary to be a valid task sequence.

  5. Note that in this paper promising tasks means feasible tasks, and non-promising tasks means infeasible tasks.

  6. The probability that a task being preempted can be determined by various factors in different applications. For example, a possible factor to estimate the probability of a task’s preemption can be the number of competing workers/tasks co-located in the proximity of the task. In this work, as a proof-of-concept, we simply assume that the preemption probability of each task is given.

  7. https://www.taskrabbit.com/

  8. For progressive algorithms we did not report the response time since it was not the concern.

  9. Recall that more than 100 ms response time makes the experience non-interactive [11].

    Fig. 18
    figure 18

    Running time on Mobile phone- Yelp

    Full size image

  10. http://www.gigwalk.com/

  11. https://postmates.com/

  12. https://www.taskrabbit.com/

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Acknowledgments

This research has been funded in part by NSF grants IIS-1115153 and IIS-1320149, a contract with Los Angeles Metropolitan Transportation Authority (LA Metro), the USC Integrated Media Systems Center (IMSC), HP Labs and unrestricted cash gifts from Google, Northrop Grumman, Microsoft and Oracle. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of any of the sponsors such as the National Science Foundation or LA Metro.

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Authors and Affiliations

  1. Computer Science Department, University of Southern California, Los Angeles, CA, 90089-0781, USA

    Dingxiong Deng, Cyrus Shahabi & Ugur Demiryurek

  2. Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina Del Rey, CA, 90292, USA

    Linhong Zhu

Authors
  1. Dingxiong Deng
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  2. Cyrus Shahabi
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Correspondence to Dingxiong Deng.

Additional information

A preliminary version of this work [13] appeared in ACM SIGSPATIAL GIS 2013.

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Deng, D., Shahabi, C., Demiryurek, U. et al. Task selection in spatial crowdsourcing from worker’s perspective. Geoinformatica 20, 529–568 (2016). https://doi.org/10.1007/s10707-016-0251-4

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