research-article

A Server-Assigned Spatial Crowdsourcing Framework

Authors:

Leyla KazemiAuthors Info & Claims

ACM Transactions on Spatial Algorithms and Systems (TSAS), Volume 1, Issue 1

Article No.: 2, Pages 1 - 28

https://doi.org/10.1145/2729713

Published: 27 July 2015 Publication History

Abstract

With the popularity of mobile devices, spatial crowdsourcing is rising as a new framework that enables human workers to solve tasks in the physical world. With spatial crowdsourcing, the goal is to crowdsource a set of spatiotemporal tasks (i.e., tasks related to time and location) to a set of workers, which requires the workers to physically travel to those locations in order to perform the tasks. In this article, we focus on one class of spatial crowdsourcing, in which the workers send their locations to the server and thereafter the server assigns to every worker tasks in proximity to the worker’s location with the aim of maximizing the overall number of assigned tasks. We formally define this maximum task assignment (MTA) problem in spatial crowdsourcing, and identify its challenges. We propose alternative solutions to address these challenges by exploiting the spatial properties of the problem space, including the spatial distribution and the travel cost of the workers. MTA is based on the assumptions that all tasks are of the same type and all workers are equally qualified in performing the tasks. Meanwhile, different types of tasks may require workers with various skill sets or expertise. Subsequently, we extend MTA by taking the expertise of the workers into consideration. We refer to this problem as the maximum score assignment (MSA) problem and show its practicality and generality. Extensive experiments with various synthetic and two real-world datasets show the applicability of our proposed framework.

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Index Terms

A Server-Assigned Spatial Crowdsourcing Framework
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Transactions on Spatial Algorithms and Systems

ACM Transactions on Spatial Algorithms and Systems Volume 1, Issue 1

Inaugural Issue

August 2015

116 pages

ISSN:2374-0353

EISSN:2374-0361

DOI:10.1145/2807914

Editor:
Hanan Samet
University of Maryland, College Park

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Copyright © 2015 ACM.

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Publication History

Published: 27 July 2015

Accepted: 01 January 2015

Revised: 01 September 2014

Received: 01 November 2013

Published in TSAS Volume 1, Issue 1

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Ignatius HBahsoon R(2024)Equity, Equality, and Need: Digital Twin Approach for Fairness-Aware Task Assignment of Heterogeneous Crowdsourced LogisticsIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.332194011:3(3420-3431)Online publication date: Jun-2024
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Jiang KCao YZhou HWu JZhang ZLiu Z(2024)Adaptive Dynamic Programming for Multi-Driver Order Dispatching at Large-ScaleIEEE Transactions on Cognitive Communications and Networking10.1109/TCCN.2023.332757810:2(607-621)Online publication date: Apr-2024
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Zhan JWu HCheng PZheng LChen LZhang CLin XZhang W(2024)TrendSharing: A Framework to Discover and Follow the Trends for Shared Mobility Services2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00333(4370-4382)Online publication date: 13-May-2024
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