A Survey of Spatial Crowdsourcing

Author:

Gummidi Srinivasa Raghavendra Bhuvan1ORCID,Xie Xike2,Pedersen Torben Bach1

Affiliation:

1. Department of Computer Science, Aalborg University, Aalborg, Denmark

2. School of Computer Science and Technology, University of Science and Technology of China, Anhui, China

Abstract

Widespread use of advanced mobile devices has led to the emergence of a new class of crowdsourcing called spatial crowdsourcing. Spatial crowdsourcing advances the potential of a crowd to perform tasks related to real-world scenarios involving physical locations, which were not feasible with conventional crowdsourcing methods. The main feature of spatial crowdsourcing is the presence of spatial tasks that require workers to be physically present at a particular location for task fulfillment. Research related to this new paradigm has gained momentum in recent years, necessitating a comprehensive survey to offer a bird’s-eye view of the current state of spatial crowdsourcing literature. In this article, we discuss the spatial crowdsourcing infrastructure and identify the fundamental differences between spatial and conventional crowdsourcing. Furthermore, we provide a comprehensive view of the existing literature by introducing a taxonomy, elucidate the issues/challenges faced by different components of spatial crowdsourcing, and suggest potential research directions for the future.

Funder

Fundamental Research Funds for the Central Universities, National Natural Science Foundation of China

CAS Pioneer Hundred Talents Program

European Commission through the Erasmus Mundus Joint Doctorate ”Information Technologies for Business Intelligence—Doctoral College”

Natural Science Foundation of Jiangsu Province

Publisher

Association for Computing Machinery (ACM)

Subject

Information Systems

Reference140 articles.

1. Quality Control in Crowdsourcing Systems: Issues and Directions

2. Location-based crowdsourcing

3. Moustafa Alzantot and Moustafa Youssef. 2012. CrowdInside: Automatic construction of indoor floorplans. In GIS’12. ACM 99--108. 10.1145/2424321.2424335 Moustafa Alzantot and Moustafa Youssef. 2012. CrowdInside: Automatic construction of indoor floorplans. In GIS’12. ACM 99--108. 10.1145/2424321.2424335

4. An On-line Truthful and Individually Rational Pricing Mechanism for Ride-sharing

Cited by 61 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Comprehensive Survey on Privacy-Preserving Spatial Data Query in Transportation Systems;IEEE Transactions on Intelligent Transportation Systems;2023-12

2. Pick-up point recommendation strategy based on user incentive mechanism;PeerJ Computer Science;2023-11-20

3. Addressing fraudulent responses in online surveys: Insights from a web‐based participatory mapping study;People and Nature;2023-11-12

4. Non-Rejection Aware Online Task Assignment in Spatial Crowdsourcing;IEEE Transactions on Services Computing;2023-11

5. Personalized Location-Preference Learning for Federated Task Assignment in Spatial Crowdsourcing;Proceedings of the 32nd ACM International Conference on Information and Knowledge Management;2023-10-21

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3