Spatial Crowdsourcing: Challenges and Opportunities

As one of the successful forms of using Wisdom of Crowd, crowdsourcing, has been widely used for many human intrinsic tasks, such as image labeling, natural language understanding, market predication and opinion mining. Meanwhile, with advances in pervasive technology, mobile devices, such as mobile phones, tablets, and PDA, have become extremely popular. These mobile devices can work as sensors to collect various types of data, such as pictures, videos, audios and texts. Therefore, in crowdsourcing, a requester can unitize power of mobile devices and their location information to ask for data related a specific location, subsequently, the mobile users who would like to perform the task will travel to the target location and collect the data (videos, audios, or pictures), which is then sent to the requester. This type of crowdsourcing is called spatial crowdsourcing. Due to the pervasiveness of mobile devices and their superb functionality, spatial crowdsourcing is gaining more attention than general crowdsourcing platforms, such as Amazon Turk(http://www.mturk.com) and Crowdflower (http://crowdflower.com/). However, to develop a spatial crowdsourcing platform, effective and efficient solutions for motivating workers, mining workers’ profiles, assigning tasks, aggregating results and controlling data quality must be developed. Therefore, in this paper, we will discuss the challenges and opportunities related to these key techniques, including 1) effective incentive mechanisms to encourage mobile device users to participate in crowdsourcing tasks; 2) automatic user profile mining methods; 3) optimal task assignment solutions; 4) novel answer aggregation models; 5) intelligent data quality

[1]  Daren C. Brabham.  Crowdsourcing as a Model for Problem Solving , 2008 .

[2]  Alireza Sahami Shirazi,et al.  Location-based crowdsourcing: extending crowdsourcing to the real world , 2010, NordiCHI.

[3]  Ted S. Sindlinger,et al.  Crowdsourcing: Why the Power of the Crowd is Driving the Future of Business , 2010 .

[4]  Kenton O'Hara,et al.  Gamification. using game-design elements in non-gaming contexts , 2011, CHI Extended Abstracts.

[5]  Lei Chen,et al.  Differential dependencies: Reasoning and discovery , 2011, TODS.

[6]  Ugur Demiryurek,et al.  Maximizing the number of worker's self-selected tasks in spatial crowdsourcing , 2013, SIGSPATIAL/GIS.

[7]  Jizhong Zhao,et al.  Task Assignment on Multi-Skill Oriented Spatial Crowdsourcing , 2015, IEEE Transactions on Knowledge and Data Engineering.

[8]  Cyrus Shahabi,et al.  GeoCrowd: enabling query answering with spatial crowdsourcing , 2012, SIGSPATIAL/GIS.

[9]  Xiang Lian,et al.  Prediction-Based Task Assignment in Spatial Crowdsourcing , 2015, 2017 IEEE 33rd International Conference on Data Engineering (ICDE).

[10]  Vincent Conitzer,et al.  Determining Possible and Necessary Winners under Common Voting Rules Given Partial Orders , 2008, AAAI.

[11]  Irwin King,et al.  A brief survey of computational approaches in Social Computing , 2009, 2009 International Joint Conference on Neural Networks.

[12]  Andrew Raij,et al.  Exploring micro-incentive strategies for participant compensation in high-burden studies , 2011, UbiComp '11.

[13]  Lei Chen,et al.  From labor to trader: opinion elicitation via online crowds as a market , 2014, KDD.

[14]  Peng Dai,et al.  Decision-Theoretic Control of Crowd-Sourced Workflows , 2010, AAAI.

[15]  Chrysanthos Dellarocas,et al.  Harnessing Crowds: Mapping the Genome of Collective Intelligence , 2009 .

[16]  Björn Hartmann,et al.  What's the Right Price? Pricing Tasks for Finishing on Time , 2011, Human Computation.

[17]  Philip S. Yu,et al.  On data dependencies in dataspaces , 2011, 2011 IEEE 27th International Conference on Data Engineering.

[18]  Benjamin B. Bederson,et al.  Human computation: a survey and taxonomy of a growing field , 2011, CHI.

[19]  Christoph Schlieder,et al.  Geogames: Designing Location-Based Games from Classic Board Games , 2006, IEEE Intelligent Systems.

[20]  E. David,et al.  Networks, Crowds, and Markets: Reasoning about a Highly Connected World , 2010 .

[21]  Lei Chen,et al.  Whom to Ask? Jury Selection for Decision Making Tasks on Micro-blog Services , 2012, Proc. VLDB Endow..

[22]  Cyrus Shahabi,et al.  A Framework for Protecting Worker Location Privacy in Spatial Crowdsourcing , 2014, Proc. VLDB Endow..

[23]  Lei Chen,et al.  WiseMarket: a new paradigm for managing wisdom of online social users , 2013, KDD.

[24]  Lei Chen,et al.  Online mobile Micro-Task Allocation in spatial crowdsourcing , 2016, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).

[25]  Lei Chen,et al.  Free Market of Crowdsourcing: Incentive Mechanism Design for Mobile Sensing , 2014, IEEE Transactions on Parallel and Distributed Systems.

[26]  Hong Cheng,et al.  Parameter-Free Determination of Distance Thresholds for Metric Distance Constraints , 2012, 2012 IEEE 28th International Conference on Data Engineering.

[27]  David Geer,et al.  E-Micropayments Sweat the Small Stuff , 2004, Computer.

[28]  Zhifeng Bao,et al.  Crowdsourced POI labelling: Location-aware result inference and Task Assignment , 2016, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).

[29]  Jon M. Kleinberg,et al.  Networks, Crowds, and Markets: Reasoning about a Highly Connected World [Book Review] , 2013, IEEE Technol. Soc. Mag..

[30]  Lei Chen,et al.  gMission: A General Spatial Crowdsourcing Platform , 2014, Proc. VLDB Endow..

[31]  Panagiotis G. Ipeirotis,et al.  Estimating the Completion Time of Crowdsourced Tasks Using Survival Analysis Models , 2011 .

[32]  Murat Demirbas,et al.  Crowdsourcing location-based queries , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[33]  Ramachandran Ramjee,et al.  Nericell: rich monitoring of road and traffic conditions using mobile smartphones , 2008, SenSys '08.

[34]  Lei Chen,et al.  Mutual benefit aware task assignment in a bipartite labor market , 2016, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).

[35]  Philip S. Yu,et al.  Comparable dependencies over heterogeneous data , 2012, The VLDB Journal.

[36]  Lei Chen,et al.  Structure Tensor Series-Based Large Scale Near-Duplicate Video Retrieval , 2012, IEEE Transactions on Multimedia.

[37]  Yuandong Tian,et al.  Learning from crowds in the presence of schools of thought , 2012, KDD.

[38]  Coye Cheshire,et al.  The Social Psychological Effects of Feedback on the Production of Internet Information Pools , 2008, J. Comput. Mediat. Commun..

[39]  Deepak Ganesan,et al.  TruCentive: A game-theoretic incentive platform for trustworthy mobile crowdsourcing parking services , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[40]  Lei Chen,et al.  Tuning Crowdsourced Human Computation , 2017, 2017 IEEE 33rd International Conference on Data Engineering (ICDE).

[41]  Cyrus Shahabi,et al.  A privacy-aware framework for participatory sensing , 2011, SKDD.

[42]  Cyrus Shahabi,et al.  MediaQ: mobile multimedia management system , 2014, MMSys '14.

[43]  Jian Peng,et al.  Variational Inference for Crowdsourcing , 2012, NIPS.

[44]  Mausam,et al.  Crowdsourcing Control: Moving Beyond Multiple Choice , 2012, UAI.

[45]  Duncan J. Watts,et al.  Financial incentives and the "performance of crowds" , 2009, HCOMP '09.

[46]  Chen-Khong Tham,et al.  Fairness and social welfare in incentivizing participatory sensing , 2012, 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON).

[47]  Jizhong Zhao,et al.  Reliable Diversity-Based Spatial Crowdsourcing by Moving Workers , 2014, Proc. VLDB Endow..