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GeoInformatica

, Volume 20, Issue 3, pp 529–568 | Cite as

Task selection in spatial crowdsourcing from worker’s perspective

  • Dingxiong DengEmail author
  • Cyrus Shahabi
  • Ugur Demiryurek
  • Linhong Zhu
Article

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.

Keywords

Crowdsourcing Spatial crowdsourcing Spatial task assignment 

Notes

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.

References

  1. 1.
  2. 2.
  3. 3.
    Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. VLDB ’94. San Francisco, pp 487–499. http://dl.acm.org/citation.cfm?id=645920.672836
  4. 4.
    Alfarrarjeh A, Emrich T, Shahabi C (2014) Scalable spatial crowdsourcing: A study of distributed algorithms. MDM ’15Google Scholar
  5. 5.
    Alt F, Shirazi AS, Schmidt A, Kramer U, Nawaz Z (2010) Location-based crowdsourcing: extending crowdsourcing to the real world. NordiCHI ’10. NY, pp 13–22. doi:10.1145/1868914.1868921
  6. 6.
    Bozzon A, Brambilla M, Ceri S, Mazza D (2013) Exploratory search framework for web data sources. VLDB J 22(5):641–663CrossRefGoogle Scholar
  7. 7.
    Bulut M, Yilmaz Y, Demirbas M (2011) Crowdsourcing location-based queries. In: PERCOM workshops. doi: 10.1109/PERCOMW.2011.5766944, pp 513–518
  8. 8.
    Chen C, Cheng SF, Gunawan A, Misra A, Dasgupta K, Chander D (2014) Traccs: a framework for trajectory-aware coordinated urban crowd-sourcing. In: 2nd AAAI conference on human computation and crowdsourcingGoogle Scholar
  9. 9.
    Chen C, Cheng SF, Misra A, Lau HC (2015) Multi-agent task assignment for mobile crowdsourcing under trajectory uncertainties. In: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, AAMAS ’15. http://dl.acm.org/citation.cfm?id=2772879.2773400. International Foundation for Autonomous Agents and Multiagent Systems, Richland, pp 1715–1716
  10. 10.
    Chen Z, Fu R, Zhao Z, Liu Z, Xia L, Chen L, Cheng P, Cao CC, Tong Y, Zhang CJ (2014) gmission: A general spatial crowdsourcing platform. PVLDBGoogle Scholar
  11. 11.
    Dabrowski JR, Munson EV (2001) Is 100 milliseconds too fast?. In: CHI ’01 extended abstracts on human factors in computing systems, CHI EA ’01, pp 317–318Google Scholar
  12. 12.
    Demartini G, Difallah DE, Cudré-Mauroux P (2013) Large-scale linked data integration using probabilistic reasoning and crowdsourcing. VLDB J 22(5):665–687CrossRefGoogle Scholar
  13. 13.
    Deng D, Shahabi C, Demiryurek U (2013) Maximizing the number of worker’s self-selected tasks in spatial crowdsourcing. In: SIGSPATIAL’13. doi:10.1145/2525314.2525370, pp 314–323
  14. 14.
    Deng D, Shahabi C, Zhu L (2015) Task matching and scheduling for multiple workers in spatial crowdsourcing. In: SIGSPATIAL’15. doi:10.1145/2525314.2525370
  15. 15.
    Doan A, Ramakrishnan R, Halevy AY (2011) Crowdsourcing systems on the world-wide web. Commun ACM 54(4):86–96CrossRefGoogle Scholar
  16. 16.
    Franklin MJ, Kossmann D, Kraska T, Ramesh S, Xin R (2011) Crowddb: answering queries with crowdsourcing. SIGMOD ’11. NY, pp 61–72Google Scholar
  17. 17.
    Grady C, Lease M (2010) Crowdsourcing document relevance assessment with mechanical turk. NAACL HLT ’10. PA, pp 172–179Google Scholar
  18. 18.
    Guo S, Parameswaran A, Garcia-Molina H (2012) So who won?: dynamic max discovery with the crowd. SIGMOD ’12. NY, pp 385–396Google Scholar
  19. 19.
    Hull B, Bychkovsky V, Zhang Y, Chen K, Goraczko M, Miu A, Shih E, Balakrishnan H, Madden S (2006) Cartel: a distributed mobile sensor computing system. SenSys ’06. NY, pp 125–138. doi:10.1145/1182807.1182821
  20. 20.
    Kantor MG, Rosenwein MB (1992) The orienteering problem with time windows. J Oper Res Soc 629–635Google Scholar
  21. 21.
    Kazemi L, Shahabi C A privacy-aware framework for participatory sensing. SIGKDD Explor ’11 13(1):43–51Google Scholar
  22. 22.
    Kazemi L, Shahabi C (2012) Geocrowd: enabling query answering with spatial crowdsourcing. In: SIGSPATIAL ’12. doi:10.1145/2424321.2424346. NY, pp 189–198
  23. 23.
    Kazemi L, Shahabi C, Chen L (2013) Geotrucrowd: Trustworthy query answering with spatial crowdsourcing. In: SIGSPATIAL’13, pp 304–313Google Scholar
  24. 24.
    Layla Pournajaf LX, Sunderam V (2014) Dynamic data driven crowd sensing task assignment. Proc Comput Sci 29:1314–1323. doi: 10.1016/j.procs.2014.05.118. http://www.sciencedirect.com/science/article/pii/S1877050914002956. 2014 International Conference on Computational ScienceCrossRefGoogle Scholar
  25. 25.
    Lee SM, Asllani AA (2004) Job scheduling with dual criteria and sequence-dependent setups: mathematical versus genetic programming. Omega 32 (2):145–153. doi: 10.1016/j.omega.2003.10.001. http://www.sciencedirect.com/science/article/pii/S0305048303001324 CrossRefGoogle Scholar
  26. 26.
    Li F, Cheng D, Hadjieleftheriou M, Kollios G, Teng SH (2005) On trip planning queries in spatial databases. SSTD’05. Berlin, pp 273–290. doi: 10.1007/11535331_16
  27. 27.
    Li Y, Deng D, Demiryurek U, Shahabi C, Ravada S (2015) Towards fast and accurate solutions to vehicle routing in a large-scale and dynamic environment. In: SSTD, vol 9239, pp 119–136Google Scholar
  28. 28.
    Li Y, Yiu ML, Xu W (2015) Oriented online route recommendation for spatial crowdsourcing task workers. In: Advances in spatial and temporal databases. Springer, pp 137–156Google Scholar
  29. 29.
    Marcus A, Wu E, Madden S, Miller RC (2011) Crowdsourced databases: query processing with people. In: CIDR, pp 211–214Google Scholar
  30. 30.
    Mohan P, Padmanabhan VN, Ramjee R (2008) Nericell: rich monitoring of road and traffic conditions using mobile smartphones. SenSys ’08. NY, pp 323–336. doi:10.1145/1460412.1460444
  31. 31.
    Moore JM (1968) An n job, one machine sequencing algorithm for minimizing the number of late jobs. Manag Sci 15(1):102–109. http://www.jstor.org/stable/2628449 CrossRefGoogle Scholar
  32. 32.
    Musthag M, Ganesan D (2013) Labor dynamics in a mobile micro-task market. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 641–650Google Scholar
  33. 33.
    Pan B, Zheng Y, Wilkie D, Shahabi C (2013) Crowd sensing of traffic anomalies based on human mobility and social media. In: SIGSPATIAL’13. doi:10.1145/2525314.2525343, pp 334–343
  34. 34.
    Papadimitriou CH (1977) The euclidean travelling salesman problem is np-complete. Theor Comput Sci 4(3):237–244. doi: 10.1016/0304-3975(77)90012-3. http://www.sciencedirect.com/science/article/pii/0304397577900123 CrossRefGoogle Scholar
  35. 35.
    Pournajaf L, Xiong L, Sunderam V, Goryczka S (2014) Spatial task assignment for crowd sensing with cloaked locations. In: Proceedings of the 2014 IEEE 15th international conference on mobile data management, MDM ’14. doi: 10.1109/MDM.2014.15, vol 1. IEEE Computer Society, Washington, DC, pp 73–82
  36. 36.
    Sharifzadeh M, Kolahdouzan M, Shahabi C (2008) The optimal sequenced route query. VLDB J 17(4):765–787. doi: 10.1007/s00778-006-0038-6 CrossRefGoogle Scholar
  37. 37.
    Snow R, O’Connor B, Jurafsky D, Ng AY (2008) Cheap and fast—but is it good?: evaluating non-expert annotations for natural language tasks. EMNLP ’08. PA, pp 254–263Google Scholar
  38. 38.
    Teodoro R, Ozturk P, Naaman M, Mason W, Lindqvist J (2014) The motivations and experiences of the on-demand mobile workforce. In: Proceedings of the 17th ACM conference on computer supported cooperative work & social computing. ACM, pp 236–247Google Scholar
  39. 39.
    Terrovitis M, Bakiras S, Papadias D, Mouratidis K (2005) Constrained shortest path computation. In: SSTD’05. doi: 10.1007/11535331_11, vol 3633, pp 181–199
  40. 40.
    To H, Ghinita G, Shahabi C (2014) A framework for protecting worker location privacy in spatial crowdsourcing. Proc VLDB Endowment 7(10)Google Scholar
  41. 41.
    To H, Shahabi C, Kazemi L (2015) A server-assigned spatial crowdsourcing framework. ACM Trans Spatial Algorithms Syst 1(1):2:1–2:28. doi:10.1145/2729713 Google Scholar
  42. 42.
    Wang Y, Huang Y, Louis C (2013) Towards a framework for privacy-aware mobile crowdsourcing. In: International conference on social computing (SocialCom), 2013. IEEE, pp 454–459Google Scholar
  43. 43.
    Yan T, Kumar V, Ganesan D (2010) Crowdsearch: exploiting crowds for accurate real-time image search on mobile phones. MobiSys ’10. NY, pp 77–90Google Scholar
  44. 44.
    Yang K, Zhang K, Ren J, Shen X (2015) Security and privacy in mobile crowdsourcing networks: challenges and opportunities. IEEE Commun Mag 53(8):75–81. doi: 10.1109/MCOM.2015.7180511 CrossRefGoogle Scholar
  45. 45.
    Zenonos A, Stein S, Jennings NR (2015) Coordinating measurements for air pollution monitoring in participatory sensing settings. In: Proceedings of the 2015 international conference on autonomous agents and multiagent systems. International Foundation for Autonomous Agents and Multiagent Systems, pp 493–501Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Dingxiong Deng
    • 1
    Email author
  • Cyrus Shahabi
    • 1
  • Ugur Demiryurek
    • 1
  • Linhong Zhu
    • 2
  1. 1.Computer Science DepartmentUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Information Sciences InstituteUniversity of Southern CaliforniaMarina Del ReyUSA

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