World Wide Web

, Volume 21, Issue 3, pp 759–782 | Cite as

GP-selector: a generic participant selection framework for mobile crowdsourcing systems

  • Jiangtao Wang
  • Yasha Wang
  • Leye Wang
  • Yuanduo He
Part of the following topical collections:
  1. Special Issue on Mobile Crowdsourcing


Participant selection is a common and crucial function for mobile crowdsourcing (MCS) systems or platforms. This paper introduces a generic framework, named GP-Selector, to handle the participant selection from MCS task creation time to runtime. Compared to existing approaches, ours has the following two unique features. 1) In the task creation time, it assists task creators with diverse levels of programming skills to define basic requirements of participant selection. 2) In the runtime, it adopts a two-phase selection process to select participants who not only meet the basic requirements but also are willing to accept the task. Specifically, we utilize the state-of-the-art techniques including ontology modeling, end-user programming and multi-classifier fusion to implement GP-Selector. We evaluate GP-Selector extensively in three aspects: the end-user task creation, the expressiveness of the core ontology model, and the willingness-based selection algorithm. The evaluation results demonstrate the usability and effectiveness.


Mobile crowdsourcing Mobile crowdsensing Participant selection 



This work is supported by the Key Program of National Natural Science Foundation of China (91546203) and Chinese Postdoctoral Science Foundation (2016M600014).


  1. 1.
    Aanensen, D.M., Huntley, D.M., Feil, E.J., Spratt, B.G., et al.: Epicollect: linking smartphones to Web applications for epidemiology, ecology and community data collection. PloS one 4(9), e6968 (2009)CrossRefGoogle Scholar
  2. 2.
    Ahmadi, H., Pham, N., Ganti, R., Abdelzaher, T., Nath, S., Han, J.: Privacy-aware regression modeling of participatory sensing data. In: Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, pp 99–112. ACM (2010)Google Scholar
  3. 3.
    Brooke, J., et al.: Sus-a quick and dirty usability scale. Usability evaluation in industry 189(194), 4–7 (1996)Google Scholar
  4. 4.
    Burke, J.A., Estrin, D., Hansen, M., Parker, A., Ramanathan, N., Reddy, S., Srivastava, M.B.: Participatory sensing. Center for Embedded Network Sensing (2006)Google Scholar
  5. 5.
    Cardone, G., Foschini, L., Bellavista, P., Corradi, A., Borcea, C., Talasila, M., Curtmola, R.: Fostering participation in smart cities: a geo-social crowdsensing platform. IEEE Commun. Mag. 51(6), 112–119 (2013)CrossRefGoogle Scholar
  6. 6.
    Carrapetta, J., Youdale, N., Chow, A., Sivaraman, V.: Haze watch project. Online: (accessed in January 2011) (2010)
  7. 7.
    Chen, C., Zhang, D., Ma, X., Guo, B., Wang, L., Wang, Y., Sha, E.: Crowddeliver: planning city-wide package delivery paths leveraging the crowd of taxis. IEEE Trans. Intell. Transp. Syst. PP(99), 1–19 (2016)CrossRefGoogle Scholar
  8. 8.
    Cornelius, C., Kapadia, A., Kotz, D., Peebles, D., Shin, M., Triandopoulos, N.: Anonysense: privacy-aware people-centric sensing. In: Proceedings of the 6Th International Conference on Mobile Systems, Applications, and Services, pp 211–224. ACM (2008)Google Scholar
  9. 9.
    Cuervo, E., Gilbert, P., Wu, B., Cox, L.P.: Crowdlab: an architecture for volunteer mobile testbeds. In: Third International Conference on Communication Systems and Networks (COMSNETS), 2011, pp 1–10. IEEE (2011)Google Scholar
  10. 10.
    Das, T., Mohan, P., Padmanabhan, V.N., Ramjee, R., Sharma, A.: Prism: platform for remote sensing using smartphones. In: Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, pp 63–76. ACM (2010)Google Scholar
  11. 11.
    Döbrich, U., Noury, P.: ESPRIT Project NOAH Introduction. Springer (1999)Google Scholar
  12. 12.
    Dong, Y.F., Kanhere, S., Chou, C.T., Bulusu, N.: Automatic collection of fuel prices from a network of mobile cameras. In: Distributed Computing in Sensor Systems, pp 140–156. Springer (2008)Google Scholar
  13. 13.
    Guo, B., Wang, Z., Yu, Z., Wang, Y., Yen, N.Y., Huang, R., Zhou, X.: Mobile crowd sensing and computing: the review of an emerging human-powered sensing paradigm. ACM Comput. Surv. 48(1), 7 (2015)CrossRefGoogle Scholar
  14. 14.
    Guo, B., Chen, H., Han, Q., Yu, Z., Zhang, D., Wang, Y.: Worker-contributed data utility measurement for visual crowdsensing systems. IEEE Trans. Mob. Comput. PP(99), 1–1 (2016)Google Scholar
  15. 15.
    Gustarini, M., Wac, K., Dey, A.K.: Anonymous smartphone data collection: factors influencing the users’ acceptance in mobile crowd sensing. Pers. Ubiquit. Comput. 20(1), 65–82 (2016)CrossRefGoogle Scholar
  16. 16.
    Hartung, C., Lerer, A., Anokwa, Y., Tseng, C., Brunette, W., Borriello, G.: Open data kit: tools to build information services for developing regions. In: Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development, p 18. ACM (2010)Google Scholar
  17. 17.
    He, S., Shin, D.H., Zhang, J., Chen, J.: Toward optimal allocation of location dependent tasks in crowdsensing. In: INFOCOM, 2014 Proceedings IEEE, pp 745–753. IEEE (2014)Google Scholar
  18. 18.
    Heggen, S., Adagale, A., Payton, J.: Lowering the barrier for crowdsensing application development. In: Mobile Computing, Applications, and Services, pp 1–18. Springer (2013)Google Scholar
  19. 19.
    Howe, J.: The rise of crowdsourcing. 06 Jenkins H Convergence Culture Where Old and New Media Collide 14(14), 1–5 (2006)Google Scholar
  20. 20.
    Joki, A., Burke, J.A., Estrin, D.: Campaignr: a framework for participatory data collection on mobile phones. Center for Embedded Network Sensing (2007)Google Scholar
  21. 21.
    Kalyanpur, A., Pastor, D.J., Battle, S., Padget, J.A.: Automatic mapping of owl ontologies into java. In: SEKE, Citeseer, vol. 4, pp 98–103 (2004)Google Scholar
  22. 22.
    Kazai, G.: In search of quality in crowdsourcing for search engine evaluation (2011)Google Scholar
  23. 23.
    Kim, S., Mankoff, J., Paulos, E.: Sensr: evaluating a flexible framework for authoring mobile data-collection tools for citizen science. In: Proceedings of the 2013 Conference on Computer Supported Cooperative Work, pp 1453–1462. ACM (2013)Google Scholar
  24. 24.
    Lee, J.S., Hoh, B.: Dynamic pricing incentive for participatory sensing. Pervasive Mob. Comput. 6(6), 693–708 (2010)CrossRefGoogle Scholar
  25. 25.
    Mohan, P., Padmanabhan, V.N., Ramjee, R.: Nericell: rich monitoring of road and traffic conditions using mobile smartphones. In: Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems, pp 323–336. ACM (2008)Google Scholar
  26. 26.
    Narasimhamurthy, A.: Theoretical bounds of majority voting performance for a binary classification problem. IEEE Trans. Pattern Anal. Mach. Intell. 27(12), 1988–1995 (2005)CrossRefGoogle Scholar
  27. 27.
    Ra, M.R., Liu, B., La, Porta TF, Govindan, R.: Medusa: a programming framework for crowd-sensing applications. In: Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, pp 337–350. ACM (2012)Google Scholar
  28. 28.
    Ramanathan, N., Alquaddoomi, F., Falaki, H., George, D., Hsieh, C.K., Jenkins, J., Ketcham, C., Longstaff, B., Ooms, J., Selsky, J., et al.: Ohmage: an open mobile system for activity and experience sampling. In: 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2012, pp 203–204. IEEE (2012)Google Scholar
  29. 29.
    Rana, R.K., Chou, C.T., Kanhere, S.S., Bulusu, N., Hu, W.: Ear-phone: an end-to-end participatory urban noise mapping system. In: Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, pp 105–116. ACM (2010)Google Scholar
  30. 30.
    Ravindranath, L., Thiagarajan, A., Balakrishnan, H., Madden, S.: Code in the air: simplifying sensing and coordination tasks on smartphones. In: Proceedings of the Twelfth Workshop on Mobile Computing Systems & Applications, p 4. ACM (2012)Google Scholar
  31. 31.
    Reddy, S., Shilton, K., Burke, J., Estrin, D., Hansen, M.H., Srivastava, M.B.: Using context annotated mobility profiles to recruit data collectors in participatory sensing. In: Location and Context Awareness, International Symposium, Loca 2009, Tokyo, Japan, May 7–8, 2009, Proceedings, pp 52–69 (2009)Google Scholar
  32. 32.
    Reddy, S., Shilton, K., Denisov, G., Cenizal, C., Estrin, D., Srivastava, M.: Biketastic: sensing and mapping for better biking. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp 1817–1820. ACM (2010)Google Scholar
  33. 33.
    Song, Z., Zhang, B., Liu, C.H., Vasilakos, A.V., Ma, J., Wang, W.: Qoi-aware energy-efficient participant selection. In: Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), 2014, pp 248–256. IEEE (2014)Google Scholar
  34. 34.
    Usability of the end user development tool., accessed: 2010-09-30 (2016)
  35. 35.
    Väätäjä, H., Sirkkunen, E., Ahvenainen, M.: A field trial on mobile crowdsourcing of news content factors influencing participation. In: Human-Computer Interaction–INTERACT 2013, pp 54–73. Springer (2013)Google Scholar
  36. 36.
    Wang, J., Helal, S., Wang, Y., Zhang, D.: Wselector: a multi-scenario and multi-view worker selection framework for crowd-sensing. In: UIC, pp 54–61 (2015)Google Scholar
  37. 37.
    Wang, J., Wang, Y., Zhao, J.: Helping campaign initiators create mobile crowd sensing apps: a supporting framework. In: IEEE 39Th Annual Computer Software and Applications Conference (COMPSAC), 2015, vol. 2. IEEE (2015)Google Scholar
  38. 38.
    Wang, J., Wang, Y., Helal, S., Zhang, D.: A context-driven worker selection framework for crowd-sensing. Int. J. Distrib. Sens. Netw. 2016(3), 1–16 (2016)Google Scholar
  39. 39.
    Wang, J., Wang, Y., Zhang, D., Wang, L., Chen, C., Lee, J.W., He, Y.: Real-time and generic queue time estimation based on mobile crowdsensing. Front. Comp. Sci. 1–12 (2016)Google Scholar
  40. 40.
    Wang, J., Wang, Y., Zhang, D., Wang, L., Xiong, H., Helal, S., He, Y., Wang, F.: Fine-grained multi-task allocation for participatory sensing with a shared budget. IEEE Internet of Things Journal PP(99), 1–1 (2016)Google Scholar
  41. 41.
    Wang, J., Wang, Y., Zhang, D., Wang, F., He, Y., Ma, L.: Psallocator: multi-task allocation for participatory sensing with sensing capability constraints. In: ACM Conference on Computer Supported Cooperative Work and Social Computing, pp 1139–1151 (2017)Google Scholar
  42. 42.
    Wang, Y., Wang, J., Zhang, X.: Qtime: a queuing-time notification system based on participatory sensing data. In: IEEE 37th Annual Computer Software and Applications Conference (COMPSAC), 2013, pp 770–777. IEEE (2013)Google Scholar
  43. 43.
    Wang, L., Zhang, D., Wang, Y., Chen, C., Han, X., M’hamed, A.: Sparse mobile crowdsensing: challenges and opportunities. IEEE Commun. Mag. 54(7), 161–167 (2016)CrossRefGoogle Scholar
  44. 44.
    Wong, J.: Marmite: towards end-user programming for the Web. In: IEEE Symposium on Visual Languages and Human-Centric Computing, 2007. VL/HCC 2007, pp 270–271. IEEE (2007)Google Scholar
  45. 45.
    Xiao, Y., Simoens, P., Pillai, P., Ha, K., Satyanarayanan, M.: Lowering the barriers to large-scale mobile crowdsensing. In: Proceedings of the 14th Workshop on Mobile Computing Systems and Applications, p 9. ACM (2013)Google Scholar
  46. 46.
    Yu, Z., Xu, H., Yang, Z., Guo, B.: Personalized travel package with multi-point-of-interest recommendation based on crowdsourced user footprints. IEEE Transactions on Human-Machine Systems 46(1), 151–158 (2016)CrossRefGoogle Scholar
  47. 47.
    Zhang, D., Xiong, H., Wang, L., Chen, G.: Crowdrecruiter: selecting participants for piggyback crowdsensing under probabilistic coverage constraint. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp 703–714. ACM (2014)Google Scholar
  48. 48.
    Zhou, P., Zheng, Y., Li, M.: How long to wait?: predicting bus arrival time with mobile phone based participatory sensing. In: Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, pp 379–392. ACM (2012)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Key Laboratory of High Confidence Software TechnologiesMinistry of EducationBeijingChina
  2. 2.School of Electronics Engineering and Computer SciencePeking UniversityBeijingChina
  3. 3.National Engineering Research Center of Software EngineeringPeking UniversityBeijingChina
  4. 4.Beida(Binhai) Information ResearchTianjingChina
  5. 5.Department of Computer ScienceHong Kong University of Science and TechnologyHong Kong SARChina

Personalised recommendations