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Big Data Uses in Crowd Based Systems

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Resource Management for Big Data Platforms

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Abstract

There are currently many trends in computer science, like Smart Cities, Internet of Things, and Wireless Sensor Networks. Many of these systems require or could dramatically benefit from having information about crowds. First of all, many of the systems are built to improve the life of people, and they require information about them to know when to activate their functionality in order to help them. Second, people represent a dynamic component of the entire systems, which is unpredictable. Measuring crowd dynamics is not an easy task. Each city consists of millions of individuals and their location needs to be known at all times. Furthermore, the other systems need to be able to extract the needed information for them to be able to function correctly while maintaining every individuals privacy. With crowd dynamic understood we open the way to the opportunity that is given by crowd sensing systems. Systems where data is gathered by sensors carried by individuals. Even more, crowd dynamic information can be supported by context, context that can be gathered from multiple sources, mostly available free on the Internet. With the vast amount of data on crowd dynamics and the context that surrounds them, the only option to extract information from these systems is given by Big Data. This is where Big Data meets crowd sensing. By having accurate, correct analysis of the crowd data and its context, the information extracted can be used by all other systems in order to be able to take smart decisions.

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References

  1. Aestetix, P.C.: CRAWDAD dataset hope/amd (v. 2008-08-07). http://crawdad.org/hope/amd/20080807, doi:10.15783/C7101B

  2. Aly, H., Basalamah, A., Youssef, M.: Map++: A crowd-sensing system for automatic map semantics identification. In: 2014 Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), pp. 546–554. IEEE (2014)

    Google Scholar 

  3. Amazon: Amazon Mechanical Turk (2016). https://www.mturk.com/mturk/welcome. Accessed 1 July 2016

  4. Anand, A., Manikopoulos, C., Jones, Q., Borcea, C.: A quantitative analysis of power consumption for location-aware applications on smart phones. In: 2007 IEEE International Symposium on Industrial Electronics, pp. 1986–1991. IEEE (2007)

    Google Scholar 

  5. Andrienko, G., Andrienko, N., Wrobel, S.: Visual analytics tools for analysis of movement data. ACM SIGKDD Explor. Newsl. 9(2), 38–46 (2007)

    Article  Google Scholar 

  6. Antonić, A., Marjanović, M., Pripužić, K., Žarko, I.P.: A mobile crowd sensing ecosystem enabled by cupus: Cloud-based publish/subscribe middleware for the internet of things. Future Gener. Comput. Syst. 56, 607–622 (2016)

    Article  Google Scholar 

  7. Aram, S., Troiano, A., Pasero, E.: Environment sensing using smartphone. In: Sensors Applications Symposium (SAS), 2012 IEEE, pp. 1–4. IEEE (2012)

    Google Scholar 

  8. Bajaj, R., Ranaweera, S.L., Agrawal, D.P.: Gps: location-tracking technology. Computer 35(4), 92–94 (2002)

    Article  Google Scholar 

  9. Batty, M., Axhausen, K.W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., Ouzounis, G., Portugali, Y.: Smart cities of the future. The Eur. Phys. J. Spec. Top. 214(1), 481–518 (2012)

    Article  Google Scholar 

  10. Blanke, U., Tröster, G., Franke, T., Lukowicz, P.: Capturing crowd dynamics at large scale events using participatory gps-localization. In: 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 1–7. IEEE (2014)

    Google Scholar 

  11. Bonné, B., Barzan, A., Quax, P., Lamotte, W.: Wifipi: Involuntary tracking of visitors at mass events. In: 2013 IEEE 14th International Symposium and Workshops on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp 1–6. IEEE (2013)

    Google Scholar 

  12. Bracciale, L., Bonola, M., Loreti, P., Bianchi, G., Amici, R., Rabuffi, A.: CRAWDAD dataset roma/taxi (v. 2014-07-17). http://crawdad.org/roma/taxi/20140717, doi:10.15783/C7QC7M

  13. Carbonell, J.G., Michalski, R.S., Mitchell, T.M.: An overview of machine learning. In: Machine Learning, Springer, pp. 3–23 (1983)

    Google Scholar 

  14. Cardone, G., Foschini, L., Bellavista, P., Corradi, A., Borcea, C., Talasila, M., Curtmola, R.: Fostering participaction in smart cities: a geo-social crowdsensing platform. IEEE Commun. Mag. 51(6), 112–119 (2013)

    Article  Google Scholar 

  15. Carreras, I., Miorandi, D., Tamilin, A., Ssebaggala, E.R., Conci, N.: Crowd-sensing: Why context matters. In: 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 368–371. IEEE (2013)

    Google Scholar 

  16. Carreras, I., Miorandi, D., Tamilin, A., Ssebaggala, E.R., Conci, N.: Matador: Mobile task detector for context-aware crowd-sensing campaigns. In: 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), IEEE, pp. 212–217. IEEE (2013)

    Google Scholar 

  17. ChaCha: Cha Cha (2016). http://www.chacha.com/. Accessed 29 June 2016

  18. Chilipirea, C., Petre, A., Dobre, C., Pop, F., Xhafa, F.: Enabling vehicular data with distributed machine learning. In: Transactions on Computational Collective Intelligence XIX, pp. 89–102. Springer (2015)

    Google Scholar 

  19. Chilipirea, C., Petre, A.C., Dobre, C., van Steen, M.: Filters for wi-fi generated crowd movement data. In: 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), pp. 285–290. IEEE (2015)

    Google Scholar 

  20. Chowdhury, G.G.: Natural language processing. Annu. Rev. inform. Sci. Technol. 37(1), 51–89 (2003)

    Article  Google Scholar 

  21. Constandache, I., Choudhury, R.R., Rhee, I.: Towards mobile phone localization without war-driving. In: Infocom, 2010 Proceedings IEEE, pp. 1–9. IEEE (2010)

    Google Scholar 

  22. Daggitt, M.L., Noulas, A., Shaw, B., Mascolo, C.: Tracking urban activity growth globally with big location data. R. Soc. Open Sci. 3(4),150, 688 (2016)

    Google Scholar 

  23. Demirbas, M., Bayir, M.A., Akcora, C.G., Yilmaz, Y.S., Ferhatosmanoglu, H.: Crowd-sourced sensing and collaboration using twitter. In: 2010 IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks (WoWMoM), pp. 1–9. IEEE (2010)

    Google Scholar 

  24. Eichler, S., Schroth, C., Eberspächer, J.: Car-to-car communication. In: VDE-Kongress 2006, VDE VERLAG GmbH (2006)

    Google Scholar 

  25. Evennou, F., Marx, F.: Advanced integration of wifi and inertial navigation systems for indoor mobile positioning. Eurasip J. Appl. Sig. Process. 2006, 164–164 (2006)

    Google Scholar 

  26. Farkas, K., Lendák, I.: Simulation environment for investigating crowd-sensing based urban parking. In: 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), pp. 320–327. IEEE (2015)

    Google Scholar 

  27. Faulkner, M., Olson, M., Chandy, R., Krause, J., Chandy, K.M., Krause, A.: The next big one: Detecting earthquakes and other rare events from community-based sensors. In: 2011 10th International Conference on Information Processing in Sensor Networks (IPSN), pp. 13–24. IEEE (2011)

    Google Scholar 

  28. Geocaching: Website (2016). https://www.geocaching.com. Accessed 29 June 2016

  29. Gill, M., Spriggs, A.: Assessing the Impact of CCTV. Home Office Research, Development and Statistics Directorate London (2005)

    Google Scholar 

  30. Guo, B., Yu, Z., Zhou, X., Zhang, D.: From participatory sensing to mobile crowd sensing. In: 2014 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 593–598. IEEE (2014)

    Google Scholar 

  31. 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. (CSUR) 48(1), 7 (2015)

    Article  Google Scholar 

  32. Han, K., Graham, E.A., Vassallo, D., Estrin, D.: Enhancing motivation in a mobile participatory sensing project through gaming. In: 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), pp. 1443–1448. IEEE (2011)

    Google Scholar 

  33. Hancke, G.P., Hancke Jr., G.P., et al.: The role of advanced sensing in smart cities. Sensors 13(1), 393–425 (2012)

    Article  Google Scholar 

  34. Hartmann, B., Link, N.: Gesture recognition with inertial sensors and optimized dtw prototypes. In: 2010 IEEE International Conference on Systems Man and Cybernetics (SMC), pp. 2102–2109. IEEE (2010)

    Google Scholar 

  35. IBM: IBM Big Data & Analytics Hub (2016). http://www.ibmbigdatahub.com/infographic/four-vs-big-data. Accessed 29 June 2016

  36. Jayaraman, P.P., Perera, C., Georgakopoulos, D., Zaslavsky, A.: Efficient opportunistic sensing using mobile collaborative platform mosden. In: 2013 9th International Conference Conference on Collaborative Computing: Networking, Applications and Worksharing (Collaboratecom), pp. 77–86. IEEE (2013)

    Google Scholar 

  37. Kalogianni, E., Sileryte, R., Lam, M., Zhou, K., Van der Ham, M., Van der Spek, S., Verbree, E.: Passive wifi monitoring of the rhythm of the campus. In: Proceedings of The 18th AGILE International Conference on Geographic Information Science; Geographics Information Science as an Enabler of Smarter Cities and Communities, Lisboa (Portugal), 9–14 June 2015; Authors version, Agile

    Google Scholar 

  38. Konidala, D.M., Deng, R.H., Li, Y., Lau, H.C., Fienberg, S.E.: Anonymous authentication of visitors for mobile crowd sensing at amusement parks. In: International Conference on Information Security Practice and Experience, pp. 174–188. Springer (2013)

    Google Scholar 

  39. Kotz, D., Henderson, T.: Crawdad: A community resource for archiving wireless data at dartmouth. IEEE Pervasive Comput. 4(4), 12–14 (2005)

    Article  Google Scholar 

  40. Krontiris, I., Dimitriou, T.: Privacy-respecting discovery of data providers in crowd-sensing applications. In: 2013 IEEE International Conference on Distributed Computing in Sensor Systems, pp. 249–257. IEEE (2013)

    Google Scholar 

  41. Laney, D.: 3D data management: Controlling data volume, velocity and variety. META Group Res. Note 6, 70 (2001)

    Google Scholar 

  42. Lee, J.S., Hoh, B.: Dynamic pricing incentive for participatory sensing. Pervasive Mobile Comput. 6(6), 693–708 (2010a)

    Article  Google Scholar 

  43. Lee, J.S., Hoh, B.: Sell your experiences: a market mechanism based incentive for participatory sensing. In: 2010 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 60–68. IEEE (2010)

    Google Scholar 

  44. Luo, T., Tan, H.P., Xia, L.: Profit-maximizing incentive for participatory sensing. In: IEEE INFOCOM 2014-IEEE Conference on Computer Communications, pp. 127–135. IEEE (2014)

    Google Scholar 

  45. Maisonneuve, N., Stevens, M., Ochab, B. (2010) Participatory noise pollution monitoring using mobile phones. Inf. Polity 15(1, 2), 51–71

    Google Scholar 

  46. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big Data: The Next Frontier for Innovation, Competition, and Productivity (2011)

    Google Scholar 

  47. Marfia, G., Roccetti, M.: Vehicular congestion detection and short-term forecasting: a new model with results. IEEE Trans. Veh. Technol. 60(7), 2936–2948 (2011)

    Article  Google Scholar 

  48. Mayrhofer, R., Gellersen, H.: Shake well before use: Authentication based on accelerometer data. In: International Conference on Pervasive Computing, pp. 144–161. Springer (2007)

    Google Scholar 

  49. Meng, C., Jiang, W., Li, Y., Gao, J., Su, L., Ding, H., Cheng, Y.: Truth discovery on crowd sensing of correlated entities. In: Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, pp. 169–182. ACM (2015)

    Google Scholar 

  50. Mirowski, P., Ho, T.K., Yi, S., MacDonald, M.: Signalslam: Simultaneous localization and mapping with mixed wifi, bluetooth, lte and magnetic signals. In: 2013 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–10. IEEE (2013)

    Google Scholar 

  51. Pankratius, V., Lind, F., Coster, A., Erickson, P., Semeter, J.: Mobile crowd sensing in space weather monitoring: the mahali project. IEEE Commun. Mag. 52(8), 22–28 (2014)

    Article  Google Scholar 

  52. Piorkowski, M., Sarafijanovic-Djukic, N., Grossglauser, M.: CRAWDAD dataset epfl/mobility (v. 2009-02-24). http://crawdad.org/epfl/mobility/20090224, doi:10.15783/C7J010

  53. Pournajaf, L., Xiong, L., Garcia-Ulloa, D.A., Sunderam, V.: A survey on privacy in mobile crowd sensing task management. Tech. rep., Technical Report TR-2014-002, Department of Mathe-matics and Computer Science, Emory University (2014)

    Google Scholar 

  54. Ra, M.R., Liu, B., La Porta, T.F., 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 

  55. Ruiz-Ruiz, A.J., Blunck, H., Prentow, T.S., Stisen, A., Kjærgaard, M.B.: Analysis methods for extracting knowledge from large-scale wifi monitoring to inform building facility planning. In: 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 130–138. IEEE (2014)

    Google Scholar 

  56. Schauer, L., Werner, M., Marcus, P.: Estimating crowd densities and pedestrian flows using wi-fi and bluetooth. Proceedings of the 11th International Conference on Mobile and Ubiquitous Systems: Computing, pp. 171–177. Networking and Services, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering) (2014)

    Google Scholar 

  57. Siebel, N.T., Maybank, S.: The advisor visual surveillance system. In: ECCV 2004 workshop applications of computer vision (ACV), Citeseer, vol. 1 (2004)

    Google Scholar 

  58. Starner, T.: Human-powered wearable computing. IBM Syst. J. 35(3.4), 618–629 (1996)

    Google Scholar 

  59. Tsui, A.W., Chuang, Y.H., Chu, H.H.: Unsupervised learning for solving rss hardware variance problem in wifi localization. Mob. Networks Appl. 14(5), 677–691 (2009)

    Article  Google Scholar 

  60. Wang, Y., Zhang, P., Liu, T., Sadler, C., Martonosi, M.: CRAWDAD dataset princeton/zebranet (v. 2007-02-14). http://crawdad.org/princeton/zebranet/20070214, doi:10.15783/C77C78

  61. Wang, Y., Yang, J., Liu, H., Chen, Y., Gruteser, M., Martin, R.P.: Measuring human queues using wifi signals. In: Proceedings of the 19th Annual International Conference on Mobile Computing & Networking, pp. 235–238. ACM (2013)

    Google Scholar 

  62. Wang, Y., Chen, Y., Ye, F., Yang, J., Liu, H.: Towards understanding the advertiser’s perspective of smartphone user privacy. In: 2015 IEEE 35th International Conference on Distributed Computing Systems (ICDCS), pp. 288–297. IEEE (2015)

    Google Scholar 

  63. Weppner, J., Lukowicz, P.: Bluetooth based collaborative crowd density estimation with mobile phones. In: 2013 IEEE international conference on Pervasive computing and communications (PerCom), pp. 193–200. IEEE (2013)

    Google Scholar 

  64. Wikimedia Foundation, Inc.: Wikipedia (2016). https://en.wikipedia.org/wiki/Main_Page. Accessed 28 June 2016

  65. 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 

  66. Xu, C., Li, S., Zhang, Y., Miluzzo, E., Chen, Y.F.: Crowdsensing the speaker count in the wild: Implications and applications. IEEE Commun. Mag. 52(10), 92–99 (2014)

    Article  Google Scholar 

  67. Yan, T., Marzilli, M., Holmes, R., Ganesan, D., Corner, M.: Mcrowd: a platform for mobile crowdsourcing. In: Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, pp. 347–348. ACM (2009)

    Google Scholar 

  68. Zaslavsky, A., Jayaraman, P.P., Krishnaswamy, S.: Sharelikescrowd: Mobile analytics for participatory sensing and crowd-sourcing applications. In: 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW), pp. 128–135. IEEE (2013)

    Google Scholar 

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Acknowledgments

The research presented in this paper is supported by projects: MobiWay, Mobility beyond Individualism: An Integrated Platform for Intelligent Transportation Systems of Tomorrow—PN-II-PTPCCA-2013-4-0321; DataWay, Real-time Data Processing Platform for Smart Cities: Making sense of Big Data—PN-II-RUTE-2014-4-2731. We would like to thank the reviewers for their time and expertise, constructive comments and valuable insight.

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  1. University Politehnica of Bucharest, Spl. Independentei 313, Bucharest, Romania

    Cristian Chilipirea, Andreea-Cristina Petre & Ciprian Dobre

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  1. Cristian Chilipirea
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  1. University Politehnica of Bucharest, Bucharest, Romania

    Florin Pop

  2. Cracow University of Technology, Cracow, Poland

    Joanna Kołodziej

  3. Second University of Naples, Naples, Caserta, Italy

    Beniamino Di Martino

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Chilipirea, C., Petre, AC., Dobre, C. (2016). Big Data Uses in Crowd Based Systems. In: Pop, F., Kołodziej, J., Di Martino, B. (eds) Resource Management for Big Data Platforms. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-44881-7_21

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