Managing big data experiments on smartphones


The explosive number of smartphones with ever growing sensing and computing capabilities have brought a paradigm shift to many traditional domains of the computing field. Re-programming smartphones and instrumenting them for application testing and data gathering at scale is currently a tedious and time-consuming process that poses significant logistical challenges. Next generation smartphone applications are expected to be much larger-scale and complex, demanding that these undergo evaluation and testing under different real-world datasets, devices and conditions. In this paper, we present an architecture for managing such large-scale data management experiments on real smartphones. We particularly present the building blocks of our architecture that encompassed smartphone sensor data collected by the crowd and organized in our big data repository. The given datasets can then be replayed on our testbed comprising of real and simulated smartphones accessible to developers through a web-based interface. We present the applicability of our architecture through a case study that involves the evaluation of individual components that are part of a complex indoor positioning system for smartphones, coined Anyplace, which we have developed over the years. The given study shows how our architecture allows us to derive novel insights into the performance of our algorithms and applications, by simplifying the management of large-scale data on smartphones.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14


  1. 1.

    Available at

  2. 2.


  3. 3.


  4. 4.

    Free Software Foundation Europe,

  5. 5.

    Available under “Code” tab at

  6. 6.

    DMSL VCenter @ UCY.

  7. 7.


  8. 8.



  1. 1.

    Chatzimiloudis, Georgios, Konstantinidis, Andreas, Laoudias, Christos, Zeinalipour-Yazti, Demetris: Crowdsourcing with Smartphones. IEEE Internet Comput. 16(5), 36–44 (2012)

    Article  Google Scholar 

  2. 2.

    Franklin, M.J., Kossmann, D., Kraska, T., Ramesh, S., Xin, R.: CrowdDB: answering queries with crowdsourcing. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of data (SIGMOD ’11), pp. 61–72. ACM, New York (2011)

  3. 3.

    Marcus, A., Wu, E., Madden, S., Miller, R.C.: Crowdsourced databases: query processing with people. In: Proceedings of the 5th Biennial Conference on Innovative Data Systems Research (CIDR ’11) January 9–12 2011

  4. 4.

    Choffnes, D.R., Bustamante, F., Ge, Z.: Crowdsourcing service-level network event monitoring. In: Proceedings of the ACM SIGCOMM 2010 Conference (SIGCOMM ’10), pp. 387–398. ACM, New York (2011)

  5. 5.

    Konstantinidis, A., Zeinalipour-Yazti, D., Andreou, P.G., Chrysanthis, P.K., Samaras, G.: Intelligent search in social communities of smartphone users. Distrib. Parallel Databases 31, 115–149 (2013)

    Article  Google Scholar 

  6. 6.

    Money, A.: Glory and Cheap talk: analyzing strategic behavior of contestants in simultaneous crowdsourcing contests on In: Proceedings of the 19th International Conference on World Wide Web (WWW ’10), pp. 21–30. ACM, New York (2011)

  7. 7.

    Zaidan, O.F., Callison-Burch, C.: Crowdsourcing translation: professional quality from non-professionals. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics (ACL ’11), pp. 1220–1229, Stroudsburg, PA (2011)

  8. 8.

    Brew, Ay., Greene, D., Cunningham, P.: Using crowdsourcing and active learning to track sentiment in online media. In: Coelho, H., Studer, R., Wooldridge M. (eds.) Proceedings of the 19th European Conference on Artificial Intelligence (ECAI ’10), pp. 145–150. IOS Press, Amsterdam (2010)

  9. 9.

    Larkou, G., Costa, C., Andreou, P.G., Konstantinidis, A., Zeinalipour-Yazti, D.: Managing smartphone testbeds with smartlab. In: Proceedings of the 27th International Conference on Large Installation System Administration, USENIX Association, LISA’13, pp. 115–132 (2013)

  10. 10.

    Laoudias, C., Constantinou, G., Constantinides, M., Nicolaou, S., Zeinalipour-Yazti, D., Panayiotou, C.G.: The airplace indoor positioning platform for android smartphones. In: Proceedings of the 13th IEEE International Conference on Mobile Data Management (MDM’12), pp. 312–315. IEEE Computer Society, Washington, DC (2012)

  11. 11.

    Larkou, G., Mintzis, M., Taranto, S., Konstantinidis, A., Andreou, P.G., Zeinalipour-Yazti, D.: Sensor Mockup experiments with SmartLab. In: Proceedings of the 13th International Symposium on Information Processing in Sensor Networks (IPSN ’14), pp. 339–340. IEEE Press, Piscataway (2014)

  12. 12.

    Harizopoulos, S., Papadimitriou, S.: A case for micro-cellstores: energy-efficient data management on recycled smartphones. In: Proceedings of the Seventh International Workshop on Data Management on New Hardware (DaMoN’11), pp. 50–55. ACM, New York (2011)

  13. 13.

    Petrou, L., Larkou, G., Laoudias, C., Zeinalipour-Yazti, D., Panayiotou, C.G.: Crowdsourced indoor localization and navigation with anyplace. In: Proceedings of the 13th International Symposium on Information Processing in Sensor Networks (IPSN ’14), pp. 331–332. IEEE Press, Piscataway (2014)

  14. 14.

    Peterson, L., Anderson, T, Culler, D., Roscoe, T.: A blueprint for introducing disruptive technology into the internet. In: Proceedings of the ACM SIGCOMM 2003 Conference (SIGCOMM ’03), vol. 33, pp. 59–64, 1 January 2003

  15. 15.

    Johnson, D., Stack, T, Fish, R., Flickinger, D.M., Stoller, L., Ricci, R., Lepreau, J.: Mobile Emulab: a robotic wireless and sensor network testbed. In: Proceedings of the 25th IEEE International Conference on Computer Communications (INFOCOM’06), pp. 1–12. IEEE Computer Society, Washington, DC (2006)

  16. 16.

    Werner-Allen, G., Swieskowski, P., Welsh, M.: MoteLab: a wireless sensor network testbed. In: Proceedings of the 4th International Symposium on Information Processing in Sensor Networks (IPSN’05), Article 68. IEEE Press, Piscataway, NJ (2005)

  17. 17.

    Samsung, Remote Test Lab.

  18. 18.

    Perfecto Mobile.

  19. 19.

    Keynote Systems Inc., Device Anywhere.

  20. 20.

    AT&T Application Resource Optimizer (ARO), Free Diagnostic Tool.

  21. 21.

    Verry, T.: MegaDroid simulates network of 300,000 Android smartphones,, Oct 3, 2012.

  22. 22.

    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 (MobiSys’10), pp. 63–76. ACM, New York (2010)

  23. 23.

    Cuervo, E., Gilbert, P., Wu, B., Cox, L.: CrowdLab: an architecture for volunteer mobile testbeds. In: Proceedings of the 3rd International Conference on Communication Systems andNetworks (COMSNETS’11), pp. 1–10. IEEE Computer Society, Washington, DC (2011)

  24. 24.

    Baldawa, R., Benedict, M., Bulut, M.F., Challen, G., Demirbas, M., Inamdar, J., Ki, T., Ko, S.Y., Kosar, T., Mandvekar, L., Sathyaraja, A., Qiao, C., Zawicki, S.: PhoneLab: a large-scale participatory smartphone testbed (poster and demo). In: Proceedings of the 9th USENIX Conference on Networked Systems Design & Implementation (NSDI’12). USENIX Association, Berkeley, CA (2012)

  25. 25.

    Oliner, A.J., Iyer, A.P., Lagerspetz, E., Stoica, I., Tarkoma, S.: Carat: collaborative energy bug detection (poster and demo). In: Proceedings of the 9th USENIX Conference on Networked Systems Design & Implementation (NSDI’12). USENIX Association, Berkeley (2012)

  26. 26.

    Hoffer, J.A., Ramesh, V., Topi, H.: Modern Database Management. Pearson-Prentice Hall, New Jersey (2013)

  27. 27.

    Smart Metering Entity website., Jan, 2014

  28. 28.

    Popular Science: Inside Google’s Quest To Popularize Self-Driving Cars article., Jan, 2014

  29. 29.

    Zhang, C., Li, F., Jestes, J.: Efficient parallel knn joins for large data in mapreduce. In: Proceedings of the 15th International Conference on Extending Database Technology (EDBT ’12), pp. 38–49. ACM, New York (2012)

  30. 30.

    Lu, W., Shen, Y., Chen, S., Ooi, B.C.: Efficient processing of k nearest neighbor joins using mapreduce. Proc. VLDB Endow. 5(10), 1016–1027 (2012)

  31. 31.

    Kitsos, I., Magoutis, K., Tzitzikas, Y.: Scalable entity-based summarization of web search results using MapReduce. Distributed and Parallel Databases, vol. 32, pp. 405–446. Springer, New York (2014)

  32. 32.

    Hadoop website., Jan, 2014

  33. 33.

    Gu, Y., Lo, A., Niemegeers, I.: A survey of indoor positioning systems for wireless personal networks. IEEE Commun. Surv. Tutor. 11, 13–32 (2009)

    Article  Google Scholar 

  34. 34.

    Li, C.-L., Laoudias, C., Larkou, G., Chatzimilioudis, G., Zeinalipour Yazti, D., Panayiotou, C.G.: Hybrid indoor positioning on multi-sensor android smartphones. In: Proceedings of International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sydney, Australia, (2012)

  35. 35.

    Konstantinidis, A., Costa, C., Larkou, G., Zeinalipour-Yazti, D.: Demo: a programming cloud of smartphones. In: Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services (MobiSys’12), pp. 465–466. ACM, New York (2012)

  36. 36.

    Kim, H., Agrawal, N., Ungureanu, C.: Revisiting storage for smartphones. In: Proceedings of the 10th USENIX Conference on File and Storage Technologies (FAST’12), pp. 17–31. USENIX Association, Berkeley (2012)

  37. 37.

    Bahl, P., Padmanabhan, V.: RADAR: an in-building RF-based user location and tracking system. In: 19th IEEE Conference on Computer Communications (Infocom’00), pp. 775–784 (2000)

  38. 38.

    Li, B., Salter, J., Dempster, A.G., Rizos, C.: Indoor positioning techniques based on wireless LAN. In: Proceedings of the 1st IEEE International Conference on Wireless Broadband and Ultra Wideband Communications (2006)

  39. 39.

    Youssef, M., Agrawala, A.: The Horus WLAN location determination system. In: Proceedings of the 3rd International Conference on Mobile Systems, Applications, and Services (MobiSys’05), pp. 205–218. ACM, Seattle (2005)

  40. 40.

    Roos, Teemu, Myllymaki, Petri, Tirri, Henry, Misikangas, Pauli, Sievanen, Juha: A probabilistic approach to WLAN user location estimation. Int. J. Wirel Inform. Netw. 9(3), 155–164 (2002)

    Article  Google Scholar 

  41. 41.

    Portokalidis, G., Homburg, P., Anagnostakis, K., Bos, H.: Paranoid android: versatile protection for smartphones. In: Proceedings of the 26th Annual Computer Security Applications Conference (ACSAC’10), pp. 347–356. ACM, New York (2010)

Download references


We would like to thank the anonymous reviewers for their valuable comments and suggestions. This work was supported in part by the last author’s Startup Grant, funded by the University of Cyprus, COST Action IC903 (MOVE) “Knowledge Discovery for Moving Objects” EU’s FP7 (MODAP) “Mobility, Data Mining, and Privacy” projects, as well as an industrial grant by MTN Cyprus.

Author information



Corresponding author

Correspondence to Demetrios Zeinalipour-Yazti.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Larkou, G., Mintzis, M., Andreou, P.G. et al. Managing big data experiments on smartphones. Distrib Parallel Databases 34, 33–64 (2016).

Download citation


  • Experimental testbed
  • Big data
  • Sensor mockups
  • Smartphones