Distributed and Parallel Databases

, Volume 34, Issue 1, pp 33–64 | Cite as

Managing big data experiments on smartphones

  • Georgios Larkou
  • Marios Mintzis
  • Panayiotis G. Andreou
  • Andreas Konstantinidis
  • Demetrios Zeinalipour-Yazti
Article

Abstract

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.

Keywords

Experimental testbed Big data Sensor mockups  Smartphones 

References

  1. 1.
    Chatzimiloudis, Georgios, Konstantinidis, Andreas, Laoudias, Christos, Zeinalipour-Yazti, Demetris: Crowdsourcing with Smartphones. IEEE Internet Comput. 16(5), 36–44 (2012)CrossRefGoogle 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)Google Scholar
  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 2011Google Scholar
  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)Google Scholar
  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)CrossRefGoogle Scholar
  6. 6.
    Money, A.: Glory and Cheap talk: analyzing strategic behavior of contestants in simultaneous crowdsourcing contests on TopCoder.com. In: Proceedings of the 19th International Conference on World Wide Web (WWW ’10), pp. 21–30. ACM, New York (2011)Google Scholar
  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)Google Scholar
  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)Google Scholar
  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)Google Scholar
  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)Google Scholar
  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)Google Scholar
  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)Google Scholar
  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)Google Scholar
  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 2003Google Scholar
  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)Google Scholar
  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)Google Scholar
  17. 17.
    Samsung, Remote Test Lab. http://goo.gl/p7SNU
  18. 18.
    Perfecto Mobile. http://goo.gl/DSlP9
  19. 19.
    Keynote Systems Inc., Device Anywhere. http://goo.gl/mCxFt
  20. 20.
    AT&T Application Resource Optimizer (ARO), Free Diagnostic Tool. http://goo.gl/FZnXS
  21. 21.
    Verry, T.: MegaDroid simulates network of 300,000 Android smartphones, Extremetech.com, Oct 3, 2012. http://goo.gl/jMaS8
  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)Google Scholar
  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)Google Scholar
  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)Google Scholar
  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)Google Scholar
  26. 26.
    Hoffer, J.A., Ramesh, V., Topi, H.: Modern Database Management. Pearson-Prentice Hall, New Jersey (2013)Google Scholar
  27. 27.
    Smart Metering Entity website. http://www.smi-ieso.ca/mdmr, Jan, 2014
  28. 28.
    Popular Science: Inside Google’s Quest To Popularize Self-Driving Cars article. http://www.popsci.com/cars/article/2013-09/google-self-driving-car, 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)Google Scholar
  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)Google Scholar
  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)Google Scholar
  32. 32.
    Hadoop website. http://hadoop.apache.org/, 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)CrossRefGoogle 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)Google Scholar
  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)Google Scholar
  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)Google Scholar
  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)Google Scholar
  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)Google Scholar
  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)Google Scholar
  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)CrossRefGoogle 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)Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Georgios Larkou
    • 1
  • Marios Mintzis
    • 1
  • Panayiotis G. Andreou
    • 1
  • Andreas Konstantinidis
    • 1
  • Demetrios Zeinalipour-Yazti
    • 1
  1. 1.Department of Computer ScienceUniversity of CyprusNicosiaCyprus

Personalised recommendations