Advertisement

Social-Sensor Cloud Service for Scene Reconstruction

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10601)

Abstract

We propose a new social-sensor cloud services selection framework for scene reconstruction. The proposed research represents social media data streams, i.e., images’ metadata and related posted information, as social sensor cloud services. The functional and non-functional aspects of social sensor cloud services are abstracted from images’ metadata and related posted information. The proposed framework is a 4-stage algorithm, to select social-sensor cloud services based on the user queries. The selection algorithm is based on spatio-temporal indexing, spatio-temporal and textual correlations, and quality of services. Analytical results are presented to prove the efficiency of the proposed approach in comparison to a traditional approach of image processing.

Notes

Acknowledgement

This research was made possible by DP160103595 grant from Australian Research Council and NPRP 9-224-1-049 grant from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors.

References

  1. 1.
    Rosi, A., Mamei, M., Zambonelli, F., Dobson, S., Stevenson, G., Ye, J.: Social sensors and pervasive services: approaches and perspectives. In: PERCOM 2011 (2011)Google Scholar
  2. 2.
    Chard, K., Caton, S., Rana, O., Bubendorfer, K.: Social cloud: cloud computing in social networks. In: IEEE 3rd ICCC 2010, pp. 99–106 (2010)Google Scholar
  3. 3.
    Aggarwal, C., Abdelzaher, T.: Social sensing. In: Aggarwal, C. (ed.) Managing and Mining Sensor Data, pp. 237–297. Springer, Boston (2013)CrossRefGoogle Scholar
  4. 4.
    Neiat, A.G., Bouguettaya, A., Sellis, T., Ye, Z.: Spatio-temporal composition of sensor cloud services. In: ICWS 2014 (2014)Google Scholar
  5. 5.
    Elers, S.: Online investigation: using the internet for investigative policing practice. Australasian Policing 6(1), 7–9 (2014)Google Scholar
  6. 6.
    Socialsensors.com.sg, Social Sensors - Sensing real-world activities from Social Media. http://socialsensors.com.sg/. Accessed 10 Dec 2015
  7. 7.
    Chae, J., Thom, D., Bosch, H., Jang, Y., Maciejewski, R., Ebert, D.S., Ertl, T.: Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition. In: VAST 2012 (2012)Google Scholar
  8. 8.
    Dong, X., Mavroeidis, D., Calabrese, F., Frossard, P.: Multiscale event detection in social media. In: DMKD 2014 (2014)Google Scholar
  9. 9.
    Ghari Neiat, A., Bouguettaya, A., Sellis, T.: Spatio-temporal composition of crowdsourced services. In: Barros, A., Grigori, D., Narendra, N.C., Dam, H.K. (eds.) ICSOC 2015. LNCS, vol. 9435, pp. 373–382. Springer, Heidelberg (2015). doi: 10.1007/978-3-662-48616-0_26 CrossRefGoogle Scholar
  10. 10.
    Guo, B., et al.: From participatory sensing to mobile crowd sensing. In: PERCOM Workshops. IEEE (2014)Google Scholar
  11. 11.
    Fernndez, J., et al.: An intelligent surveillance platform for large metropolitan areas with dense sensor deployment. Sensors 13(6), 7414–7442 (2013)CrossRefGoogle Scholar
  12. 12.
    Balke, W.-T., Diederich, J.: A quality-and cost-based selection model for multimedia service composition in mobile environments. In: ICWS 2006 (2006)Google Scholar
  13. 13.
    Perera, C., Arkady, Z., Peter, C., Dimitrios, G.: Sensing as a service model for smart cities supported by internet of things. Trans. ETT 25(1), 81–93 (2014)Google Scholar
  14. 14.
    Lowe, D.G.: Distinctive image features from scale-invariant key points. Int. J. Comp. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  15. 15.
    Patwardhan, S., Banerjee, S., Pedersen, T.: Using measures of semantic relatedness for word sense disambiguation. In: Gelbukh, A. (ed.) CICLing 2003. LNCS, vol. 2588, pp. 241–257. Springer, Heidelberg (2003). doi: 10.1007/3-540-36456-0_24 CrossRefGoogle Scholar
  16. 16.
    Mihalcea, R., Corley, C., Strapparava, C.: Corpus-based and knowledge-based measures of text semantic similarity. In: AAAI 2006, vol. 6 (2006)Google Scholar
  17. 17.
    Gretzel, U., Marianna, S., Zheng, X., Chulmo, K.: Smart tourism: foundations and developments. Electron. Mark. 25(3), 179–188 (2015)CrossRefGoogle Scholar
  18. 18.
    Kantarci, B., Hussein, M.: Trustworthy crowdsourcing via mobile social networks. In: GLOBECOM 2014, pp. 2905–2910. IEEE (2014)Google Scholar
  19. 19.
    Slabaugh, G., et al.: A survey of methods for volumetric scene reconstruction from photographs. In: Mueller, K., Kaufman, A.E. (eds.) Volume Graphics 2001. Eurographics. Springer, Vienna (2001)Google Scholar
  20. 20.
    Limna, T., Tandayya, P.: A flexible and scalable component-based system architecture for video surveillance as a service, running on infrastructure as a service. Multimed. Tools Appl. 75(4), 1765–1791 (2016)CrossRefGoogle Scholar
  21. 21.
    Theodoridis, Y., Vazirgiannis, M., Sellis, T.: Spatio-temporal indexing for large multimedia applications. In: Proceedings of the 3rd IEEE International Conference on Multimedia Computing and Systems, pp. 441–448 (1996)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of ScienceRMIT UniversityMelbourneAustralia
  2. 2.School of Information TechnologiesThe University of SydneySydneyAustralia
  3. 3.College of EngineeringQatar UniversityDohaQatar

Personalised recommendations