Social-Sensor Composition for Scene Analysis

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11236)


We consider the scene analysis as a service composition problem. A social-sensor cloud services composition model is proposed for the scene analysis. Our proposed model selects and composes social-sensor cloud services based on the user queries. Textual features of the social-sensor cloud services, i.e., description, comments, and meta-data of the social media images are used to reconstruct a scene. Our key contribution is an efficient and real-time composition of related images for scene analysis relying on meta-data and related posted information. Analytical results demonstrate the performance of the proposed model.



This research was partly made possible by NPRP 9-224-1-049 grant from the Qatar National Research Fund (a member of The Qatar Foundation) and DP1601 00149 and LE180100158 grants from Australian Research Council. The statements made herein are solely the responsibility of the authors.


  1. 1.
    Rosi, A., Mamei, M., Zambonelli, F., et al.: Social sensors and pervasive services: approaches and perspectives. In: Proceedings of PERCOM (2011)Google Scholar
  2. 2.
    Aggarwal, C.C., Abdelzaher, T.: Social sensing. In: Aggarwal, C.C. (ed.) Managing and Mining Sensor data. Springer, Boston (2013). Scholar
  3. 3.
    Aamir, T., Dong, H., Bouguettaya, A.: Trust in social-sensor cloud service. In: Proceedings of IEEE ICWS (2018)Google Scholar
  4. 4.
    Aamir, T., Bouguettaya, A., Dong, H., et al.: Social-sensor cloud service selection. In: Proceedings of IEEE ICWS (2017)Google Scholar
  5. 5.
    Aamir, T., Bouguettaya, A., Dong, H., Mistry, S., Erradi, A.: Social-sensor cloud service for scene reconstruction. In: Maximilien, M., Vallecillo, A., Wang, J., Oriol, M. (eds.) ICSOC 2017. LNCS, vol. 10601, pp. 37–52. Springer, Cham (2017). Scholar
  6. 6.
    Neiat, A.G., Bouguettaya, A., Sellis, T., Ye, Z.: Spatio-temporal composition of sensor cloud services. In: ICWS (2014)Google Scholar
  7. 7.
    Bouguettaya, A., Singh, M., et al.: A service computing manifesto: the next 10 years. In: CACM (2017)Google Scholar
  8. 8.
    Wang, H., Shi, Y., et al.: Web service classification using support vector machine. In: Proceedings of IEEE ICTAI (2010)Google Scholar
  9. 9.
    Aamir, T., Dong, H., Bouguettaya, A.: Stance and credibility based trust in social-sensor cloud service. In: Proceedings of WISE (2018)Google Scholar
  10. 10.
    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). Scholar
  11. 11.
    Lowe, D.G.: Distinctive image features from scale-invariant key points. IJCV 60, 91–110 (2004)CrossRefGoogle Scholar
  12. 12.
    Li, L., Liu, D., Bouguettaya, A.: Semantic based aspect-oriented programming for context-aware web service composition. Inf. Syst. 36(3), 551–564 (2011)CrossRefGoogle Scholar
  13. 13.
    Bouguettaya, A., Nepal, S., et al.: End-to-end service support for mashups. In: IEEE TSC (2010)Google Scholar
  14. 14.
    Mihalcea, R., et al.: Corpus-based and knowledge-based measures of text semantic similarity. In: Proceedings of AAAI (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of ScienceRMIT UniversityMelbourneAustralia
  2. 2.School of Information TechnologiesThe University of SydneySydneyAustralia

Personalised recommendations