Journal of Grid Computing

, Volume 17, Issue 1, pp 79–95 | Cite as

SimSim: A Service Discovery Method Preserving Content Similarity and Spatial Similarity in P2P Mobile Cloud

  • Zhiming Cai
  • Ivan LeeEmail author
  • Shu-Chuan Chu
  • Xuehong Huang


Mobile cloud has become a new computing paradigm such that services are accessible in any place and at any time. Despite its promising prospect, challenges arise due to unreliable channel condition and limited bandwidth in wireless communication, dynamic route establishment due to node mobility, difficulties in associating request to relevant service providers, and complication in service deployment. To ensure the fairness of resource allocation and network load balance, it is necessary to consider strategies for distributing services. In this paper, we propose SimSim, a service discovery scheme based on keywords search which preserves content similarity and spatial similarity. A mapping from a keyword set of services to a bit vector with identical hash is designed to preserve content similarity. The proposed technique applies a hierarchical hash clustering model and investigates the strategies of service deployment and discovery. By mapping the services characterized by keywords to the Gray space, SimSim offers similar services at close geographical proximity. Extensive simulations have been conducted to assess the proposed system.


Service discovery Gray space Content similarity Spatial similarity Hierarchical hash clustering 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



The authors would like to thank the anonymous reviewers for their valuable comments and suggestions. This work is supported by Major Scientific and Technological Project of Fujian, China under Grant No. 2013HZ0001-4, and Experimental Teaching Reform project of Fujian University of Technology under Grant No. SJ2015019.


  1. 1.
    Barjini, H., Othman, M., Ibrahim, H., Udzir, N.I.: Shortcoming, problems and analytical comparison for flooding-based search techniques in unstructured P2P networks. Peer-to-Peer Networking and Applications 5, 1–13 (2012)CrossRefGoogle Scholar
  2. 2.
    Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18, 509–517 (1975)CrossRefzbMATHGoogle Scholar
  3. 3.
    Cai, Z., Chen, C.: Demand-driven task scheduling using 2d chromosome genetic algorithm in mobile cloud. In: International Conference on Progress in Informatics and Computing (PIC). IEEE, pp 539–545 (2014)Google Scholar
  4. 4.
    Cai, Z., Chen, C.: Task scheduling based on degenerated monte carlo estimate in mobile cloud. Int. J. Grid. Distributed. Comput. 7, 179–196 (2014)CrossRefGoogle Scholar
  5. 5.
    Carlini, E., Coppola, M., Laforenza, D., Ricci, L.: Reducing Traffic in DHT-Based Discovery Protocols for Dynamic Resources. Springer, Boston (2010)CrossRefGoogle Scholar
  6. 6.
    Carlini, E., Coppola, M., Ricci, L.: Probabilistic dropping in push and pull dissemination over distributed hash tables. In: IEEE International Conference on Computer and Information Technology, pp 47–52 (2011)Google Scholar
  7. 7.
    Caron, E., Desprez, F., Tedeschi, C.: Enhancing computational grids with peer-to-peer technology for large scale service discovery. J. Grid. Comput. 5(3), 337–360 (2007)CrossRefGoogle Scholar
  8. 8.
    Chen, F., Lu, C.,Wu, H., Li,M.: A semantic similarity measure integrating multiple conceptual relationships for web service discovery. Expert. Syst. Appl. 67, 19–31 (2017)CrossRefGoogle Scholar
  9. 9.
    Choi, S., Chung, K., Yu, H.: Fault tolerance and QoS scheduling using CAN in mobile social cloud computing. Clust. Comput. 17, 911–926 (2014)CrossRefGoogle Scholar
  10. 10.
    Cong, Z., Fernandez, A., Billhardt, H., Lujak, M.: Service discovery acceleration with hierarchical clustering. Inf. Syst. Front. 17, 799–808 (2015)CrossRefGoogle Scholar
  11. 11.
    Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the 20th Annual Symposium on Computational Geometry. ACM, pp 253–262 (2004)Google Scholar
  12. 12.
    Djemaiel, Y., Berrahal, S., Boudriga, N.: A novel graph-based approach for the management of health data on cloud-based wsans. J. Grid. Comput. 16(2), 317–344 (2018)CrossRefGoogle Scholar
  13. 13.
    Faloutsos, C.: Gray codes for partial match and range queries. IEEE Trans. Softw. Eng. 14, 1381–1393 (1988)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Forestiero, A., Leonardi, E., Mastroianni, C., Meo, M.: Self-Chord: A bio-inspired P2P framework for self-organizing distributed systems. IEEE/ACM Trans. Netw. 18(5), 1651–1664 (2010)CrossRefGoogle Scholar
  15. 15.
    He, Y., Lee, I., Guan, L.: Distributed algorithms for network lifetime maximization in wireless visual sensor networks. IEEE Trans. Circuits Syst. Video Technol. 19(5), 704–718 (2009)CrossRefGoogle Scholar
  16. 16.
    He, Y., Lee, I., Guan, L.: Optimized video multicasting over wireless ad hoc networks using distributed algorithm. IEEE Trans. Circuits Syst. Video Technol. 19(6), 796–807 (2009)CrossRefGoogle Scholar
  17. 17.
    Hua, Y., Xiao, B., Liu, X., Feng, D.: The design and implementations of locality-aware approximate queries in hybrid storage systems. IEEE Trans. Parallel Distrib. Syst. 26, 3194–3207 (2015)CrossRefGoogle Scholar
  18. 18.
    Joung, Y., Yang, L., Fang, C.: Keyword search in DHT-based peer-to-peer networks. IEEE J. Sel. Areas Commun. 25, 46–61 (2007)CrossRefGoogle Scholar
  19. 19.
    Khodaei, A., Shahabi, C., Li, C.: SKIf-P: a point-based indexing and ranking of web documents for spatial-keyword search. GeoInformatica 16, 563–596 (2012)CrossRefGoogle Scholar
  20. 20.
    Lee, I., Shaw,W., Park, J.H.: On prolonging the lifetime for wireless video sensor networks. Mobile Netw. Appl. 15(4), 575–588 (2010)CrossRefGoogle Scholar
  21. 21.
    Li, R., Song, W., Shen, H., Xiao, W., Lu, Z.: A flabellate overlay network for multi-attribute search. J. Parallel. Distrib. Comput. 71, 407–423 (2011)CrossRefGoogle Scholar
  22. 22.
    Liao, J., Yang, D., Li, T., Qi, Q., Wang, J., Sun, H.: Fusion feature for LSH-based image retrieval in a cloud datacenter. Multimed. Tools Appl. 75(15), 405–15,427 (2016)Google Scholar
  23. 23.
    Lin, H.F., Chen, C.H.: Design and application of augmented reality query-answering system in mobile phone information navigation. Expert Syst. Appl. 42, 810–820 (2015)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Onan, A., Korukolu, S., Bulut, H.: Ensemble of keyword extraction methods and classifiers in text classification. Expert Syst. Appl. 57, 232–247 (2016)CrossRefGoogle Scholar
  25. 25.
    Pan, J.S., Kong, L., Sung, T.W., Pei-Wei, T., Snasel, W.: α-fraction first strategy for hirarchical wireless sensor networks. J. Internet Technol. 19(6), 1717–1726 (2018)Google Scholar
  26. 26.
    Qi, S., Wu, D., Mamoulis, N.: Location aware keyword query suggestion based on document proximity. IEEE Trans. Knowl. Data Eng. 28, 82–97 (2016)CrossRefGoogle Scholar
  27. 27.
    Ratnasamy, S., Francis, P., Handley, M., Karp, R., Shenker, S.: A scalable content-addressable network. SIGCOMM. Comput. Commun. Rev. 31, 161–172 (2001)CrossRefzbMATHGoogle Scholar
  28. 28.
    Rowstron, A., Druschel, P.: Pastry: Scalable, distributed object location and routing for large-scale peer-to-peer systems. In: IFIP/ACM International Conference on Distributed Systems Platforms (Middleware), pp 1–22 (2001)Google Scholar
  29. 29.
    Salgado, C., Cheema, M.A., Ali, M.E.: Continuous monitoring of range spatial keyword query over moving objects. World Wide Web (2017)Google Scholar
  30. 30.
    Schmidt, C., Parashar, M.: A peer-to-peer approach to web service discovery. World Wide Web: Internet and Web Information Systems 7, 211–229 (2004)CrossRefGoogle Scholar
  31. 31.
    Schmidt, C., Parashar, M.: Squid: Enabling search in DHT-based systems. J. Parallel Distrib. Comput. 68, 962–975 (2008)CrossRefzbMATHGoogle Scholar
  32. 32.
    Selimi, M, Cerdà-Alabern, L, Freitag, F, Veiga, L, Sathiaseelan, A, Crowcroft, J: A lightweight service placement approach for community network micro-clouds. Journal of Grid Computing (2018)Google Scholar
  33. 33.
    Shou, L, Zhang, X, Wang, P, Chen, G, Dong, J: Supporting multi-dimensional queries in mobile P2P network. Inform. Sci. 181, 2841–2857 (2011)CrossRefGoogle Scholar
  34. 34.
    Silva, T, Kamienski, C, Fernandes, S, Sadok, D: A flexible DHT-based directory service for information management. Peer-to-Peer Networking and Applications 8, 512–531 (2015)CrossRefGoogle Scholar
  35. 35.
    Stoica, I, Morris, R, Karger, D, et al.: Chord: A scalable peer-to-peer lookup service for internet applications. In: Proceedings of ACM SIGCOMM, pp 1–12 (2001)Google Scholar
  36. 36.
    Sun, G, Shen, J: Towards organizing smart collaboration and enhancing teamwork performance: a GA-supported system oriented to mobile learning through cloud-based online course. Int. J. Mach. Learn. Cybern. 7, 391–409 (2016)CrossRefGoogle Scholar
  37. 37.
    Verbelen, T, Simoens, P, De Turck, F, Dhoedt, B: Adaptive deployment and configuration for mobile augmented reality in the cloudlet. J. Netw. Comput. Appl. 41, 206–216 (2014)CrossRefGoogle Scholar
  38. 38.
    Wang, J, Yu, X, Zhao, M: Privacy-preserving ranked multi-keyword fuzzy search on cloud encrypted data supporting range query. Arab. J. Sci. Eng. 40, 2375–2388 (2015)CrossRefGoogle Scholar
  39. 39.
    Withanage, KI, Lee, I, Brinkworth, R, Mackintosh, S, Thewlis, D: Fall recovery subactivity recognition with rgb-d cameras. IEEE Trans. Ind. Inf. 12(6), 2312–2320 (2016)CrossRefGoogle Scholar
  40. 40.
    Wu, J, Chen, L, Zheng, Z, Lyu, MR, Wu, Z: Clustering Web services to facilitate service discovery. Knowl. Inf. Syst. 38, 207–229 (2014)CrossRefGoogle Scholar
  41. 41.
    Zhang, Y., Huang, H., He, H., Teng, J., Wang, Z.: Efficient distributed semantic based data and service unified discovery with one-dimensional semantic space. J. Netw. Comput. Appl. 49, 78–87 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of Information Science and EngineeringFujian University of TechnologyFuzhouChina
  2. 2.School of Information Technology and Mathematical SciencesUniversity of South AustraliaAdelaideAustralia
  3. 3.School of Computer Science, Engineering and MathematicsFlinders University of South AustraliaAdelaideAustralia
  4. 4.National Demonstration Center for Experimental Electronic Information and Electrical Technology EducationFujian University of TechnologyFuzhouChina

Personalised recommendations