The Journal of Supercomputing

, Volume 75, Issue 4, pp 1766–1782 | Cite as

Optimization modeling and analysis of trustworthiness determination strategies for service discovery of MSNP

  • Xixi Ma
  • Qun JinEmail author
  • Julong Pan
  • Yufeng Wang


In trustworthy service discovery for Mobile Social Networking in Proximity (MSNP), conventional trust computation faces a big challenging issue—relatively high latency. To cope with it, trustworthiness determination strategies were proposed in our previous study, aiming at avoiding trust computation under certain conditions, so as to reduce the latency. These strategies are conceived based on the assumption that data of a user’s past experience and current profile could be used, and they are incorporated with a set of thresholds from the analysis result of these data. The settings of these thresholds directly affect service quality and user satisfaction on the MSNP service, which in turn becomes an optimization problem. In this paper, we focus on formulating this optimization problem and demonstrating the effectiveness of our proposed solution by designing a simulation experiment. In detail, we establish mathematical models and adjust parameters. We conduct simulations on MATLAB and analyze the results obtained under several different settings. We further compare our work with related works. The results show that our proposed solution is practically feasible and effective in reducing latency under certain conditions.


Mobile Social Networking in Proximity (MSNP) Trust computation Trustworthiness determination strategies Optimization modeling Simulation 


  1. 1.
    Wang Y, Tang J, Jin Q, Ma J (2013) Overview mobile social networking in proximity (MSNP): applications, characteristics and challenges. Proceedings of the 2013 IEEE International Conference on High Performance Computing and Communications, and 2013 IEEE International Conference on Embedded and Ubiquitous Computing, pp 2112–2119Google Scholar
  2. 2.
    Wang Y, Vasilakos AV, Jin Q, Ma J (2014) Survey on mobile social networking in proximity (MSNP): approaches, challenges and architecture. Wirel Netw 20(6):1295–1311CrossRefGoogle Scholar
  3. 3.
    Chang C, Srirama SN, Ling S (2015) Mobile social network in proximity: taxonomy, approaches and open challenges. Int J Pervasive Comput Commun 11(1):77–101CrossRefGoogle Scholar
  4. 4.
    Wang Y, Wei L, Vasilakos AV, Jin Q (2017) Device-to-device based mobile social networking in proximity (MSNP) on smartphones: framework, challenges and prototype. Fut Gen Comput Syst 74(2017):241–253CrossRefGoogle Scholar
  5. 5.
    Wang Y, Jin Q, Vasilakos AV (2016) Special issue on mobile social networking and computing in proximity (MSNP). J Comput Syst Sci 82(1):91–92MathSciNetCrossRefGoogle Scholar
  6. 6.
    Sarpong S, Xu C (2014) A secure and efficient privacy-preserving attribute matchmaking protocol in proximity-based mobile social networks. In: Advanced Data Mining and Applications. Lecture Notes in Computer Science. Springer, New York, vol 8933, pp 305–318Google Scholar
  7. 7.
    Chang C (2013) Service-oriented mobile social network in proximity, PhD Thesis, Monash University, AustraliaGoogle Scholar
  8. 8.
    Zhang R, Zhang J, Zhang Y, Sun J, Yan G (2013) Privacy-preserving profile matching for proximity-based mobile social networking. IEEE J Sel Areas Commun 31(9):656–668CrossRefGoogle Scholar
  9. 9.
    Chang C, Ling S, Srirama S (2014) Trustworthy service discovery for mobile social network in proximity. In: Proceedings of the 2014 IEEE International Conference on Pervasive Computing and Communications Workshops. pp 478–483Google Scholar
  10. 10.
    Chang C, Srirama SN, Ling S (2014) Towards an adaptive mediation framework for Mobile Social Network in Proximity. Pervasive Mob Comput 12(2014):179–196CrossRefGoogle Scholar
  11. 11.
    Ma X, Jin Q, Pan J, Wang Y (2015) Service Discovery Based on Trustworthiness in MSNP: major issues, potential solutions, and feasible strategies. In: Proceedings of the 2015 IEEE International Conference on Smart City/SocialCom/SustainCom, pp 315–320Google Scholar
  12. 12.
    Li J, Zhang Z, Zhang W (2010) MobiTrust: trust management system in mobile social computing. In: Proceedings of the 10th IEEE International Conference on Computer and Information Technology, pp 954–959Google Scholar
  13. 13.
    Rathnayake U, Sivaraman V, Boreli R (2011) Environmental context aware trust in mobile P2P networks. In: Proceedings of the 2011 IEEE 36th Conference on Local Computer Networks, pp 324–332Google Scholar
  14. 14.
    Niu W, Lei J, Tong E et al (2014) Context-aware service ranking in wireless sensor networks. J Netw Syst Manag 22(1):50–74CrossRefGoogle Scholar
  15. 15.
    Zhang B, Huang ZH, Yu J, Xiang Y (2014) Trust computation for multiple routes recommendation in social network sites. Secur Commun Netw 7(12):2258–2276CrossRefGoogle Scholar
  16. 16.
    Wu X, He J, Xu F (2009) A group-based reputation mechanism for mobile P2P networks. In: Advances in Grid and Pervasive Computing. Lecture Notes in Computer Science, Springer, New York, vol 5529, pp 410–421Google Scholar
  17. 17.
    Qureshi B, Min G, Kouvatsos D (2012) A distributed reputation and trust management scheme for mobile peer-to-peer networks. Comput Commun 35(5):608–618CrossRefGoogle Scholar
  18. 18.
    Waluyo AB, Taniar D, Rahayu W, Aikebaier A, Takizawa M, Srinivasan B (2012) Trustworthy-based efficient data broadcast model for P2P interaction in resource-constrained wireless environments. J Comput Syst Sci 78(6):1716–1736MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Qian F, Zhao S, Tang J, Zhang Y (2016) SoRS: social recommendation using global rating reputation and local rating similarity. Physica A 461(2016):61–72CrossRefGoogle Scholar
  20. 20.
    Chen S, Wang G, Jia W (2016) Towards an adaptive mediation framework for Mobile Social Network in Proximity. Fut Gen Comput Syst 55(2016):391–400CrossRefGoogle Scholar
  21. 21.
    Kalaï A, Zayani CA, Amous I, Abdelghani W, Sèdes F (2018) Social collaborative service recommendation approach based on user’s trust and domain-specific expertise. Fut Gen Comput Syst 80(2018):355–367CrossRefGoogle Scholar
  22. 22.
    Zhang Z, Wen J, Wang X, Zhao C (2017) A novel crowd evaluation method for security and trustworthiness of online social networks platforms based on signaling theory. J Comput Sci. Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.China Jiliang UniversityHangzhouChina
  2. 2.Waseda UniversityTokorozawaJapan
  3. 3.Nanjing University of Posts and TelecommunicationsNanjingChina

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