Abstract
There are lots of misbehaving nodes in opportunistic networks which can cause severe performance downgrade. Those misbehaving nodes contains malicious nodes and selfish nodes. Selfish nodes don’t cooperate in routing and forwarding. Malicious nodes drop data packets or forward lots of garbage packets hindering the normal process of data forwarding. In order to improve network performance, a credibility evaluation method is proposed in this paper, named FICT. According to the FICT, familiar degree, intimate degree and contribution degree are defined to describe the social attributes of nodes. We use the number of contacts, connect time and PLR to calculate the value of the credibility of nodes. Just when the value of credibility is greater than or equal to the threshold, the node is selected to forward data packets. We performed simulation experiments with FICT method on the ONE. The simulation results show that by using the FICT method, the success rate of message delivery increases and the average latency of message delivery reduces. Especially when the number of misbehaving nodes becomes large, the FICT method can improve the performance of the networks significantly.
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References
Xiong, Y.P., Sun, L.M., et al.: Opportunistic networks. J. Softw. 20(1), 124–137 (2009)
Huang, C.M., Lan, K.C., et al.: A survey of opportunistic networks. In: International Conference on Advanced Information Networking and Applications, pp. 1672–1677. IEEE (2008)
Spyropoulos, T., et al.: Efficient routing in intermittently connected mobile networks. IEEE/ACM Trans. Netw. 16(1), 77–90 (2008)
Li, Q., et al.: A routing protocol for socially selfish delay tolerant networks. Ad Hoc Netw. 10(1), 1619–1632 (2012)
Wang, B., et al.: Trust-based minimum cost opportunistic routing for Ad hoc networks. J. Syst. Softw. 84, 2107–2122 (2011)
Hu, Y., Perrig, A.: A survey of secure wireless Ad hoc routing. IEEE Secur. Priv. 2(3), 28–39 (2004)
Wu, Y., Li, J.H., et al.: Survey of security and trust in opportunistic networks. J. Comput. Res. Dev. 50(2), 278–290 (2013)
Conti, M., Kumar, M.: Opportunities in opportunistic computing. Computer 43(1), 42–50 (2010)
Trifunovic, S., Legendre, F., et al.: Social trust in opportunistic networks. In: INFOCOM IEEE Conference on Computer Communications Workshops, pp. 1–6. IEEE (2010)
Mtibaa, A., Harras, K.A.: Social-based trust in mobile opportunistic networks. In: Proceedings of IEEE Simna (2011)
Becker, C., Schlinga, S., et al.: Trustful data forwarding in social opportunistic networks. In: 2013 IEEE 10th International Conference on Ubiquitous Intelligence and Computing, and 10th International Conference on Autonomic and Trusted Computing (UIC/ATC), pp. 430–437. IEEE (2013)
Bigwood, G., Henderson, T.: IRONMAN: using social networks to add incentives and reputation to opportunistic networks. In: 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust (PASSAT), and 2011 IEEE Third International Conference on Social Computing (Social Com), pp. 65–72. IEEE (2011)
Gupta, S., Dhurandher, S.K., et al.: Trust-based security protocol against black hole attacks in opportunistic networks. In: 2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 724–729. IEEE, Lyon (2013)
Premalatha, S., Mary Anita Rajam, V.: Reputation management for data forwarding in opportunistic networks. In: 2014 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–7. IEEE, Coimbatore (2014)
Grabner-Kräuter, S., Bitter, S.: Trust in online social networks: a multifaceted perspective. Forum Soc. Econ. 44, 48–68 (2013). doi:10.1080/07360932.2013.781517
Gu, B., Sheng, V.S.: A robust regularization path algorithm for ν-support vector classification. IEEE Trans. Neural Netw. Learn. Syst. 28, 1241–1248 (2017). doi:10.1109/TNNLS.2016.2527796
Xia, Z., Wang, X., Zhang, L., Qin, Z., Sun, X., Ren, K.: A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE Trans. Inf. Forensics Secur. 11, 2594–2608 (2016). doi:10.1109/TIFS.2016.2590944
Fu, Z., Ren, K., Shu, J., Sun, X., Huang, F.: Enabling personalized search over encrypted outsourced data with efficiency improvement. IEEE Trans. Parallel Distrib. Syst. 27, 2546–2559 (2016). doi:10.1109/TPDS.2015.2506573
Gu, B., Sheng, V.S., Tay, K.Y., Romano, W., Li, S.: Incremental support vector learning for ordinal regression. IEEE Trans. Neural Netw. Learn. Syst. 26, 1403–1416 (2015). doi:10.1109/TNNLS.2014.2342533
Chen, Y., Hao, C., Wu, W., Wu, E.: Robust dense reconstruction by range merging based on confidence estimation. Sci. China Inf. Sci. 59 (2016). doi:10.1007/s11432-015-0957-4
Kong, Y., Zhang, M., Ye, D.: A belief propagation-based method for task allocation in open and dynamic cloud environments. Knowl. Based Syst. 115, 123–132 (2017). doi:10.1016/j.knosys.2016.10.016
Acknowledgements
This work was partly supported by the NSFC-Guangdong Joint Found (U1501254) and National key research and development program (2016YFB0800302) and the Co-construction Program with the Beijing Municipal Commission of Education and the Ministry of Science and Technology of China (2012BAH45B01) and the Director’s Project Fund of Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education (Grant No. 2017ZR01) and the Fundamental Research Funds for the Central Universities (BUPT2011RCZJ16, 2014ZD03-03) and China Information Security Special Fund (NDRC).
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Dou, J., Yao, W., Wang, D. (2017). A Credibility Evaluation Method in Opportunistic Networks. In: Sun, X., Chao, HC., You, X., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2017. Lecture Notes in Computer Science(), vol 10602. Springer, Cham. https://doi.org/10.1007/978-3-319-68505-2_33
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DOI: https://doi.org/10.1007/978-3-319-68505-2_33
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