An Implementation on Detection of Trusted service provider in Mobile Ad-hoc Networks
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2014 by IJETT Journal|
|Year of Publication : 2014|
|Authors : Mr.Rahul A Jichkar , Dr.M.B. Chandak
Mr.Rahul A Jichkar , Dr.M.B. Chandak. "An Implementation on Detection of Trusted service provider in Mobile Ad-hoc Networks", International Journal of Engineering Trends and Technology (IJETT), V11(2),64-74 May 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
The mobile ad-hoc network consists of energy constraint devices called nodes communicating through radio signals forming a temporary network i.e. nodes are continuously switching from one network to another. To minimize the power consumption, we form the clusters with an elected cluster head (Service Provider) based on any cluster head selection strategy. As the topology of this network is time variant attributable to node mobility, nodes are continuously leaving and entering the clusters, automatically registering with the cluster head to become the member of the cluster. But, there can be a scenario where a new node wants to access a service provided by the cluster head, at this time the newly entered node is unaware of the trustworthiness of the cluster head. To establish a trusted link amongst newly entered node and CH we have adopted an indirect trust computation technique based on recommendations, which form an important component in trust-based access control models for pervasive environment. It can provide the new node the con?dence to interact with unknown service provider or CH to establish a trusted link for reliable accessibility of the service. In this paper, we shall present some existing indirect trust based techniques and subsequently discuss our proposal along with its merits and future scope.
 A Josang, R Ismail, C Boyd, “A survey of trust and reputation systems for online service provision,” in Decis. Support Syst. 43(2), 618–644 (2007).
 L Xiong, L Liu, “Peertrust: supporting reputation-based trust for peer-to-peer electronic communities,” in IEEE Trans. Knowl. Data Engr. 16(7), 843–857 (2004)
 M Chen, JP Singh, “Computing and using reputations for internet ratings,” in 3rd ACM Conference on Electronic Commerce (ACM, New York, 2001), pp. 154–162
 Z Malik, A Bouguettaya,” Evaluating rater credibility for reputation assessment of web services,” in 8th International Conference on Web Information Systems Engineering (Springer, Heidelberg, 2007), pp. 38–49
 S Ganeriwal, LK Balzano, MB Srivastava, “Reputation-based framework for high integrity sensor networks,” in ACM Trans. Sensor Netw. 4, 1–37 (2008)14.
 R Zhou, K Hwang, “Powertrust: a robust and scalable reputation system for trusted peer-to-peer computing,” in IEEE Trans. Parallel Distributed Syst. 18(4), 460–473 (2007)
 X Liu, A Datta, H Fang, J Zhang, “Detecting imprudence of reliable sellers in online auction sites,” in IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications (IEEE, Los Alamitos, 2012), pp. 246–253
 C Ziegler, J Golbeck,” Investigating correlations of trust and interest similarity–do birds of a feather really ?ock together?,” J. Artif. Intell. Res. (2005). doi:10.1.1.79.7225
 I Varlamis, M Eirinaki, M Louta, “A study on social network metrics and their application in trust networks,” in 2010 International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (IEEE, Los Alamitos, 2010), pp. 168–175
 E Davoodi, M Afsharchi, K Kianmehr,”A social network-based approach to expert recommendation system,” in Hybrid Arti?cial Systems. 7th International Conference on Hybrid Arti?cial Intelligent Systems, Salamanca, March 2012 (Springer, Heidelberg, 2012), pp. 91–102
 H Ma, I King, M Lyu, “Learning to recommend with social trust ensemble,” in 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM, New York, 2006), pp. 203–210
 F Almenarez, A Marin, D Diaz, “A Cortes, C Campo, C Garcia, Managing ad-hoc trust relationships in pervasive computing environments, in Proceedings of the Workshop on Security and Privacy in Pervasive Computing,” SPPC’04, Vienna, 20 April 2004
 C Dellarocas, “Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior,” in 2nd ACM Conference on Electronic Commerce (ACM, New York, 2000), pp.150–157
 S Liu, J Zhang, C Miao, Y Theng, “A Kot, An integrated clustering-based approach to ?ltering unfair multi-nominal testimonies.,” Comput. Intell. (2012). doi:10.1111/j.1467-8640.2012.00464.x
 A Whitby, A Josang, J Indulska, “Filtering out unfair ratings in Bayesian reputation systems,” in 3rd International Joint Conference on Autonomous Agents and Multi Agent Systems (IEEE, Washington, 2005), pp. 106– 117
 J Weng, C Miao, “A Goh, An entropy-based approach to protecting rating systems from unfair testimonies,” IEICE Trans. Inf. Syst. 89(9), 2502–2511 (2006)
 SI Ahamed, M Haque, M Endadul, F Rahman, N Talukder, “Design, analysis, and deployment of omnipresent formal trust model (FTM) with trust bootstrapping for pervasive environments,” J. Syst. Software 83(2), 253–270 (2010)
 MK Deno, T Sun, I Woungang, “Trust management in ubiquitous computing: a Bayesian approach,” Comput. Commun. 34(3), 398–406 (2011)
 Naima Iltaf, Abdul Ghafoor and Uzman Zia, “A mechanism for detecting dishonest recommendation in indirect trust computation,“ in EURASIP Journal on Wireless Communications and Networking 2013
 Z Zhang, X Feng, “New methods for deviation-based outlier detection in large database,” in Sixth International Conference on Fuzzy Systems and Knowledge Discovery (IEEE, Los Alamitos, 2009), pp. 495–499
 A Arning, R Agrawal, P Raghavan, “A linear method for deviation detection in large databases,” in 2nd International Conference on Data Mining and Knowledge Discovery (AAAI, Portland, 1996), pp. 164–169
 BW Matthews, ”Comparison of the predicted and observed secondary structure of T4 phage lysozyme,” Biochim. Biophys. Acta 405, 442–451 (1975)
Cluster Head, Indirect Trust Computation, Pervasive Environment, Trust Value, Recommendations.