Modelling autonomic and dynamic trust decision-making mechanism for large-scale open environments. (3rd September 2009)

• Record Type: Journal Article
• Authors: Li, Xiaoyong; Gui, Xiaolin
• Is Part Of: International journal of computer applications technology. Volume 36:Number 3/4(2009)
• Page Start: 297
• Page End: 307
• Publication Date: 2009-09-03
• Subjects: large-scale open environments -- trust decision making -- TDM -- dynamic trust modelling -- automatic weight allocation -- grid computing -- ubiquitous computing -- P2P computing -- peer-to-peer networks -- ad hoc networks -- trust management -- network economics -- cognitive psychology; Technology -- Data processing -- Periodicals; 620.00285
• Languages: English
• ISSNs: 0952-8091

Abstract

With the widespread applications of large-scale open environments, such as grid computing, ubiquitous computing, P2P computing, ad hoc networks, etc., the technology of dynamic trust management has become a significant requirement from a network economics' point of view and trust evaluating mechanism has become a determining factor for any given service's success. But the dynamic nature of trust creates the biggest challenge in measuring trust value and predicting trust-ship. In this paper, firstly, the concepts, problems and research approaches of dynamic trust relationship are summarised and presented. Then, focusing on challenges in current studies, combining human cognitive psychology, an autonomic and dynamic trust decision-making mechanism is proposed, in which direct trust computing method based on attenuation function and feedback trust converging mechanism based on direct trust tree (DTT) are set up. Meanwhile, the proposed model uses two new operators to determine the classification weights of trust decision factors automatically, which makes proposed mechanism exhibit a more robust adaptability. Simulation's results show that compared to the existing trust decision-making mechanisms, the new mechanism has remarkable enhancements in the trust decision's accuracy and has a better robustness in the dynamic adaptation capability.