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As a popular innovation, Web Service provides a flexible solution to integrate diverse online applications with existing Internet protocols and open standards. The availability and flexibility of Web Service enable its potential to handle dynamic requests in distributed online collaboration. However, this potential is limited by Web Service's security concerns due to its uncertainty, openness and fraudulence. A solution to this problem, Trust, an important social concept in all human interactions, has been proven to be a promising way to resolve the security issues raised by these distributed collaborations. This paper introduces a novel evaluation model of Web Service by leveraging trust as an approach. We first incorporate a trust management module into the standard Service Oriented Architecture (SOA). Then, after transforming a Web Service network to a small-world network based on the trust relationships of service entities, we propose a trust evaluation model with an amendatory subjective logic. The simulation experiments we ran compared our trust evaluation model with two other popular models. The result shows our proposed model outperforms in terms of both detection capability and stability.

Trust is an important mechanism to promote cooperation among persons in social network. As a security mechanism, it is widely implemented computer networks. In P2P applications, trust among cooperating peers is an essential precondition. It is becoming increasing vital in open, distributed, dynamic and anonymous networks to protect against malicious peers. This paper proposed a trust-aware propagation model based on the social network and with the basis of analyzing aware-computing to attempt to discover the propagation characteristics and regularity of trust being as network stability factor, and to reveal the functional mechanism of trust in the network security, which can improve the stability and robustness of the network. Firstly, we proposed a trust-aware propagation framework that contains three layers: propagation layer, aware layer, and computation layer. And then, the trust-aware propagation algorithm is presented to implement the formation of trust networks as fast as in P2P. A fitness function for peers computes their fitness degree based on contextual information to provide an initial entity identification mechanism, and the process of trust-aware propagation based on Markov process is defined to apperceive of trust among peers to ulteriorly form trust alliance. Thirdly, we built the dynamics model of trust propagation in order to deeply analyze the stability of trust networks. Next, three propagation strategies is proposed for implementing trust propagation. Finally, simulation experiments are carried in the bio-network platform. The results show that the trust-aware propagation model can effectively enhance the security and stability of P2P network, and improve the availability of the peer's resources.

With deep and broad applications in distributed computing, how to promote cooperation between entities becomes more and more important. Trust has been proven to be essential to enforce cooperative behaviors in distributed environments. To build trust relationship depends on some factors, such as context, behaviors, and experiences, and it is more challenging to accurately measure them. In this paper, we present a context-sensitive trust computing model to address this problem. Firstly, a trust space with fit-degree is defined based on the contextual information and a context-sensitive fit-law is proposed to judge the abilities of entities. Then, a trust computing model is proposed and followed by an expatiated dynamics trust analysis. Next, considering a new entity without trustworthiness can almost not do anything, we further present an algorithm of initial trustworthiness based on the new entity's abilities. The entity's trustworthiness is divided into the initial trustworthiness, the direct trustworthiness, and the recommended trustworthiness. Based on the trustworthiness obtained by the trust fusion algorithm, a mechanism of making trust decision is presented to promote cooperation. The simulation results show that our model can enhance the cooperation among entities. The malicious behaviors can be controlled because of the trustworthiness threshold of services. As such, the honest peers can be incentive, the network stability can be promoted, and the extent of network security can be improved. The proposed dynamic trust computing model is proven to be reasonable, practical, comparable, and workable in distributed services environments.

There are a number of dirty data in observation data set derived from ocean observing network. These data should be carefully and reasonably processed before they are used for forecasting or analysis in oceanic warning system (OWS). Due to high-dimensional and dynamic oceanic data, we propose an intelligent integrated data processing model for the OWS. Firstly, we design an integrated framework of the oceanic data processing and present its processing model. The function of each module of this model is analyzed in details. Then, we propose several intelligent data processing methods, such as an intelligent data cleaning method based on the fuzzy c-means algorithm, a data filtering and clustering method based on the greedy clustering algorithm, and a data processing method based on the maximum entropy for the OWS. The efficiency and accuracy of the proposed model is proved by experimental results of observation data of the Red Tide. The proposed model can automatically find the new clustering center with the updated sample data, and outperforms several algorithms in data processing for the OWS.