As interest in social network studies has grown bigger along with the development of the Web, social network trust management and applications have come into the spotlight. The increasing interest in social network services that are open systems has motivated the need for a reliable trust model that enables practical information sharing and information protection. In this paper, we propose an identity management-based social trust model for solving a sparsity problem and an information leakage. The proposed trust model contributes to increasing the opportunities for information sharing. In addition, the creation and use of identity groups with a clustering approach and partial identities in the proposed approach effectively address security and privacy risks in social networks. In experiments, the performance of the proposed approach is evaluated using precision-recall and F-measures.
Online environments offer a major advantage that data can be accessed freely. At the same time however, they present us with an issue of trust: how much any data from online sites can be trusted. Trust and Reputation Systems (TRS), developed to address this issue of trust on network, quantify reliability in terms of semantics and derive a trustnetwork from a targeted online data. The performance of TRS is often hindered despite the promises because the number of links formed in the ideal scenario frequently is not reached, suffering from the problems of cold-start and sparsity. In this paper, we propose a method in which Link Prediction(LP) and Clustering are applied to TRS so that these two problems are adequately addressed. We evaluate our proposed method with a recommendation system we constructed. Our experiment results show that our method positively contributes to the performance of a recommendation system and help control the problems of cold-start and sparsity in TRS.
A wireless sensor network consisting of resources, size, and cost-limited sensors is used in many military and civil applications. This paper proposes an energy-efficient clustering algorithm that extends the lifetime of sensor networks. The proposed clustering algorithm is an extended hierarchical clustering protocol that minimizes the overall amount of consumed energy in the network. The proposed approach dynamically updates clusters and distributes the load on the heavily loaded cluster heads among different nodes. It also balances the remaining energy on nodes in the network, which leads to prolonged network lifetime. The performance is evaluated in terms of network lifetime, average energy consumption, and standard deviation of residual energy.
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