TOWARDS A MACHINE LEARNING-DRIVEN TRUST EVALUATION MODEL FOR SOCIAL INTERNET OF THINGS: A TIME-AWARE APPROACH

Abstract

Social Internet of Things is a trend in the technology which allows to the add objects to the network through which communication is possible using unique object relationship and ability to transfer the data in a network. Internet of Things is able to achieve more efficiency in decision making, Social internet of things is a subset of Internet of Things that establishes the relationship with other objects for effective communication and can improve the scalability, trust, resource management using social trust computing. Many existing models are not dynamic in nature in proving the trust with objects and user interaction and decision making process is not identifiable, the proposed Resilient Based Social Internet of Things model increases performance of evaluation with various attributes like information gain, resilience of the system, cooperativeness and trustworthiness. In SIoT trustworthiness is very important in defining reliability in user communications and interactions. The proposed experiments shows the significant improvement in the trust model for the AppClassNet data set and social internet of things data set in order to segregate trust and untrusts effectively in the network model with 92% information gain and high resilience by comparing with existing model.