ConTrust: A Novel Context-Dependent Trust Management Model in Social Internet of Things

The global population is around 7.4 billion people. This population density requires connectivity to improve the standard of living by transmitting and receiving variety of services. As a result, numerous forms of communication among objects are required for our everyday living demands, independent of their nature. Furthermore, to create a good relationship, every object that is regarded as associate of another object should have distinct criteria such as scalability, interoperability, and trustworthiness. Many security threats, however, have an impact on the social interaction between objects in a social internet of things (SIoT) context, including illegal admittance and suspicious behavior owing to a lack of verification architecture. Others include attempting to provide a proper viewpoint of a malicious object to earn the trust of other objects. As a result, there is a requirement for an acceptable method to check the behavior of objects such as capability, commitment, reliability, and previous job satisfaction before proceeding with any type of job assignment. This will aid in distinguishing between malicious and trustworthy objects by anticipating their upcoming behavior, allowing better judgments regarding service assignment to be made. This study proposes a context-dependent trust management technique (ConTrust) for choosing and allocating jobs in a SIoT environment. The feature-property match approach, as well as the combination of capability, commitment, and satisfaction, were utilized to increase the efficiency of trust assessment and the resolution of context-dependent difficulties. The proposed trust model considers job characteristics, object capabilities and honesty, and the impact of malicious conduct. The experimental results show that the proposed ConTrust model is viable and capable of ensuring the reliability and efficacy of SIoT service sharing between objects as compared to the benchmark models considered in this work.