Trust-based Friend Selection Algorithm for navigability in social Internet of Things

Abstract The Internet of Things (IoT) is a growing area of millions of heterogeneous objects denoted by intensive interactions. The social Internet of Things (SIoT) is a promising approach for expediting object interaction issues by integrating the IoT’s social networking notion. The object discovery and service search are promoted with the right friend selection and trust management on that ground. The number of friends and complexity of devoting relation types affect the navigability in a network. On the other hand, constraints in the storage area and battery life of SIoT devices are two crucial challenges that highlighted the importance of mechanisms to increase the lifetime and robustness of devices. Therefore, this scenario addressed an intelligent friend selection strategy by considering objects’ characteristics, typology, and associated functionalities. This issue is leveraged by trust value to eliminate interception of untrustworthy aspects. We designed a generic reference model and utilized optimization decision theory for optimal friend choice to reduce resource consumption to this effect. The simulation results illuminated that a rational selection of friends per service exploration fortifies global navigability in measuring average path length, degree distribution, and the number of links. The efficiency of our solution was revealed by lessening the average distance and promoting the number of links to achieve a well-tied network and benefiting scale-free degree distribution, which hinted existence of hubs or high degree nodes.