  The emerging yet promising paradigm of the Social Internet of Things (SIoT)
integrates the notion of the Internet of Things with human social networks. In
SIoT, objects, i.e., things, have the capability to socialize with the other
objects in the SIoT network and can establish their social network autonomously
by modeling human behaviour. The notion of trust is imperative in realizing
these characteristics of socialization in order to assess the reliability of
autonomous collaboration. The perception of trust is evolving in the era of
SIoT as an extension to traditional security triads in an attempt to offer
secure and reliable services, and is considered as an imperative aspect of any
SIoT system for minimizing the probable risk of autonomous decision-making.
This research investigates the idea of trust quantification by employing trust
measurement in terms of direct trust, indirect trust as a recommendation, and
the degree of SIoT relationships in terms of social similarities
(community-of-interest, friendship, and co-work relationships). A weighted sum
approach is subsequently employed to synthesize all the trust features in order
to ascertain a single trust score. The experimental evaluation demonstrates the
effectiveness of the proposed model in segregating trustworthy and
untrustworthy objects and via identifying the dynamic behaviour (i.e.,
trust-related attacks) of the SIoT objects.
