  The emerging paradigm of the Social Internet of Things (SIoT) has transformed
the traditional notion of the Internet of Things (IoT) into a social network of
billions of interconnected smart objects by integrating social networking
facets into the same. In SIoT, objects can establish social relationships in an
autonomous manner and interact with the other objects in the network based on
their social behaviour. A fundamental problem that needs attention is
establishing of these relationships in a reliable and trusted way, i.e.,
establishing trustworthy relationships and building trust amongst objects. In
addition, it is also indispensable to ascertain and predict an object's
behaviour in the SIoT network over a period of time. Accordingly, in this
paper, we have proposed an efficient time-aware machine learning-driven trust
evaluation model to address this particular issue. The envisaged model
deliberates social relationships in terms of friendship and community-interest,
and further takes into consideration the working relationships and
cooperativeness (object-object interactions) as trust parameters to quantify
the trustworthiness of an object. Subsequently, in contrast to the traditional
weighted sum heuristics, a machine learning-driven aggregation scheme is
delineated to synthesize these trust parameters to ascertain a single trust
score. The experimental results demonstrate that the proposed model can
efficiently segregates the trustworthy and untrustworthy objects within a
network, and further provides the insight on how the trust of an object varies
with time along with depicting the effect of each trust parameter on a trust
score.
