  The Internet of Things (IoT) is an evolving network of billions of
interconnected physical objects, such as numerous sensors, smartphones,
wearables, and embedded devices. These physical objects, generally referred to
as the smart objects, when deployed in the real-world aggregates useful
information from their surrounding environment. As-of-late, this notion of IoT
has been extended to incorporate the social networking facets which have led to
the promising paradigm of the `Social Internet of Things' (SIoT). In SIoT, the
devices operate as an autonomous agent and provide an exchange of information
and service discovery in an intelligent manner by establishing social
relationships among them with respect to their owners. Trust plays an important
role in establishing trustworthy relationships among the physical objects and
reduces probable risks in the decision-making process. In this paper, a trust
computational model is proposed to extract individual trust features in a SIoT
environment. Furthermore, a machine learning-based heuristic is used to
aggregate all the trust features in order to ascertain an aggregate trust
score. Simulation results illustrate that the proposed trust-based model
isolates the trustworthy and untrustworthy nodes within the network in an
efficient manner.

Cryptography and Security;Performance