Towards a Machine Learning-driven Trust Evaluation Model for Social Internet of Things: A Time-aware Approach

TL;DR

This paper introduces a time-aware machine learning model for trust evaluation in the Social Internet of Things, enabling reliable relationship establishment and behavior prediction among interconnected objects.

Contribution

It proposes a novel machine learning-based trust assessment framework that considers social relationships and temporal dynamics in SIoT networks.

Findings

Effectively distinguishes trustworthy and untrustworthy objects

Provides insights into trust variation over time

Shows impact of different trust parameters on scores

Abstract

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.