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.

中文翻译：

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面向机器学习驱动的社交物联网信任评估模型：一种时间感知方法

社交物联网（SIoT）的新兴范例通过将社交网络的各个方面集成在一起，从而将传统的物联网（IoT）概念转变为数十亿个互连智能对象的社交网络。在SIoT中，对象可以自主方式建立社交关系，并根据其社交行为与网络中的其他对象进行交互。需要注意的一个基本问题是以可靠和可信赖的方式建立这些关系，即建立可信赖的关系并在对象之间建立信任。此外，在一段时间内确定和预测对象在SIoT网络中的行为也是必不可少的。因此，在本文中，我们提出了一种有效的基于时间的机器学习驱动的信任评估模型，以解决这一特定问题。设想的模型根据友谊和社区利益来考虑社会关系，并进一步将工作关系和合作性（对象-对象交互）作为信任参数来量化对象的可信赖性。随后，与传统的加权和启发式算法相反，描述了机器学习驱动的聚合方案以合成这些信任参数以确定单个信任分数。实验结果表明，该模型可以有效地隔离网络中的可信任对象和不可信任对象，