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.

中文翻译：

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社交物联网的信任计算启发式：基于机器学习的方法

物联网（IoT）是一个由数十亿个互连物理对象组成的不断发展的网络，例如众多传感器，智能手机，可穿戴设备和嵌入式设备。这些物理对象（通常称为智能对象）在实际环境中部署时会聚集来自其周围环境的有用信息。截至目前，物联网的概念已扩展到包含社交网络方面，从而形成了“社交物联网”（SIoT）的有希望的范例。在SIoT中，设备充当自治代理，并通过在设备之间与其所有者建立社交关系，以智能方式提供信息和服务发现的交换。信任在物理对象之间建立可信赖的关系并降低决策过程中的可能风险方面起着重要作用。本文提出了一种信任计算模型来提取SIoT环境中的各个信任特征。此外，基于机器学习的启发式算法用于汇总所有信任特征，以确定汇总的信任分数。仿真结果表明，所提出的基于信任的模型以有效的方式隔离了网络内的可信任和不可信任节点。