The investigation of anomalies is an important element in many scientific research fields. In recent years, this activity has been also extended to social networking and social internetworking, where different networks interact with each other. In these research fields, we have recently witnessed an important evolution because, beside networks of people, networks of things are becoming increasingly common. IoT and Multiple IoT scenarios are thus more and more studied. This paper represents a first attempt to investigate anomalies in a Multiple IoT scenario (MIoT). First, we propose a new methodological framework that can make future investigations in this research field easier, coherent, and uniform. Then, in the context of anomaly detection in an MIoT, we define the so-called “forward problem” and “inverse problem”. The definition of these problems allows the investigation of how anomalies depend on inter-node distances, the size of IoT networks, and the degree centrality and closeness centrality of anomalous nodes. The approach proposed herein is applied to a smart city scenario, which is a typical MIoT. Here, data coming from sensors and social networks can boost smart lighting in order to provide citizens with a smart and safe environment.

异常的调查是许多科学研究领域中的重要元素。近年来，该活动还扩展到了社交网络和社交网络互连，其中不同的网络彼此交互。在这些研究领域中，我们最近见证了一个重要的发展，因为除了人际网络之外，物联网也越来越普遍。因此，对物联网和多种物联网场景的研究越来越多。本文代表了首次尝试调查多物联网场景（MIoT）中的异常情况。首先，我们提出了一个新的方法框架，可以使该研究领域的未来研究更加轻松，连贯和统一。然后，在MIoT中的异常检测的情况下，我们定义了所谓的“正向问题”和“逆向问题”。这些问题的定义允许研究异常如何取决于节点间距离，IoT网络的大小以及异常节点的度中心性和紧密度中心性。本文提出的方法适用于典型的MIoT智能城市场景。在这里，来自传感器和社交网络的数据可以增强智能照明，从而为市民提供一个智能，安全的环境。