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The Internet of Things (IoT) is a novel paradigm based on the connectivity among different entities or “things”. IoT environment in the form of interconnected smart “things” represents a great potential in terms of effective and efficient solutions related to urban context (e.g., system architecture, design and development, human involvement, data management and applications). On the other hand, with the introduction of the IoT environment, devices and network security have become a fundamental and challenging issue. Moreover, growing number of users connected via IoT system necessitates focusing on the vulnerability of complex networks and defence challenges at the topological level. This paper addresses these challenges from the perspective of graph theory. In this work, the authors introduce a novel AV11 algorithm to identify the most critical and influential IoT nodes in a Social IoT (SIoT) network in a smart city context using ENEA Portici CRESCO infrastructure.

The dynamic identification by ambient vibration data is widely used to supply information on the global health of structures through the investigation of changes in their modal parameters. It can be used even for verification of the state of damage of structures after hazardous threats, for example seismic activity. Therefore, it can play a crucial role to integrate and support conservation strategies for historic architectural assets. Sometimes, in historic constructions only a limited number of positions are accessible or usable to install sensors, and so modal analysis must be based on data from few measurement points. Moreover, they might not be the optimal positions for the studied structure, so the obtained results would need further verification. In such circumstances, the mutual validation between different modal analysis techniques can be useful to assess the reliability of results. In the present paper a case study of application to the so-called Temple of Minerva Medica, Rome, is described. Ambient vibration data were acquired in four rowing acquisition sessions carried out from July 2016 to July 2017, which is a timespan usable to assess the impact of the recent Central Italy seismic sequence. For problems related to the installation of the scaffolding only few points were available for instruments positioning. A variety of techniques were applied, including FRF, FDD, EFDD, SSI, HVSR and complex modal models. The variance of the modal parameters obtained by each different technique was utilized to provide indications on the reliability of the average values. © Published under licence by IOP Publishing Ltd.

The epidemic spreading of a disease can be described by a contact network whose nodes are persons or centers of contagion and links heterogeneous relations among them. We provide a procedure to identify multiple sources of an outbreak or their closer neighbors. Our methodology is based on a simple spectral technique requiring only the knowledge of the undirected contact graph. The algorithm is tested on a variety of graphs collected from outbreaks including fluency, H5N1, Tbc, in urban and rural areas. Results show that the spectral technique is able to identify the source nodes if the graph approximates a tree sufficiently., Comment: 5 tables, 5 figures