Abstract: The number of users of the parking lots of the Polytechnic of Leiria, a higher education institution has been increasing each year and it’s becoming a ma-jor concern to address the high demand for a free parking spot on the cam-pus. In order to ease this problem, this paper proposes the design of a smart parking system that can help users to easily find a parking spot using an in-tegrated system that includes actual sensors and a mobile application. The system is based on the information about the occupation status of parking lots generated by parking sensors. This information is accessed by the mobile application through a REST webservice and presented to end-users contributing to the decrease of time wasted on the quest of finding an empty spot. The software architecture behind this layer is a set of decoupled modules that compute and share the information generated by sensors. This architectural approach is noteworthy because it maximizes system scalabil-ity and responsiveness to change. It allows the system to expand with the integration of new applications and perform updates on the existing ones, without an overall impact on the operations of the other system modules.

Abstract: The Internet of Things (IoT) is a novel paradigm based on the connectivity among different entities namely "things". The vision of IoT environment based on smart "things" represents an essential strategy based on the progress of effective and efficient solutions related to the 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, the security of the devices and the network become fundamental, challenging issues. Moreover, the proliferation of human IoT connecting in the system required to focus the efforts in the vulnerability of the complex network as well as the defence challenges at the topologic level. This paper addresses these challenges from the perspective of the graph theory. In this work, the authors use their AV11 algorithm to identify the most critical and influential IoT nodes in a Social IoT (SIoT) network in a smart city context using ENEA- Cresco infrastructure.

Abstract: Interactive visualization of spatio-temporal data is a very active area that has experienced remarkable advances in the last decade. This is due to the emergence of fields of research such as big data and advances in hardware that allow better analysis of information. This article describes the methodology fol-lowed and the design of an open source tool, which in addition to interactively visualizing spatio-temporal data that are represented in an ontology, allows the definition of what to visualize and how to do it. The tool allows selecting, filter-ing and visualizing in a graphical way the entities of the ontology with spatio-temporal data, as well as the instances related to them. The graphical elements used to display the information are specified on the same ontology, extending the VISO graphic ontology, used for mapping concepts to graphic objects with RDFS/OWL Visualization Language (RVL). This extension contemplates the data visualization on rich real-time 3D environments, allowing different modes of visualization according to the level of detail of the scene, while also empha-sizing the treatment of spatio-temporal data, very often used in cultural heritage models. This visualization tool involves simple visualization scenarios and high interaction environments that allow complex comparative analysis. It combines traditional solutions, like hypercube or time-animations with innovative data se-lection methods. This work has been developed in the SILKNOW project, which received funding from the European Union’s Horizon 2020 research and innova-tion programme under grant agreement No 769504.

Abstract: The paper is devoted to the development of high-level tools to automate tangible human-machine interfaces creation bringing together IoT technologies and ontology engineering methods. We propose using ontology-driven approach to enable automatic generation of firmware for the devices and middleware for the applications to design from scratch or transform the existing M2M ecosystem with respect to new human needs and, if necessary, to transform M2M systems into human-centric ones. Thanks to our previous research, we developed the firmware and middleware generator on top of SciVi scientific visualization system that was proven to be a handy tool to integrate different data sources, including software solvers and hardware data providers, for monitoring and steering purposes. The high-level graphical user SciVi interface enables to design human-machine communication in terms of data flow and ontological specifications. Thereby the SciVi platform capabilities are sufficient to automatically generate all the necessary components for IoT ecosystem software. We tested our approach tackling the real-world problems of creating hardware device turning human gestures into semantics of spatiotemporal deixis, which relates to the verbal behavior of people having different psychological types. The device firmware generated by means of SciVi tools enables researchers to understand complex matters and helps them analyze the linguistic behavior of users of social networks with different psychological characteristics, and identify patterns inherent in their communication in social networks.

Abstract: Reliable vessel re-identification would enable maritime surveillance systems to analyze the behavior of vessels by drawing their accurate trajectories, when they pass along different camera locations. However, challenging outdoor conditions and varying viewpoint appearances combined with the large size of vessels limit conventional methods to obtain robust re-identification performance. This paper employs CNNs to address these challenges. In this paper, we propose an Identity Oriented Re- identification network (IORnet), which improves the triplet method with a new identity-oriented loss function. The resulting method increases the feature vector similarities between vessel samples belonging to the same vessel identity. Our experimental results reveal that the proposed method achieves 81.5% and 91.2% on mAP and Rank1 scores, respectively. Additionally, we report experimental results with data augmentation and hyper-parameters optimization to facilitate reliable ship re- identification. Finally, we provide our real-world vessel re- identification dataset with various annotated multi-class features to public access.