Digital Twins (DTs) in Metaverse face many challenges such as the lack of optimized AI models to allow the interaction between the user and the virtual environment. In this paper, we propose an optimized model for human language processing based on Convolutional Neural Networks (CCNs) and we present an input processing strategy to meet the real-time requirements of smart applications that integrate DTs oriented to speech-based functionalities for user interaction and Metaverse. In our solution, CNNs are applied for the processing and classification of the human voice, while structured data and MFCC coefficients are used to train the neural networks and generate interference in the models. Similarly, the MFCC algorithm is provided to extract the unique characteristics that specify each generated audio file and to reduce the complexity of the neural network model in order to obtain better performance. Starting from an approach to the problem available in the literature, we have optimized a specific CNN model for Natural Language Processing (NLP) in order to increase effective results. The proposed model has demonstrated excellent performance and can be used as a basis for the implementation of software that allows the interaction of DTs with voice commands issued by a user.

Edge systems are increasingly popular for data collection and processing. Typically, due to their limited storage capacity, pieces of data are continuously exchanged with Cloud systems which store them in distributed DataBase Management System (DBMS). This scenario, known as Cloud/Edge Continuum, is critical from a data security point of view as it is exposed to many risks. Transparent Data Encryption (TDE) is proposed as a possible solution for encrypting database files. However, current solutions do not suit the Cloud/Edge continuum requirements. In this paper, we aim at fulfilling this gap by proposing a solution to encrypt the data locally at the Edge and transfer them to a distributed database over the Cloud. Our approach allows us to perform queries directly on encrypted data over the Cloud and to retrieve them on the Edge for decryption. Experiments performed on different NoSQL DBMS solutions demonstrate the feasibility of our approach.

Nowadays the process of anonymization of documents has been the subject of several studies and debates. By anonymization of documents, we mean the process of replacing sensitive data in order to preserve the confidentiality of documents without altering their content. In this work, we introduce Docflow, an open-source document anonymization engine capable of anonymizing documents based on specific filters chosen by the user. We applied Docflow to anonymize a set of legal documents and performed a processing performance analysis. By providing a Markdown input file to be anonymized, Docflow is able to redact all information according to users' choices, preserving the document content. Docflow will be integrated with NLP algorithms for the generation of the Markdown source file starting from documents already processed in different formats, but always with human supervision in the loop.

This paper focuses on a new approach to conceiving “virtual sensors” operating in smart environments, which are abstracted components able to map different behaviours on the same Internet of Things (IoT)-based infrastructures according to the needs of the high-level applications. To realize “virtual sensors”, it is necessary to codify user requests in an automation process for the deployment at the Edge of the microservices (MSs) that satisfy such requests. We present a solution that implements all the necessary functionalities to bind the user application with the Edge device in charge to execute the “virtual sensors”. In particular, the solution we propose is based on the FIWARE NGSI-LD information model, which helps us to standardize the communication among the different entities involved in the process. Moreover, the paper describes the reference architecture we designed, provides the implementation details of our first prototype and reports the results of our evaluation experiments.

The innovation process for the management of a Water Distribution Network (WDN) in a Smart City starts from an efficient digital representation of the network itself. This paper presents a new visualization tool for WDN that overcomes current challenges and provides water companies with useful managing information. Existing visualization tools are self-contained systems that work independently from other visualization software and do not provide real-time analysis of the pipes and water flow status in the WDN. Using digital maps such as Google Maps it is possible to extend the traditional digital representation of the WDN based on EPANET software. Moreover, the WDN representation can be enriched with localized information (e.g. roads or buildings superimposed on the WDN), that is useful for planning maintenance and structural services. In presence of a WDN equipped with sensors and flowmeters, the proposed tool can be used for optimized visualization of the flow rate and the condition of the pipes in real-time. For these reasons, this tool can be a powerful instrument to help technicians quickly identify problems in the WDN. In this work, we used synthetic data generation techniques to obtain a data-set of values that updated over time. Finally, to evaluate the designed solution, we implemented the proposed visualization tool and performed some experiments to test its effectiveness.

Nowadays, the Internet of Things (IoT) is widely adopted. To push-down costs, a possible solution can be represented by the large-scale adoption of low-cost sensors that, unfortunately, present several issues including sensor ageing that may cause, over time, failures in IoT systems due to erroneous collected data. In our opinion, a possible solution is IoT rejuvenation, a proactive cost-effective technique that can contrast the inevitable ageing of IoT systems guaranteeing the accuracy of collected data over time. Specifically, the purpose of this paper is to experimentally demonstrate the seriousness of the IoT ageing issue. To achieve such a goal, we consider a smart city scenario including intelligent street pole lamps equipped with low-cost ultrasonic sensors able to switch on lights when a vehicle is detected. Moreover, we pave the way toward IoT rejuvenation by discussing the perspectives of a Function as a Service (FaaS) based approach acting at the Edge.

Nowadays, video surveillance is a very common practice in Smart Cities. There are public and private video surveillance systems, and very often different systems or single devices frame the same area. However, when a target needs to be identified or needs to be tracked in real-time, such solutions typically require human intervention to configure the devices in the best possible way (e.g., choosing the optimal cameras, setting up their focus, and so on). To address such a problem, in this paper, we define a new interrogation method based on a Federated Edge approach. This approach addresses the problem from the point of view of both camera hardware and shooting angle associated with it. According to the presented approach, it is possible to understand which the best camera to identify a target and possibly tracking it in a specific area is. A case study is defined in the context of urban mobility management.

The usage of untrusted, non-certified or non-validated Internet of Things (IoT) devices can affect the entire system functioning, causing service interruption and invalidating information processing. These vulnerabilities are mainly due to the errors in providing services and the whole ecosystem's exposure to different cyber attacks. This paper aims to address these problems by proposing a new method to create a trusted distributed environment for implementing IoT services based on Edge computing. Our solution is based on the key idea that IoT devices are certified by a certification authority (CA) through an authorized broker. IoT devices will connect to the CA only if they have been previously authorized to the same. The CA will also allow single isolated IoT nodes to participate in one or more applications in a specific FIWARE-based domain. The entire certification process of IoT devices and the digital certificate issuance take place using a Mobile Edge Computing system (MEC) located near the IoT node. The MEC is the broker device that allows the interaction of the IoT device with the CA. We implemented the proposed solution and performed some experiments to test its effectiveness.

Social Networks, geotags, and Virtual Reality (VR) are parts of the everyday life of most of the people in the world. In particular, geotags represents a way to discover places through metadata added to media. At the same time, VR reproduces with high accuracy real places that we can navigate through a browser or a 3D visor. In this paper, we present GAVIN to fill the gap between social media geotagging and Virtual Indoor Navigation. GAVIN is a platform for the navigation of virtual environments able to exploit BIM Digital Twins and geolocated data coming from different sources and, in particular, from the social experience bounded to a real/virtual place. In the paper we provide design and implementation details on the GAVIN platform, and we present some performance evaluations.