Wearable sensors networks for safety applications in industrial scenarios

Industrial contexts, and in particular the port areas, are very complex systems to be monitored and controlled due to the combined presence of vehicles and people. The port areas are the gateway between navigation and terrestrial transportation and are of great importance in transport logistics. Unfortunately, the management of port areas is quite complex because the safety of the workers must be always assured. Therefore, in such a context, a centralized control system for the monitoring and the prevention of risks is of particular importance. In this thesis, a real-time control system for the monitoring of people and vehicles in industrial areas is proposed. The proposed system is based on the Internet of Things paradigm, i.e. a network of “things” (such as sensors, tag RFID, actuators etc.) which can communicate and interact with each other within a shared IP addressing range, in order to share data and contribute to the management and development of advanced applications. Specifically, the thesis is focused on the design of a wearable sensors network based on RFID technology, and specifically on WISP sensors, for assuring the safety of the workers. In this network, wearable devices that can be inserted directly on the textile have been selected. Differently from conventional sensors, wearable sensors ensure a higher level of comfort, and provide higher electromagnetic performance. Furthermore, textile materials are easily available. Microstrips are good candidates for these applications because they mainly radiate perpendicularly to the planar structure, and their ground plane allows a good shielding on the body tissues. Therefore, I have designed specific antennas for RFID, that unlike the classical microstrip antennas have the radiating surface composed of several "side by side" conductive "threads of textile". Since the microwave model does not allow the design of an antenna with these characteristics with a good approximation, a specific microwave model for coupled lines has been designed. With this model, the specific antenna for RFID has been designed, with Jeans as substrate. The particular antenna’s substrate allows direct integration into garments, but since the wearable antennas are placed very close to the human body, biological issues which may arise on the human body from the use of these sensors have been analysed. The Specific Absorption Rate (SAR) has been considered and simulations have been conducted for evaluating the effects on the human body, and especially on the head, when irradiated with the electromagnetic waves generated by the wearable antenna realized with different materials. Dosimetric effects have been evaluated in function of the distance from the body, in order to define a safe distance for placing the antenna on the human body. The SAR has been evaluated also for full patches with different textile substrates, whose surface is larger than that of the proposed model of coupled lines. Therefore, if the SAR values evaluated for the full patch are satisfying, the SAR values for the model of coupled lines will surely be acceptable

Wearable sensors networks for safety applications in industrial scenarios

Industrial contexts, and in particular the port areas, are very complex systems to be monitored and controlled due to the combined presence of vehicles and people. The port areas are the gateway between navigation and terrestrial transportation and are of great importance in transport logistics. Unfortunately, the management of port areas is quite complex because the safety of the workers must be always assured. Therefore, in such a context, a centralized control system for the monitoring and the prevention of risks is of particular importance. In this thesis, a real-time control system for the monitoring of people and vehicles in industrial areas is proposed. The proposed system is based on the Internet of Things paradigm, i.e. a network of “things” (such as sensors, tag RFID, actuators etc.) which can communicate and interact with each other within a shared IP addressing range, in order to share data and contribute to the management and development of advanced applications. Specifically, the thesis is focused on the design of a wearable sensors network based on RFID technology, and specifically on WISP sensors, for assuring the safety of the workers. In this network, wearable devices that can be inserted directly on the textile have been selected. Differently from conventional sensors, wearable sensors ensure a higher level of comfort, and provide higher electromagnetic performance. Furthermore, textile materials are easily available. Microstrips are good candidates for these applications because they mainly radiate perpendicularly to the planar structure, and their ground plane allows a good shielding on the body tissues. Therefore, I have designed specific antennas for RFID, that unlike the classical microstrip antennas have the radiating surface composed of several "side by side" conductive "threads of textile". Since the microwave model does not allow the design of an antenna with these characteristics with a good approximation, a specific microwave model for coupled lines has been designed. With this model, the specific antenna for RFID has been designed, with Jeans as substrate. The particular antenna’s substrate allows direct integration into garments, but since the wearable antennas are placed very close to the human body, biological issues which may arise on the human body from the use of these sensors have been analysed. The Specific Absorption Rate (SAR) has been considered and simulations have been conducted for evaluating the effects on the human body, and especially on the head, when irradiated with the electromagnetic waves generated by the wearable antenna realized with different materials. Dosimetric effects have been evaluated in function of the distance from the body, in order to define a safe distance for placing the antenna on the human body. The SAR has been evaluated also for full patches with different textile substrates, whose surface is larger than that of the proposed model of coupled lines. Therefore, if the SAR values evaluated for the full patch are satisfying, the SAR values for the model of coupled lines will surely be acceptable

Task allocation in the Internet of Things

The last few years have been involved by the technological revolution represented by the Internet of Things (IoT). The IoT vision aims to interconnect devices with different capabilities such as sensors, actuators, Radio Frequency Identification (RFID) tags, smart objects (e.g. smartphones), and servers, within the same heterogeneous network. The aim is to enable the network objects to dynamically cooperate and make their resources available, in order to reach a goal, i.e. the execution of one or more applications assigned to the network. As known since its invention, the Internet interconnects nodes with dissimilar characteristics without central authorities by introducing some simple yet effective protocols that allow for nodes' interoperability so that information is successfully exchanged and services are provided by servers to clients and among peers. Fortunately, the same happens among objects in the IoT so that interoperability is assured and the data sensed by objects distributed and connected to the physical world is now available for the benefit of the human users. The realization of the IoT paradigm relies on the implementation of systems of cooperative intelligent objects with key interoperability capabilities. However, to reach this goal, it's important to consider some key features that characterize many IoT objects: i) available nodes' resources (electrical energy, memory, processing, node capability to perform a given task) are often limited. This is the case, for example, of battery powered nodes, which have limited energy amounts. ii) sensors may provide information that is not unique but can be generated by set of different objects which for example are capable to sense the same physical measure of the same geographical. iii) the number of nodes in the IoT is quickly overcoming the number of hosts in the 'traditional' Internet and most of these have a low reliability due mostly to the mobility and energy. This entails for a new paradigm of communication according to which objects coordinate with the other objects in groups and provide a unified service to the external world (the application that requires the service), with the intent to distribute the load of the requested services according to specific community defined rules, which could be: lifetime extension, QoS (Quality of Service) maximization, reward maximization, or others. It is evident that an appropriate coordination of objects' resources utilization would consistently improve their performance. This foreword is necessary to introduce this thesis, which is defined as follows. Task allocation in the IoT: given the IoT paradigm and the requirements of IoT applications, the nodes involved in the execution of the same application should cooperate to reach the optimal allocation of tasks among them. They should execute tasks to reach the global application target and to satisfy the relevant requirements while optimizing the network performance in terms of resources used. This issue should be continuously addressed to dynamically adapt the system to changes in terms of application requirements and network topolog

An ensemble architecture for forgery detection and localization in digital images

Questa tesi presenta un approccio d'insieme unificato - "ensemble" - per il rilevamento e la localizzazione di contraffazioni in immagini digitali. Il focus della ricerca è su due delle più comuni ma efficaci tecniche di contraffazione: "copy-move" e "splicing". L'architettura proposta combina una serie di metodi di rilevamento e localizzazione di manipolazioni per ottenere prestazioni migliori rispetto a metodi utilizzati in modalità "standalone". I principali contributi di questo lavoro sono elencati di seguito. In primo luogo, nel Capitolo 1 e 2 viene presentata un'ampia rassegna dell'attuale stato dell'arte nel rilevamento di manipolazioni ("forgery"), con particolare attenzione agli approcci basati sul deep learning. Un'importante intuizione che ne deriva è la seguente: questi approcci, sebbene promettenti, non possono essere facilmente confrontati in termini di performance perché tipicamente vengono valutati su dataset personalizzati a causa della mancanza di dati annotati con precisione. Inoltre, spesso questi dati non sono resi disponibili pubblicamente. Abbiamo poi progettato un algoritmo di rilevamento di manipolazioni copy-move basato su "keypoint", descritto nel capitolo 3. Rispetto a esistenti approcci simili, abbiamo aggiunto una fase di clustering basato su densità spaziale per filtrare le corrispondenze rumorose dei keypoint. I risultati hanno dimostrato che questo metodo funziona bene su due dataset di riferimento e supera uno dei metodi più citati in letteratura. Nel Capitolo 4 viene proposta una nuova architettura per predire la direzione della luce 3D in una data immagine. Questo approccio sfrutta l'idea di combinare un metodo "data-driven" con un modello di illuminazione fisica, consentendo così di ottenere prestazioni migliori. Al fine di sopperire al problema della scarsità di dati per l'addestramento di architetture di deep learning altamente parametrizzate, in particolare per il compito di scomposizione intrinseca delle immagini, abbiamo sviluppato due algoritmi di generazione dei dati. Questi sono stati utilizzati per produrre due dataset - uno sintetico e uno di immagini reali - con lo scopo di addestrare e valutare il nostro approccio. Il modello di stima della direzione della luce proposto è stato sfruttato in un nuovo approccio di rilevamento di manipolazioni di tipo splicing, discusso nel Capitolo 5, in cui le incoerenze nella direzione della luce tra le diverse regioni dell'immagine vengono utilizzate per evidenziare potenziali attacchi splicing. L'approccio ensemble proposto è descritto nell'ultimo capitolo. Questo include un modulo "FusionForgery" che combina gli output dei metodi "base" proposti in precedenza e assegna un'etichetta binaria (forged vs. original). Nel caso l'immagine sia identificata come contraffatta, il nostro metodo cerca anche di specializzare ulteriormente la decisione tra attacchi splicing o copy-move. In questo secondo caso, viene eseguito anche un tentativo di ricostruire le regioni "sorgente" utilizzate nell'attacco copy-move. Le prestazioni dell'approccio proposto sono state valutate addestrandolo e testandolo su un dataset sintetico, generato da noi, comprendente sia attacchi copy-move che di tipo splicing. L'approccio ensemble supera tutti i singoli metodi "base" in termini di prestazioni, dimostrando la validità della strategia proposta.This thesis presents a unified ensemble approach for forgery detection and localization in digital images. The focus of the research is on two of the most common but effective forgery techniques: copy-move and splicing. The ensemble architecture combines a set of forgery detection and localization methods in order to achieve improved performance with respect to standalone approaches. The main contributions of this work are listed in the following. First, an extensive review of the current state of the art in forgery detection, with a focus on deep learning-based approaches is presented in Chapter 1 and 2. An important insight that is derived is the following: these approaches, although promising, cannot be easily compared in terms of performance because they are typically evaluated on custom datasets due to the lack of precisely annotated data. Also, they are often not publicly available. We then designed a keypoint-based copy-move detection algorithm, which is described in Chapter 3. Compared to previous existing keypoints-based approaches, we added a density-based clustering step to filter out noisy keypoints matches. This method has been demonstrated to perform well on two benchmark datasets and outperforms one of the most cited state-of-the-art methods. In Chapter 4 a novel architecture is proposed to predict the 3D light direction of the light in a given image. This approach leverages the idea of combining, in a data-driven method, a physical illumination model that allows for improved regression performance. In order to fill in the gap of data scarcity for training highly-parameterized deep learning architectures, especially for the task of intrinsic image decomposition, we developed two data generation algorithms that were used to produce two datasets - one synthetic and one of real images - to train and evaluate our approach. The proposed light direction estimation model has then been employed to design a novel splicing detection approach, discussed in Chapter 5, in which light direction inconsistencies between different regions in the image are used to highlight potential splicing attacks. The proposed ensemble scheme for forgery detection is described in the last chapter. It includes a "FusionForgery" module that combines the outputs of the different previously proposed "base" methods and assigns a binary label (forged vs. pristine) to the input image. In the case of forgery prediction, our method also tries to further specialize the decision between splicing and copy-move attacks. If the image is predicted as copy-moved, an attempt to reconstruct the source regions used in the copy-move attack is also done. The performance of the proposed approach has been assessed by training and testing it on a synthetic dataset, generated by us, comprising both copy-move and splicing attacks. The ensemble approach outperforms all of the individual "base" methods, demonstrating the validity of the proposed strategy

JPEG XR scalable coding for remote image browsing applications

The growing popularity of the Internet has opened the road to multimedia and interactivity, emphasizing the importance of visual communication. In this context, digital images have taken a lead role and have an increasing number of applications. Consider, for example, the spread that digital cameras and mobile devices such as mobile phones have become in recent years. Thus, it arises the need for a flexible system that can handle images from different sources and are able to adapt to a different view. The importance of this issue lies in the application scenario: today there are datastores with a large number of images saved in JPEG format and systems for rendering digital images are various and with very different characteristics with each other. The ISO/IEC committee has recently issued a new format, called JPEG-XR, created explicitly for the modern digital cameras. The new coding algorithm JPEG-XR, can overcome various limitations of the first JPEG algorithm and provides viable alternatives to the JPEG2000 algorithm. This research has primarily focused on issues concerning the scalability of the new format of digital images.Additional scalability levels are fundamental for image browsing applications, because enable the system to ensure a correct and efficient functioning even when there is a sharp increase in the number of resources and users.Scalability is mostly required when dealing with large image database on the Web in order to reduce the transferred data, especially when it comes to large images. The interactive browsing also requires the ability to access to arbitrary parts of the image. The starting point is the use of a client-server architecture, in which the server stores a database of JPEG XR images and analyzes requests from a client. Client and server communicate via HTTP and use an exchange protocol. In order to minimize the transferred information, the JPEG XR coded file format should make use of the frequency mode order and partitioning of images into optimized tiles. The main goal is transmitting only some subset of the available sub-band coefficients. This is necessary to allow access an interactive access to portion of images, that are downloaded and displayed, minimizing the amount of data transferred and maintaining an acceptable image quality.The proposed architecture has of course prompted a study of errors in transmission on unreliable channel, such as the wireless one, and the definition of possible optimizations/variants of the codec in order to overcome its own limitations. Image data compressed with JPEG XR when transmitted over error-prone channels is severely distorted. In fact, due to the adaptive coding strategies used by the codec, even a single bit error causes a mismatch in the alignment of the reading position from the bit-stream, leading to completely different images at the decoder side. An extension to the JPEG XR algorithm is proposed, consisting in an error recovery process enabling the decoder to realign itself to the right bit-stream position and to correctly decode the most part of the image. Several experiments have been performed using different encoder parameter and different error probabilities while image distortion is measured by PSNR objective metric. The simplicity of the proposed algorithm adds very little computational overhead and seems very promising as confirmed by objective image quality results in experimental tests

Evolution of Communication and Monitoring Systems for the Management of Traffic and Safety Control Systems in Railway Sites

The collaboration between R.F.I. S.p.A. and Alma Mater Studiorum of Bologna had the purpose to evaluate the feasibility and to investigate the main radar sensor tech- nologies suited to be integrated with existing signalling systems in rail sites, with particular reference to the UWB technology. This activity has been supported by a study of the UWB radio channel characteristics in a reference (LC-like) scenario, through ray tracing (RT) simulation. It was also studied, with the aid of computer simulations, the remote interconnection of several radars via fiber optic links (RoF), in order to concentrate in a single physical location all the signal processing operations and network control

Heterogeneous wireless networks for smart cities

In the near future, a world of smart cities is envisioned in which many devices equipped with sensors and communication interfaces can be used to collect and share data in order to derive maps or infer information on some parameter of interest. Wireless technologies are enabling this smart city paradigms, where many items are networked for the growth of society. This scenario opens new challenges to wireless network designers, with new performance metrics, coverage and privacy needs, as well as the need for a tighter integration of different networks. This is the fundamental concept of Heterogeneous Networks. Enclosing humans in the loop, through crowdsensing techniques, will dramatically increase the amount of data available for the mapping process, with obvious benefits in terms of the resulting accuracy. On the other hand, the huge amount of data generated represents also a challenge that, along with the irregular, uncontrollable, spatial distribution of measurements represent serious challenges to be addressed. Another important aspect of smart cities scenarios is represented by vehicular networks. Several technologies have been proposed to address such application. Among the others, an interesting solution is provided by Visible Light Communications (VLC). Based on the use of the light emission diodes (LEDs) that are already available on the majority of vehicles, VLC would enable short range communication in large, unlicensed, and uncongested bands with limited costs. In the framework of smart cities scenarios, my research activity aimed at formulating and solving some of the issues arising from the envisioned challenging services, with both analytical and simulation-based approaches