Smart Sensors For Interoperable Smart Environment

Smart Environments are currently considered a key factor to connect the physical world with the information world. A Smart Environment can be defined as the combination of a physical environment, an infrastructure for data management (called Smart Space), a collection of embedded systems gathering heterogeneous data from the environment and a connectivity solution to convey these data to the Smart Space. With this vision, any application which takes advantages from the environment could be devised, without the need to directly access to it, since all information are stored in the Smart Space in a interoperable format. Moreover, according to this vision, for each entity populating the physical environment, i.e. users, objects, devices, environments, the following questions can be arise: “Who?”, i.e. which are the entities that should be identified? “Where?” i.e. where are such entities located in physical space? and “What?” i.e. which attributes and properties of the entities should be stored in the Smart Space in machine understandable format, in the sense that its meaning has to be explicitly defined and all the data should be linked together in order to be automatically retrieved by interoperable applications. Starting from this the location detection is a necessary step in the creation of Smart Environments. If the addressed entity is a user and the environment a generic environment, a meaningful way to assign the position, is through a Pedestrian Tracking System. In this work two solution for these type of system are proposed and compared. One of the two solution has been studied and developed in all its aspects during the doctoral period. The work also investigates the problem to create and manage the Smart Environment. The proposed solution is to create, by means of natural interactions, links between objects and between objects and their environment, through the use of specific devices, i.e. Smart Objects

Exploiting the environment for structural health monitoring: Strategies, algorithms, and technologies

A densely built environment is a complex system of infrastructure, nature, and people closely interconnected and interacting. Vehicles, public transport, weather action, and sports activities constitute a manifold set of excitation and degradation sources for civil structures. In this context, operators should consider different factors in a holistic approach for assessing the structural health state. Vibration-based structural health monitoring (SHM) has demonstrated great potential as a decision-supporting tool to schedule maintenance interventions. However, most excitation sources are considered an issue for practical SHM applications since traditional methods are typically based on strict assumptions on input stationarity. Last-generation low-cost sensors present limitations related to a modest sensitivity and high noise floor compared to traditional instrumentation. If these devices are used for SHM in urban scenarios, short vibration recordings collected during high-intensity events and vehicle passage may be the only available datasets with a sufficient signal-to-noise ratio. While researchers have spent efforts to mitigate the effects of short-term phenomena in vibration-based SHM, the ultimate goal of this thesis is to exploit them and obtain valuable information on the structural health state. First, this thesis proposes strategies and algorithms for smart sensors operating individually or in a distributed computing framework to identify damage-sensitive features based on instantaneous modal parameters and influence lines. Ordinary traffic and people activities become essential sources of excitation, while human-powered vehicles, instrumented with smartphones, take the role of roving sensors in crowdsourced monitoring strategies. The technical and computational apparatus is optimized using in-memory computing technologies. Moreover, identifying additional local features can be particularly useful to support the damage assessment of complex structures. Thereby, smart coatings are studied to enable the self-sensing properties of ordinary structural elements. In this context, a machine-learning-aided tomography method is proposed to interpret the data provided by a nanocomposite paint interrogated electrically.

Smart Textile Sensors for Healthcare Monitoring

Wearable electronic textiles are an emerging research field playing a pivotal role among several different technological areas such as sensing, communication, clothing, health monitoring, information technology, and microsystems. The possibility to realise a fully-textile platform, endowed with various sensors directly realised with textile fibres and fabric, represents a new challenge for the entire research community. Among several high-performing materials, the intrinsically conductive poly(3,4-ethylenedioxythiophene) (PEDOT), doped with poly(styrenesulfonic acid) (PSS), or PEDOT:PSS, is one of the most representative and utilised, having an excellent chemical and thermal stability, as well as reversible doping state and high conductivity. This work relies on PEDOT:PSS combined with sensible materials to design, realise, and develop textile chemical and physical sensors. In particular, chloride concentration and pH level sensors in human sweat for continuous monitoring of the wearer's hydration status and stress level are reported. Additionally, a prototype smart bandage detecting the moisture level and pH value of a bed wound to allow the remote monitoring of the healing process of severe and chronic wounds is described. Physical sensors used to monitor the pressure distribution for rehabilitation, workplace safety, or sport tracking are also presented together with a novel fully-textile device able to measure the incident X-ray dose for medical or security applications where thin, comfortable, and flexible features are essential. Finally, a proof-of-concept for an organic-inorganic textile thermoelectric generator that harvests energy directly from body heat has been proposed. Though further efforts must be dedicated to overcome issues such as durability, washability, power consumption, and large-scale production, the novel, versatile, and widely encompassing area of electronic textiles is a promising protagonist in the upcoming technological revolution.

Design and characterization of wideband Hall current sensors in BCD technology for smart power and metering applications

Power electronic circuits are moving towards higher switching frequencies, exploiting the capabilities of novel devices to shrink the dimension of passive components. This trend demands sensors capable enough to operate at such high frequencies. This thesis aims to demonstrate through experimental characterization, the broadband capability of a fully integrated CMOS X-Hall current sensor in current mode interfaced with a transimpedance amplifier (TIA), chip CH09, realized in CMOS technology for power electronics applications such as power converters. The system exploits a common-mode control system to operate the dual supply system, 5-V for the X-Hall probe and 1.2-V for the readout. The developed prototype achieves a maximum acquisition bandwidth of 12 MHz, a power consumption of 11.46 mW, resolution of 39 mArms, a sensitivity of 8 % /T, and a FoM of 569-MHz/A2mW, significantly higher than current state-of-the-art. Further enhancements were proposed to CH09 as a new chip CH100, aiming for accuracy levels prerequisite for a real-time power electronic application. The TIA was optimized for a wider bandwidth of 26.7 MHz with nearly 30% reduction of the integrated input referred noise of 26.69 nArms at the probe-AFE interface in the frequency band of DC-30 MHz, and a 10% improvement in the dynamic range. The expected input range is 5-A. The chip incorporates a dual sensing chain for differential sensing to overcome common mode interferences. A novel offset cancellation technique is proposed that would require switching of polarity of bias currents. Thermal gain drift was improved by a factor of 8 and will be digitally calibrated utilizing a new built-in temperature sensor with a post calibration measurement accuracy greater than 1%. The estimated power consumption of the entire system is 55.6 mW. Both prototypes have been implemented through a 90-nm microelectronic process from STMicroelectronics and occupy a silicon area of 2.4 mm2.

Architectures for Context Aware Services in Smart Environments

This thesis deals with Context Aware Services, Smart Environments, Context Management and solutions for Devices and Service Interoperability. Multi-vendor devices offer an increasing number of services and end-user applications that base their value on the ability to exploit the information originating from the surrounding environment by means of an increasing number of embedded sensors, e.g. GPS, compass, RFID readers, cameras and so on. However, usually such devices are not able to exchange information because of the lack of a shared data storage and common information exchange methods. A large number of standards and domain specific building blocks are available and are heavily used in today's products. However, the use of these solutions based on ready-to-use modules is not without problems. The integration and cooperation of different kinds of modules can be daunting because of growing complexity and dependency. In this scenarios it might be interesting to have an infrastructure that makes the coexistence of multi-vendor devices easy, while enabling low cost development and smooth access to services. This sort of technologies glue should reduce both software and hardware integration costs by removing the trouble of interoperability. The result should also lead to faster and simplified design, development and, deployment of cross-domain applications. This thesis is mainly focused on SW architectures supporting context aware service providers especially on the following subjects: - user preferences service adaptation - context management - content management - information interoperability - multivendor device interoperability - communication and connectivity interoperability Experimental activities were carried out in several domains including Cultural Heritage, indoor and personal smart spaces – all of which are considered significant test-beds in Context Aware Computing. The work evolved within european and national projects: on the europen side, I carried out my research activity within EPOCH, the FP6 Network of Excellence on “Processing Open Cultural Heritage” and within SOFIA, a project of the ARTEMIS JU on embedded systems. I worked in cooperation with several international establishments, including the University of Kent, VTT (the Technical Reserarch Center of Finland) and Eurotech. On the national side I contributed to a one-to-one research contract between ARCES and Telecom Italia. The first part of the thesis is focused on problem statement and related work and addresses interoperability issues and related architecture components. The second part is focused on specific architectures and frameworks: - MobiComp: a context management framework that I used in cultural heritage applications - CAB: a context, preference and profile based application broker which I designed within EPOCH Network of Excellence - M3: "Semantic Web based" information sharing infrastructure for smart spaces designed by Nokia within the European project SOFIA - NoTa: a service and transport independent connectivity framework - OSGi: the well known Java based service support framework The final section is dedicated to the middleware, the tools and, the SW agents developed during my Doctorate time to support context-aware services in smart environments.

Quality of Service in Distributed Stream Processing for large scale Smart Pervasive Environments

The wide diffusion of cheap, small, and portable sensors integrated in an unprecedented large variety of devices and the availability of almost ubiquitous Internet connectivity make it possible to collect an unprecedented amount of real time information about the environment we live in. These data streams, if properly and timely analyzed, can be exploited to build new intelligent and pervasive services that have the potential of improving people's quality of life in a variety of cross concerning domains such as entertainment, health-care, or energy management. The large heterogeneity of application domains, however, calls for a middleware-level infrastructure that can effectively support their different quality requirements. In this thesis we study the challenges related to the provisioning of differentiated quality-of-service (QoS) during the processing of data streams produced in pervasive environments. We analyze the trade-offs between guaranteed quality, cost, and scalability in streams distribution and processing by surveying existing state-of-the-art solutions and identifying and exploring their weaknesses. We propose an original model for QoS-centric distributed stream processing in data centers and we present Quasit, its prototype implementation offering a scalable and extensible platform that can be used by researchers to implement and validate novel QoS-enforcement mechanisms. To support our study, we also explore an original class of weaker quality guarantees that can reduce costs when application semantics do not require strict quality enforcement. We validate the effectiveness of this idea in a practical use-case scenario that investigates partial fault-tolerance policies in stream processing by performing a large experimental study on the prototype of our novel LAAR dynamic replication technique. Our modeling, prototyping, and experimental work demonstrates that, by providing data distribution and processing middleware with application-level knowledge of the different quality requirements associated to different pervasive data flows, it is possible to improve system scalability while reducing costs.

Metrological characterization of sensors and instrumentation for distribution grid monitoring and electrical asset diagnostics

The Smart Grid needs a large amount of information to be operated and day by day new information is required to improve the operation performance. It is also fundamental that the available information is reliable and accurate. Therefore, the role of metrology is crucial, especially if applied to the distribution grid monitoring and the electrical assets diagnostics. This dissertation aims at better understanding the sensors and the instrumentation employed by the power system operators in the above-mentioned applications and studying new solutions. Concerning the research on the measurement applied to the electrical asset diagnostics: an innovative drone-based measurement system is proposed for monitoring medium voltage surge arresters. This system is described, and its metrological characterization is presented. On the other hand, the research regarding the measurements applied to the grid monitoring consists of three parts. The first part concerns the metrological characterization of the electronic energy meters’ operation under off-nominal power conditions. Original test procedures have been designed for both frequency and harmonic distortion as influence quantities, aiming at defining realistic scenarios. The second part deals with medium voltage inductive current transformers. An in-depth investigation on their accuracy behavior in presence of harmonic distortion is carried out by applying realistic current waveforms. The accuracy has been evaluated by means of the composite error index and its approximated version. Based on the same test setup, a closed-form expression for the measured current total harmonic distortion uncertainty estimation has been experimentally validated. The metrological characterization of a virtual phasor measurement unit is the subject of the third and last part: first, a calibrator has been designed and the uncertainty associated with its steady-state reference phasor has been evaluated; then this calibrator acted as a reference, and it has been used to characterize the phasor measurement unit implemented within a real-time simulator.

Surveillance risks in IoT applied to smart cities

Nowadays, cities deal with unprecedented pollution and overpopulation problems, and Internet of Things (IoT) technologies are supporting them in facing these issues and becoming increasingly smart. IoT sensors embedded in public infrastructure can provide granular data on the urban environment, and help public authorities to make their cities more sustainable and efficient. Nonetheless, this pervasive data collection also raises high surveillance risks, jeopardizing privacy and data protection rights. Against this backdrop, this thesis addresses how IoT surveillance technologies can be implemented in a legally compliant and ethically acceptable fashion in smart cities. An interdisciplinary approach is embraced to investigate this question, combining doctrinal legal research (on privacy, data protection, criminal procedure) with insights from philosophy, governance, and urban studies. The fundamental normative argument of this work is that surveillance constitutes a necessary feature of modern information societies. Nonetheless, as the complexity of surveillance phenomena increases, there emerges a need to develop more fine-attuned proportionality assessments to ensure a legitimate implementation of monitoring technologies. This research tackles this gap from different perspectives, analyzing the EU data protection legislation and the United States and European case law on privacy expectations and surveillance. Specifically, a coherent multi-factor test assessing privacy expectations in public IoT environments and a surveillance taxonomy are proposed to inform proportionality assessments of surveillance initiatives in smart cities. These insights are also applied to four use cases: facial recognition technologies, drones, environmental policing, and smart nudging. Lastly, the investigation examines competing data governance models in the digital domain and the smart city, reviewing the EU upcoming data governance framework. It is argued that, despite the stated policy goals, the balance of interests may often favor corporate strategies in data sharing, to the detriment of common good uses of data in the urban context.

Development of textile wearable chemical sensors

In this elaborate, a textile-based Organic Electrochemical Transistor (OECT) was first developed for the determination of uric acid in wound exudate based on the conductive polymer poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS), which was then coupled to an electrochemically gated textile transistor consisting of a composite of iridium oxide particles and PEDOT:PSS for pH monitoring in wound exudate. In that way a sensor for multiparameter monitoring of wound health status was assembled, including the ability to differentiate between a wet-dry status of the smart bandage by implementing impedance measurements exploiting the OECT architecture. Afterwards, for both wound management as well as generic health status tracking applications, a glass-based calcium sensor was developed employing polymeric ion-selective membranes on a novel architecture inspired by the Wrighton OECT configuration, which was later converted to a Proof-of-Concept textile prototype for wearable applications. Lastly, in collaboration with the King Abdullah University of Science and Technology (KAUST, Thuwal, Saudi Arabia) under the supervision of Prof. Sahika Inal, different types of ion-selective thiophene-based monomers were used to develop ion-selective conductive polymers to detect sodium ion by different methods, involving standard potentiometry and OECT-based approaches. The textile OECTs for uric acid detection performances were optimized by investigating the geometry effect on the instrumental response and the properties of the different textile materials involved in their production, with a special focus on the final application that implies the operativity in flow conditions to simulate the wound environment. The same testing route was followed for the multiparameter sensor and the calcium sensor prototype, with a particular care towards the ion-selective membrane composition and electrode conditioning protocol optimization. The sodium-selective polymer electrosynthesis was optimized in non-aqueous environments and was characterized by means of potentiostatic and potentiodynamic techniques coupled with Quartz Crystal Microbalance and spectrophotometric measurements.

Design and development of new pressure sensors for aerodynamic applications

This artwork reports on two different projects that were carried out during the three years of Doctor of the Philosophy course. In the first years a project regarding Capacitive Pressure Sensors Array for Aerodynamic Applications was developed in the Applied Aerodynamic research team of the Second Faculty of Engineering, University of Bologna, Forlì, Italy, and in collaboration with the ARCES laboratories of the same university. Capacitive pressure sensors were designed and fabricated, investigating theoretically and experimentally the sensor’s mechanical and electrical behaviours by means of finite elements method simulations and by means of wind tunnel tests. During the design phase, the sensor figures of merit are considered and evaluated for specific aerodynamic applications. The aim of this work is the production of low cost MEMS-alternative devices suitable for a sensor network to be implemented in air data system. The last two year was dedicated to a project regarding Wireless Pressure Sensor Network for Nautical Applications. Aim of the developed sensor network is to sense the weak pressure field acting on the sail plan of a full batten sail by means of instrumented battens, providing a real time differential pressure map over the entire sail surface. The wireless sensor network and the sensing unit were designed, fabricated and tested in the faculty laboratories. A static non-linear coupled mechanical-electrostatic simulation, has been developed to predict the pressure versus capacitance static characteristic suitable for the transduction process and to tune the geometry of the transducer to reach the required resolution, sensitivity and time response in the appropriate full scale pressure input A time dependent viscoelastic error model has been inferred and developed by means of experimental data in order to model, predict and reduce the inaccuracy bound due to the viscolelastic phenomena affecting the Mylar® polyester film used for the sensor diaphragm. The development of the two above mentioned subjects are strictly related but presently separately in this artwork.

A new snowfall detection algorithm for high latitude regions based on a combination of active and passive sensors

Precipitation retrieval over high latitudes, particularly snowfall retrieval over ice and snow, using satellite-based passive microwave spectrometers, is currently an unsolved problem. The challenge results from the large variability of microwave emissivity spectra for snow and ice surfaces, which can mimic, to some degree, the spectral characteristics of snowfall. This work focuses on the investigation of a new snowfall detection algorithm specific for high latitude regions, based on a combination of active and passive sensors able to discriminate between snowing and non snowing areas. The space-borne Cloud Profiling Radar (on CloudSat), the Advanced Microwave Sensor units A and B (on NOAA-16) and the infrared spectrometer MODIS (on AQUA) have been co-located for 365 days, from October 1st 2006 to September 30th, 2007. CloudSat products have been used as truth to calibrate and validate all the proposed algorithms. The methodological approach followed can be summarised into two different steps. In a first step, an empirical search for a threshold, aimed at discriminating the case of no snow, was performed, following Kongoli et al. [2003]. This single-channel approach has not produced appropriate results, a more statistically sound approach was attempted. Two different techniques, which allow to compute the probability above and below a Brightness Temperature (BT) threshold, have been used on the available data. The first technique is based upon a Logistic Distribution to represent the probability of Snow given the predictors. The second technique, defined Bayesian Multivariate Binary Predictor (BMBP), is a fully Bayesian technique not requiring any hypothesis on the shape of the probabilistic model (such as for instance the Logistic), which only requires the estimation of the BT thresholds. The results obtained show that both methods proposed are able to discriminate snowing and non snowing condition over the Polar regions with a probability of correct detection larger than 0.5, highlighting the importance of a multispectral approach.

Context detection and abstraction in smart environments

Context-aware computing is currently considered the most promising approach to overcome information overload and to speed up access to relevant information and services. Context-awareness may be derived from many sources, including user profile and preferences, network information, sensor analysis; usually context-awareness relies on the ability of computing devices to interact with the physical world, i.e. with the natural and artificial objects hosted within the "environment”. Ideally, context-aware applications should not be intrusive and should be able to react according to user’s context, with minimum user effort. Context is an application dependent multidimensional space and the location is an important part of it since the very beginning. Location can be used to guide applications, in providing information or functions that are most appropriate for a specific position. Hence location systems play a crucial role. There are several technologies and systems for computing location to a vary degree of accuracy and tailored for specific space model, i.e. indoors or outdoors, structured spaces or unstructured spaces. The research challenge faced by this thesis is related to pedestrian positioning in heterogeneous environments. Particularly, the focus will be on pedestrian identification, localization, orientation and activity recognition. This research was mainly carried out within the “mobile and ambient systems” workgroup of EPOCH, a 6FP NoE on the application of ICT to Cultural Heritage. Therefore applications in Cultural Heritage sites were the main target of the context-aware services discussed. Cultural Heritage sites are considered significant test-beds in Context-aware computing for many reasons. For example building a smart environment in museums or in protected sites is a challenging task, because localization and tracking are usually based on technologies that are difficult to hide or harmonize within the environment. Therefore it is expected that the experience made with this research may be useful also in domains other than Cultural Heritage. This work presents three different approaches to the pedestrian identification, positioning and tracking: Pedestrian navigation by means of a wearable inertial sensing platform assisted by the vision based tracking system for initial settings an real-time calibration; Pedestrian navigation by means of a wearable inertial sensing platform augmented with GPS measurements; Pedestrian identification and tracking, combining the vision based tracking system with WiFi localization. The proposed localization systems have been mainly used to enhance Cultural Heritage applications in providing information and services depending on the user’s actual context, in particular depending on the user’s location.

Progetto di reti Sensori Wireless e tecniche di Fusione Sensoriale

Ambient Intelligence (AmI) envisions a world where smart, electronic environments are aware and responsive to their context. People moving into these settings engage many computational devices and systems simultaneously even if they are not aware of their presence. AmI stems from the convergence of three key technologies: ubiquitous computing, ubiquitous communication and natural interfaces. The dependence on a large amount of fixed and mobile sensors embedded into the environment makes of Wireless Sensor Networks one of the most relevant enabling technologies for AmI. WSN are complex systems made up of a number of sensor nodes, simple devices that typically embed a low power computational unit (microcontrollers, FPGAs etc.), a wireless communication unit, one or more sensors and a some form of energy supply (either batteries or energy scavenger modules). Low-cost, low-computational power, low energy consumption and small size are characteristics that must be taken into consideration when designing and dealing with WSNs. In order to handle the large amount of data generated by a WSN several multi sensor data fusion techniques have been developed. The aim of multisensor data fusion is to combine data to achieve better accuracy and inferences than could be achieved by the use of a single sensor alone. In this dissertation we present our results in building several AmI applications suitable for a WSN implementation. The work can be divided into two main areas: Multimodal Surveillance and Activity Recognition. Novel techniques to handle data from a network of low-cost, low-power Pyroelectric InfraRed (PIR) sensors are presented. Such techniques allow the detection of the number of people moving in the environment, their direction of movement and their position. We discuss how a mesh of PIR sensors can be integrated with a video surveillance system to increase its performance in people tracking. Furthermore we embed a PIR sensor within the design of a Wireless Video Sensor Node (WVSN) to extend its lifetime. Activity recognition is a fundamental block in natural interfaces. A challenging objective is to design an activity recognition system that is able to exploit a redundant but unreliable WSN. We present our activity in building a novel activity recognition architecture for such a dynamic system. The architecture has a hierarchical structure where simple nodes performs gesture classification and a high level meta classifiers fuses a changing number of classifier outputs. We demonstrate the benefit of such architecture in terms of increased recognition performance, and fault and noise robustness. Furthermore we show how we can extend network lifetime by performing a performance-power trade-off. Smart objects can enhance user experience within smart environments. We present our work in extending the capabilities of the Smart Micrel Cube (SMCube), a smart object used as tangible interface within a tangible computing framework, through the development of a gesture recognition algorithm suitable for this limited computational power device. Finally the development of activity recognition techniques can greatly benefit from the availability of shared dataset. We report our experience in building a dataset for activity recognition. Such dataset is freely available to the scientific community for research purposes and can be used as a testbench for developing, testing and comparing different activity recognition techniques.

Design and Characterization of New Luminescent Sensors and Labels

The aim of this Ph.D. project has been the design and characterization of new and more efficient luminescent tools, in particular sensors and labels, for analytical chemistry, medical diagnostics and imaging. Actually both the increasing temporal and spatial resolutions that are demanded by those branches, coupled to a sensitivity that is required to reach the single molecule resolution, can be provided by the wide range of techniques based on luminescence spectroscopy. As far as the development of new chemical sensors is concerned, as chemists we were interested in the preparation of new, efficient, sensing materials. In this context, we kept developing new molecular chemosensors, by exploiting the supramolecular approach, for different classes of analytes. In particular we studied a family of luminescent tetrapodal-hosts based on aminopyridinium units with pyrenyl groups for the detection of anions. These systems exhibited noticeable changes in the photophysical properties, depending on the nature of the anion; in particular, addition of chloride resulted in a conformational change, giving an initial increase in excimeric emission. A good selectivity for dicarboxylic acid was also found. In the search for higher sensitivities, we moved our attention also to systems able to perform amplification effects. In this context we described the metal ion binding properties of three photoactive poly-(arylene ethynylene) co-polymers with different complexing units and we highlighted, for one of them, a ten-fold amplification of the response in case of addition of Zn2+, Cu2+ and Hg2+ ions. In addition, we were able to demonstrate the formation of complexes with Yb3+ an Er3+ and an efficient sensitization of their typical metal centered NIR emission upon excitation of the polymer structure, this feature being of particular interest for their possible applications in optical imaging and in optical amplification for telecommunication purposes. An amplification effect was also observed during this research in silica nanoparticles derivatized with a suitable zinc probe. In this case we were able to prove, for the first time, that nanoparticles can work as “off-on” chemosensors with signal amplification. Fluorescent silica nanoparticles can be thus seen as innovative multicomponent systems in which the organization of photophysically active units gives rise to fruitful collective effects. These precious effects can be exploited for biological imaging, medical diagnostic and therapeutics, as evidenced also by some results reported in this thesis. In particular, the observed amplification effect has been obtained thanks to a suitable organization of molecular probe units onto the surface of the nanoparticles. In the effort of reaching a deeper inside in the mechanisms which lead to the final amplification effects, we also attempted to find a correlation between the synthetic route and the final organization of the active molecules in the silica network, and thus with those mutual interactions between one another which result in the emerging, collective behavior, responsible for the desired signal amplification. In this context, we firstly investigated the process of formation of silica nanoparticles doped with pyrene derivative and we showed that the dyes are not uniformly dispersed inside the silica matrix; thus, core-shell structures can be formed spontaneously in a one step synthesis. Moreover, as far as the design of new labels is concerned, we reported a new synthetic approach to obtain a class of robust, biocompatible silica core-shell nanoparticles able to show a long-term stability. Taking advantage of this new approach we also showed the synthesis and photophysical properties of core-shell NIR absorbing and emitting materials that proved to be very valuable for in-vivo imaging. In general, the dye doped silica nanoparticles prepared in the framework of this project can conjugate unique properties, such as a very high brightness, due to the possibility to include many fluorophores per nanoparticle, high stability, because of the shielding effect of the silica matrix, and, to date, no toxicity, with a simple and low-cost preparation. All these features make these nanostructures suitable to reach the low detection limits that are nowadays required for effective clinical and environmental applications, fulfilling in this way the initial expectations of this research project.