Wireless Sensor Networks for Monitoring Applications

Wireless Sensor Networks (WSNs) are getting wide-spread attention since they became easily accessible with their low costs. One of the key elements of WSNs is distributed sensing. When the precise location of a signal of interest is unknown across the monitored region, distributing many sensors randomly/uniformly may yield with a better representation of the monitored random process than a traditional sensor deployment. In a typical WSN application the data sensed by nodes is usually sent to one (or more) central device, denoted as sink, which collects the information and can either act as a gateway towards other networks (e.g. Internet), where data can be stored, or be processed in order to command the actuators to perform special tasks. In such a scenario, a dense sensor deployment may create bottlenecks when many nodes competing to access the channel. Even though there are mitigation methods on the channel access, concurrent (parallel) transmissions may occur. In this study, always on the scope of monitoring applications, the involved development progress of two industrial projects with dense sensor deployments (eDIANA Project funded by European Commission and Centrale Adritica Project funded by Coop Italy) and the measurement results coming from several different test-beds evoked the necessity of a mathematical analysis on concurrent transmissions. To the best of our knowledge, in the literature there is no mathematical analysis of concurrent transmission in 2.4 GHz PHY of IEEE 802.15.4. In the thesis, experience stories of eDIANA and Centrale Adriatica Projects and a mathematical analysis of concurrent transmissions starting from O-QPSK chip demodulation to the packet reception rate with several different types of theoretical demodulators, are presented. There is a very good agreement between the measurements so far in the literature and the mathematical analysis.

Sparse Signal Representation of Ultrasonic Signals for Structural Health Monitoring Applications

Assessment of the integrity of structural components is of great importance for aerospace systems, land and marine transportation, civil infrastructures and other biological and mechanical applications. Guided waves (GWs) based inspections are an attractive mean for structural health monitoring. In this thesis, the study and development of techniques for GW ultrasound signal analysis and compression in the context of non-destructive testing of structures will be presented. In guided wave inspections, it is necessary to address the problem of the dispersion compensation. A signal processing approach based on frequency warping was adopted. Such operator maps the frequencies axis through a function derived by the group velocity of the test material and it is used to remove the dependence on the travelled distance from the acquired signals. Such processing strategy was fruitfully applied for impact location and damage localization tasks in composite and aluminum panels. It has been shown that, basing on this processing tool, low power embedded system for GW structural monitoring can be implemented. Finally, a new procedure based on Compressive Sensing has been developed and applied for data reduction. Such procedure has also a beneficial effect in enhancing the accuracy of structural defects localization. This algorithm uses the convolutive model of the propagation of ultrasonic guided waves which takes advantage of a sparse signal representation in the warped frequency domain. The recovery from the compressed samples is based on an alternating minimization procedure which achieves both an accurate reconstruction of the ultrasonic signal and a precise estimation of waves time of flight. Such information is used to feed hyperbolic or elliptic localization procedures, for accurate impact or damage localization.

Enabling computationally intensive bioinformatics applications on the Grid platform

Bioinformatics is a recent and emerging discipline which aims at studying biological problems through computational approaches. Most branches of bioinformatics such as Genomics, Proteomics and Molecular Dynamics are particularly computationally intensive, requiring huge amount of computational resources for running algorithms of everincreasing complexity over data of everincreasing size. In the search for computational power, the EGEE Grid platform, world's largest community of interconnected clusters load balanced as a whole, seems particularly promising and is considered the new hope for satisfying the everincreasing computational requirements of bioinformatics, as well as physics and other computational sciences. The EGEE platform, however, is rather new and not yet free of problems. In addition, specific requirements of bioinformatics need to be addressed in order to use this new platform effectively for bioinformatics tasks. In my three years' Ph.D. work I addressed numerous aspects of this Grid platform, with particular attention to those needed by the bioinformatics domain. I hence created three major frameworks, Vnas, GridDBManager and SETest, plus an additional smaller standalone solution, to enhance the support for bioinformatics applications in the Grid environment and to reduce the effort needed to create new applications, additionally addressing numerous existing Grid issues and performing a series of optimizations. The Vnas framework is an advanced system for the submission and monitoring of Grid jobs that provides an abstraction with reliability over the Grid platform. In addition, Vnas greatly simplifies the development of new Grid applications by providing a callback system to simplify the creation of arbitrarily complex multistage computational pipelines and provides an abstracted virtual sandbox which bypasses Grid limitations. Vnas also reduces the usage of Grid bandwidth and storage resources by transparently detecting equality of virtual sandbox files based on content, across different submissions, even when performed by different users. BGBlast, evolution of the earlier project GridBlast, now provides a Grid Database Manager (GridDBManager) component for managing and automatically updating biological flatfile databases in the Grid environment. GridDBManager sports very novel features such as an adaptive replication algorithm that constantly optimizes the number of replicas of the managed databases in the Grid environment, balancing between response times (performances) and storage costs according to a programmed cost formula. GridDBManager also provides a very optimized automated management for older versions of the databases based on reverse delta files, which reduces the storage costs required to keep such older versions available in the Grid environment by two orders of magnitude. The SETest framework provides a way to the user to test and regressiontest Python applications completely scattered with side effects (this is a common case with Grid computational pipelines), which could not easily be tested using the more standard methods of unit testing or test cases. The technique is based on a new concept of datasets containing invocations and results of filtered calls. The framework hence significantly accelerates the development of new applications and computational pipelines for the Grid environment, and the efforts required for maintenance. An analysis of the impact of these solutions will be provided in this thesis. This Ph.D. work originated various publications in journals and conference proceedings as reported in the Appendix. Also, I orally presented my work at numerous international conferences related to Grid and bioinformatics.

Design of wireless sensor networks for fluid dynamic applications

In fluid dynamics research, pressure measurements are of great importance to define the flow field acting on aerodynamic surfaces. In fact the experimental approach is fundamental to avoid the complexity of the mathematical models for predicting the fluid phenomena. It’s important to note that, using in-situ sensor to monitor pressure on large domains with highly unsteady flows, several problems are encountered working with the classical techniques due to the transducer cost, the intrusiveness, the time response and the operating range. An interesting approach for satisfying the previously reported sensor requirements is to implement a sensor network capable of acquiring pressure data on aerodynamic surface using a wireless communication system able to collect the pressure data with the lowest environmental–invasion level possible. In this thesis a wireless sensor network for fluid fields pressure has been designed, built and tested. To develop the system, a capacitive pressure sensor, based on polymeric membrane, and read out circuitry, based on microcontroller, have been designed, built and tested. The wireless communication has been performed using the Zensys Z-WAVE platform, and network and data management have been implemented. Finally, the full embedded system with antenna has been created. As a proof of concept, the monitoring of pressure on the top of the mainsail in a sailboat has been chosen as working example.

In situ real-time investigation of Organic Ultra-Thin-Film transistors: growth, electrical properties and biosensing applications

Organic electronics has grown enormously during the last decades driven by the encouraging results and the potentiality of these materials for allowing innovative applications, such as flexible-large-area displays, low-cost printable circuits, plastic solar cells and lab-on-a-chip devices. Moreover, their possible field of applications reaches from medicine, biotechnology, process control and environmental monitoring to defense and security requirements. However, a large number of questions regarding the mechanism of device operation remain unanswered. Along the most significant is the charge carrier transport in organic semiconductors, which is not yet well understood. Other example is the correlation between the morphology and the electrical response. Even if it is recognized that growth mode plays a crucial role into the performance of devices, it has not been exhaustively investigated. The main goal of this thesis was the finding of a correlation between growth modes, electrical properties and morphology in organic thin-film transistors (OTFTs). In order to study the thickness dependence of electrical performance in organic ultra-thin-film transistors, we have designed and developed a home-built experimental setup for performing real-time electrical monitoring and post-growth in situ electrical characterization techniques. We have grown pentacene TFTs under high vacuum conditions, varying systematically the deposition rate at a fixed room temperature. The drain source current IDS and the gate source current IGS were monitored in real-time; while a complete post-growth in situ electrical characterization was carried out. At the end, an ex situ morphological investigation was performed by using the atomic force microscope (AFM). In this work, we present the correlation for pentacene TFTs between growth conditions, Debye length and morphology (through the correlation length parameter). We have demonstrated that there is a layered charge carriers distribution, which is strongly dependent of the growth mode (i.e. rate deposition for a fixed temperature), leading to a variation of the conduction channel from 2 to 7 monolayers (MLs). We conciliate earlier reported results that were apparently contradictory. Our results made evident the necessity of reconsidering the concept of Debye length in a layered low-dimensional device. Additionally, we introduce by the first time a breakthrough technique. This technique makes evident the percolation of the first MLs on pentacene TFTs by monitoring the IGS in real-time, correlating morphological phenomena with the device electrical response. The present thesis is organized in the following five chapters. Chapter 1 makes an introduction to the organic electronics, illustrating the operation principle of TFTs. Chapter 2 presents the organic growth from theoretical and experimental points of view. The second part of this chapter presents the electrical characterization of OTFTs and the typical performance of pentacene devices is shown. In addition, we introduce a correcting technique for the reconstruction of measurements hampered by leakage current. In chapter 3, we describe in details the design and operation of our innovative home-built experimental setup for performing real-time and in situ electrical measurements. Some preliminary results and the breakthrough technique for correlating morphological and electrical changes are presented. Chapter 4 meets the most important results obtained in real-time and in situ conditions, which correlate growth conditions, electrical properties and morphology of pentacene TFTs. In chapter 5 we describe applicative experiments where the electrical performance of pentacene TFTs has been investigated in ambient conditions, in contact to water or aqueous solutions and, finally, in the detection of DNA concentration as label-free sensor, within the biosensing framework.

Modeling, design and experimental characterization of Micro-Electro-Mechanical-Systems for gas chromatographic applications

Design parameters, process flows, electro-thermal-fluidic simulations and experimental characterizations of Micro-Electro-Mechanical-Systems (MEMS) suited for gas-chromatographic (GC) applications are presented and thoroughly described in this thesis, whose topic belongs to the research activities the Institute for Microelectronics and Microsystems (IMM)-Bologna is involved since several years, i.e. the development of micro-systems for chemical analysis, based on silicon micro-machining techniques and able to perform analysis of complex gaseous mixtures, especially in the field of environmental monitoring. In this regard, attention has been focused on the development of micro-fabricated devices to be employed in a portable mini-GC system for the analysis of aromatic Volatile Organic Compounds (VOC) like Benzene, Toluene, Ethyl-benzene and Xylene (BTEX), i.e. chemical compounds which can significantly affect environment and human health because of their demonstrated carcinogenicity (benzene) or toxicity (toluene, xylene) even at parts per billion (ppb) concentrations. The most significant results achieved through the laboratory functional characterization of the mini-GC system have been reported, together with in-field analysis results carried out in a station of the Bologna air monitoring network and compared with those provided by a commercial GC system. The development of more advanced prototypes of micro-fabricated devices specifically suited for FAST-GC have been also presented (silicon capillary columns, Ultra-Low-Power (ULP) Metal OXide (MOX) sensor, Thermal Conductivity Detector (TCD)), together with the technological processes for their fabrication. The experimentally demonstrated very high sensitivity of ULP-MOX sensors to VOCs, coupled with the extremely low power consumption, makes the developed ULP-MOX sensor the most performing metal oxide sensor reported up to now in literature, while preliminary test results proved that the developed silicon capillary columns are capable of performances comparable to those of the best fused silica capillary columns. Finally, the development and the validation of a coupled electro-thermal Finite Element Model suited for both steady-state and transient analysis of the micro-devices has been described, and subsequently implemented with a fluidic part to investigate devices behaviour in presence of a gas flowing with certain volumetric flow rates.

Perfluoroalkylated substances in food matrices: development of mass spectrometry based analytical methods and preliminary monitoring

Perfluoroalkylated substances are a group of chemicals that have been largely employed during the last 60 years in several applications, widely spreading and accumulating in the environment due to their extreme resistance to degradation. As a consequence, they have been found also in various types of food as well as in drinking water, proving that they can easily reach humans through the diet. The available information concerning their adverse effects on health has recently increased the interest towards these contaminants and highlighted the importance of investigating all the potential sources of human exposure, among which diet was proved to be the most relevant. This need has been underlined by the European Union through Recommendation 2010/161/EU: in this document, Member States were called to monitor their presence of in food, producing accurate estimations of human exposure. The purpose of the research presented in this thesis, which is the result of a partnership between an Italian and a French laboratory, was to develop reliable tools for the analysis of these pollutants in food, to be used for generating data on potentially contaminated matrices. An efficient method based on liquid chromatography-mass spectrometry for the detection of 16 different perfluorinated compounds in milk has been validated in accordance with current European regulation guidelines (2002/657/EC) and is currently under evaluation for ISO 17025 accreditation. The proposed technique was applied to cow, powder and human breast milk samples from Italy and France to produce a preliminary monitoring on the presence of these contaminants. In accordance with the above mentioned European Recommendation, this project led also to the development of a promising technique for the quantification of some precursors of these substances in fish. This method showed extremely satisfying performances in terms of linearity and limits of detection, and will be useful for future surveys.

Mental states monitoring through passive Brain-Computer Interface systems

The monitoring of cognitive functions aims at gaining information about the current cognitive state of the user by decoding brain signals. In recent years, this approach allowed to acquire valuable information about the cognitive aspects regarding the interaction of humans with external world. From this consideration, researchers started to consider passive application of brain–computer interface (BCI) in order to provide a novel input modality for technical systems solely based on brain activity. The objective of this thesis is to demonstrate how the passive Brain Computer Interfaces (BCIs) applications can be used to assess the mental states of the users, in order to improve the human machine interaction. Two main studies has been proposed. The first one allows to investigate whatever the Event Related Potentials (ERPs) morphological variations can be used to predict the users’ mental states (e.g. attentional resources, mental workload) during different reactive BCI tasks (e.g. P300-based BCIs), and if these information can predict the subjects’ performance in performing the tasks. In the second study, a passive BCI system able to online estimate the mental workload of the user by relying on the combination of the EEG and the ECG biosignals has been proposed. The latter study has been performed by simulating an operative scenario, in which the occurrence of errors or lack of performance could have significant consequences. The results showed that the proposed system is able to estimate online the mental workload of the subjects discriminating three different difficulty level of the tasks ensuring a high reliability.

Behaviour and applications of elastic waves in structures and metamaterials

The present thesis focuses on elastic waves behaviour in ordinary structures as well as in acousto-elastic metamaterials via numerical and experimental applications. After a brief introduction on the behaviour of elastic guided waves in the framework of non-destructive evaluation (NDE) and structural health monitoring (SHM) and on the study of elastic waves propagation in acousto-elastic metamaterials, dispersion curves for thin-walled beams and arbitrary cross-section waveguides are extracted via Semi-Analytical Finite Element (SAFE) methods. Thus, a novel strategy tackling signal dispersion to locate defects in irregular waveguides is proposed and numerically validated. Finally, a time-reversal and laser-vibrometry based procedure for impact location is numerically and experimentally tested. In the second part, an introduction and a brief review of the basic definitions necessary to describe acousto-elastic metamaterials is provided. A numerical approach to extract dispersion properties in such structures is highlighted. Afterwards, solid-solid and solid-fluid phononic systems are discussed via numerical applications. In particular, band structures and transmission power spectra are predicted for 1P-2D, 2P-2D and 2P-3D phononic systems. In addition, attenuation bands in the ultrasonic as well as in the sonic frequency regimes are experimentally investigated. In the experimental validation, PZTs in a pitch-catch configuration and laser vibrometric measurements are performed on a PVC phononic plate in the ultrasonic frequency range and sound insulation index is computed for a 2P-3D phononic barrier in the sonic frequency range. In both cases the numerical-experimental results comparison confirms the existence of the numerical predicted band-gaps. Finally, the feasibility of an innovative passive isolation strategy based on giant elastic metamaterials is numerically proved to be practical for civil structures. In particular, attenuation of seismic waves is demonstrated via finite elements analyses. Further, a parametric study shows that depending on the soil properties, such an earthquake-proof barrier could lead to significant reduction of the superstructure displacement.

Ultra-Low Power and Non-intrusive Wireless Monitoring for Smart Buildings

Wireless Sensor Networks (WSNs) offer a new solution for distributed monitoring, processing and communication. First of all, the stringent energy constraints to which sensing nodes are typically subjected. WSNs are often battery powered and placed where it is not possible to recharge or replace batteries. Energy can be harvested from the external environment but it is a limited resource that must be used efficiently. Energy efficiency is a key requirement for a credible WSNs design. From the power source's perspective, aggressive energy management techniques remain the most effective way to prolong the lifetime of a WSN. A new adaptive algorithm will be presented, which minimizes the consumption of wireless sensor nodes in sleep mode, when the power source has to be regulated using DC-DC converters. Another important aspect addressed is the time synchronisation in WSNs. WSNs are used for real-world applications where physical time plays an important role. An innovative low-overhead synchronisation approach will be presented, based on a Temperature Compensation Algorithm (TCA). The last aspect addressed is related to self-powered WSNs with Energy Harvesting (EH) solutions. Wireless sensor nodes with EH require some form of energy storage, which enables systems to continue operating during periods of insufficient environmental energy. However, the size of the energy storage strongly restricts the use of WSNs with EH in real-world applications. A new approach will be presented, which enables computation to be sustained during intermittent power supply. The discussed approaches will be used for real-world WSN applications. The first presented scenario is related to the experience gathered during an European Project (3ENCULT Project), regarding the design and implementation of an innovative network for monitoring heritage buildings. The second scenario is related to the experience with Telecom Italia, regarding the design of smart energy meters for monitoring the usage of household's appliances.