Distributed sensing has been an attractive alternative to the traditional single-point sensing technology when measurement at multiple locations is required. Traditional distributed sensing methods based on silica optical fiber and electric coaxial cables have some limitations for specific applications, such as in smart textiles and wearable sensors. By adopting the fiber thermal drawing technique, we have designed and fabricated multi-material electrode-embedded polymer fibers with distributed sensing capabilities. Polymers sensitive to temperature and pressure have been incorporated into the fiber structure, and thin metal electrodes placed inside fiber by convergence drawing have enabled detection of local impedance change with electrical reflectometry. We have demonstrated that these fibers can detect temperature and pressure change with high spatial resolution. We have also explored the possibility of using polymer optical fiber in a Raman scattering based distributed temperature sensing system. Stokes and Anti-Stokes signals of a PMMA fiber illuminated by a 532 nm pulsed laser was recorded, and the ratio was used to indicate local temperature change. We have also developed a unique way to fabricate porous polymer by thermal drawing polymer materials with controlled water content in the polymer. The porous fibers were loaded with a fluorescent dye, and its release in tissue phantoms and murine tumors was observed. The work has broadened the scope of multi-material, multi-functional fiber and may shed light on the development of novel smart textile devices. / PHD / In recent years smart textiles and wearable gadgets have already changed the way we live. There has been increasing industrial interest to develop novel flexible, stretchable devices that can interact with human and the environment. Thermal drawing technique originally invented for manufacturing telecommunication optical fiber has been used by researchers to fabricate fibers with more functionality. In this work, we report the progress made on the fabrication of multi-material fiber. Soft polymer fibers capable of measuring temperature and pressure were designed and made by the thermal drawing technique. Submillimeter fibers with thin copper electrodes have shown potential to be readily embedded in a smart fabric to provide 1D information in one direction or woven into a 2D pattern for area monitoring. We have also explored another temperature measurement scheme using polymer optical fibers with a pulsed laser. Compared with the electronic fibers, it is less susceptible to electrical noise and more robust. Lastly, we have shown a unique way to generate porosity in thermally drawn polymer fibers. The elongated pores in the fibers come from water escaping the fiber during the fabrication process. The three aspects of the project expand the scope of multi-material, multi-functional fiber and can shed light on the future development of electronic textile devices.

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Large-scale sensor networks have become a reality due to recent developments in sensor node hardware and algorithms. Sensor networks can provide real-time information based on detection and tracking. This information cannot be reliable if little is known about the sensor coverage of the network, which can be defined as the total sensing range of the network due to contributions from each sensor node. Knowledge about coverage can also be useful in determining if there is any gap in coverage in the region of interest as well as improving the algorithm that determines the placement of nodes. Although coverage estimation is this thesis's central concern, other factors such as energy-efficiency and network lifespan that affect the network performance are investigated. Energy-efficiency and network lifespan depend on the communication model used for obtaining coverage information from each sensor node. This thesis proposes the use of B-splines for describing coverage efficiently. The properties of B-splines also enable communication models such as directed diffusion and hierarchical clustering to provide better performance as compared to a centralized scheme. Results obtained from simulation experiments indicate that hierarchical clustering and directed diffusion can be used effectively for coverage measurement. The hierarchical clustering model, however, exhibited some drawbacks such as a dependency on the routing scheme and poor node-failure recovery. / Master of Science

La surveillance des structures de génie civil, afin d'en estimer l'état de santé, est un enjeu majeur pour les maîtres d'ouvrages. Les systèmes de mesures réparties par fibre optique, composés d'un interrogateur connecté à une fibre optique intégrée dans un câble, permettent de mesurer le profil de déformation avec un pas de mesure centimétrique et une portée kilométrique. Ils sont donc adaptés aux structures présentant de grands linéaires ou de grandes surfaces. Mais, avant tout déploiement industriel, il est nécessaire d'en évaluer leurs performances. Du fait de la déformation par cisaillement du revêtement du câble, le profil de déformation mesuré le long de la fibre optique n'est pas strictement identique à celui du matériau environnant. Une méthode, basée sur la mise en œuvre d'essais et de simulations numériques, a été développée afin de caractériser les mécanismes de transfert d'effort du milieu hôte à la fibre optique à travers le revêtement du câble. Cette méthode a été appliquée pour déterminer la réponse mécanique d'un câble particulier noyé dans le béton. Les performances métrologiques d'un système de mesure donné ont été évaluées sur la base d'une analyse d'essais « du laboratoire au terrain ». Cette étude a permis de quantifier les différentes composantes d'incertitude et d'estimer les performances du système de mesure complet. Enfin, le câble, noyé dans le béton, ne peut être remplacé. La connaissance de l'impact du vieillissement sur la réponse mécanique du câble est donc primordiale. Une étude spécifique est menée dont le but est d'estimer la durabilité du câble face aux sollicitations chimique, thermique et mécanique correspondant à une application donnée / Structural Health Monitoring is a key factor in life-cycle management of civil structures. Truly distributed fiber optic sensors, composed by an optoelectronic device paired with an optical fiber in a cable, provide strain profiles over several kilometers with a centimeter resolution. They are thus able to provide relevant information on large structures. However, a preliminary performance assessment is required prior to any industrial application. Due to shear deformation of the cable's protective coating, strain measurements provided by the measuring system may differ from actual strains in the embedding medium. A methodology, based on mechanical tests and modelling, was thus developed to determine the relationship between measured/actual strains. It was applied to determine the mechanical response of a specific cable embedded in concrete. Performance assessment method was applied to a specific measuring system. Tests were carried out under laboratory conditions on the fiber optic cable, out of the concrete medium in a first stage, and then embedded in concrete structures. It enabled to evaluate its components and standard uncertainties. The cable could not be replaced after being embedded in concrete. It is necessary to evaluate the ageing effects on its mechanical properties to use it for a long term period. A specific study was carried out to determine the cable durability under chemical, thermal and mechanical solicitations

The nature and complexity of the security threats faced by our society in recent years have made it clear that a smart pervasive surveillance system constitutes the most effective cure, as it presents a conducive framework for seamless interaction between preventative capabilities and investigative protocols. Applications such as wild-life preserve monitoring, natural disaster warnings, and facility surveillance tend to be characterized by large and remote geographic areas, requiring large numbers of unattended sensor nodes to cover the volume-of-interest. Such large unattended sensor networks add new challenges as well as complicate the system management problem. Such challenges can be in the form of distributed operation with collaborative decision making, adaptive performance, and energy-aware strategies, to name a few. To meet the challenges of these mission-critical applications, the sensor system must exhibit capabilities such as heterogeneous and self-organized behaviour, data and information fusion, and collaborative resources control and management. Sensor Management (SM) refers to the process that plans and controls the use of the sensor nodes in a manner that synergistically maximizes the success rate of the whole system in achieving the goals of its mission in assessing the situation in a timely, reliable, and accurate fashion. Managing heterogeneous sensors involves making decisions and compromises regarding alternate sensing strategies under time and resource availability constraints. As a result, the performance of the collective sensors dictates the performance of the entire system. Consequently, there is a need for an intelligent Sensor Management Framework (SMF) to drive the system performance. SMF provides a control system to manage and coordinate the use of sensing resources in a manner that maximizes the system success rate in achieving its goals. An SMF must handle an overwhelming amount of information collected, and adapt to the highly dynamic environments, in addition to network and system limitations. This thesis proposes a resource-aware and intelligent SMF for managing pervasive sensor systems in surveillance context. The proposed SMF considerably improves the process of information acquisition by coordinating the sensing resources in order to gather the most reliable data from a dynamic scene while operating under energy constraints. The proposed SMF addresses both the operation of the coordination paradigm, as well as, the local and collaborative decision making strategies. A conceptual analysis of the SM problem in a layered structure is discussed to introduce an open and flexible design framework based on the service-oriented architecture to provide a modular, reusable, and extendable framework for the proposed SMF solution. A novel sensor management architecture, called Extended Hybrid Architecture for Sensor Management (E-HASM), is proposed. E-HASM combines the operation of the holonic, federated, and market-based architectures in a complementary manner. Moreover, a team-theoretic formulation of Belief-Desire-Intention (BDI), that represent the E-HASM components, is proposed as a mechanism for effective energy-aware decision making to address the local sensor utility. Also, intelligent schemes that provide adaptive sensor operation to the changes in environment dynamics and sensor energy levels are designed to include adaptive sleep, active sensing, dynamic sensing range, adaptive multimodality, and constrained communication. Furthermore, surveillance systems usually operate under uncertainty in stochastic environment. Therefore, this research formulates the collaborative decision-making entities as Partially Observable Markov Decision Processes (POMDP). To increase the tracking quality and the level of the information reliability, cooperation between the sensors is adopted, which adds an extra dimension in the design of the proposed SMFs. The propose SMF is implemented using the Jadex platform and is compared to the popular centralized architecture. The results illustrate the operation of the proposed SMF outperforms in terms of tracking quality, detection rate, energy consumption, network lifetime, and scalability.

The nature and complexity of the security threats faced by our society in recent years have made it clear that a smart pervasive surveillance system constitutes the most effective cure, as it presents a conducive framework for seamless interaction between preventative capabilities and investigative protocols. Applications such as wild-life preserve monitoring, natural disaster warnings, and facility surveillance tend to be characterized by large and remote geographic areas, requiring large numbers of unattended sensor nodes to cover the volume-of-interest. Such large unattended sensor networks add new challenges as well as complicate the system management problem. Such challenges can be in the form of distributed operation with collaborative decision making, adaptive performance, and energy-aware strategies, to name a few. To meet the challenges of these mission-critical applications, the sensor system must exhibit capabilities such as heterogeneous and self-organized behaviour, data and information fusion, and collaborative resources control and management. Sensor Management (SM) refers to the process that plans and controls the use of the sensor nodes in a manner that synergistically maximizes the success rate of the whole system in achieving the goals of its mission in assessing the situation in a timely, reliable, and accurate fashion. Managing heterogeneous sensors involves making decisions and compromises regarding alternate sensing strategies under time and resource availability constraints. As a result, the performance of the collective sensors dictates the performance of the entire system. Consequently, there is a need for an intelligent Sensor Management Framework (SMF) to drive the system performance. SMF provides a control system to manage and coordinate the use of sensing resources in a manner that maximizes the system success rate in achieving its goals. An SMF must handle an overwhelming amount of information collected, and adapt to the highly dynamic environments, in addition to network and system limitations. This thesis proposes a resource-aware and intelligent SMF for managing pervasive sensor systems in surveillance context. The proposed SMF considerably improves the process of information acquisition by coordinating the sensing resources in order to gather the most reliable data from a dynamic scene while operating under energy constraints. The proposed SMF addresses both the operation of the coordination paradigm, as well as, the local and collaborative decision making strategies. A conceptual analysis of the SM problem in a layered structure is discussed to introduce an open and flexible design framework based on the service-oriented architecture to provide a modular, reusable, and extendable framework for the proposed SMF solution. A novel sensor management architecture, called Extended Hybrid Architecture for Sensor Management (E-HASM), is proposed. E-HASM combines the operation of the holonic, federated, and market-based architectures in a complementary manner. Moreover, a team-theoretic formulation of Belief-Desire-Intention (BDI), that represent the E-HASM components, is proposed as a mechanism for effective energy-aware decision making to address the local sensor utility. Also, intelligent schemes that provide adaptive sensor operation to the changes in environment dynamics and sensor energy levels are designed to include adaptive sleep, active sensing, dynamic sensing range, adaptive multimodality, and constrained communication. Furthermore, surveillance systems usually operate under uncertainty in stochastic environment. Therefore, this research formulates the collaborative decision-making entities as Partially Observable Markov Decision Processes (POMDP). To increase the tracking quality and the level of the information reliability, cooperation between the sensors is adopted, which adds an extra dimension in the design of the proposed SMFs. The propose SMF is implemented using the Jadex platform and is compared to the popular centralized architecture. The results illustrate the operation of the proposed SMF outperforms in terms of tracking quality, detection rate, energy consumption, network lifetime, and scalability.

abstract: Fully distributed wireless sensor networks (WSNs) without fusion center have advantages such as scalability in network size and energy efficiency in communications. Each sensor shares its data only with neighbors and then achieves global consensus quantities by in-network processing. This dissertation considers robust distributed parameter estimation methods, seeking global consensus on parameters of adaptive learning algorithms and statistical quantities. Diffusion adaptation strategy with nonlinear transmission is proposed. The nonlinearity was motivated by the necessity for bounded transmit power, as sensors need to iteratively communicate each other energy-efficiently. Despite the nonlinearity, it is shown that the algorithm performs close to the linear case with the added advantage of power savings. This dissertation also discusses convergence properties of the algorithm in the mean and the mean-square sense. Often, average is used to measure central tendency of sensed data over a network. When there are outliers in the data, however, average can be highly biased. Alternative choices of robust metrics against outliers are median, mode, and trimmed mean. Quantiles generalize the median, and they also can be used for trimmed mean. Consensus-based distributed quantile estimation algorithm is proposed and applied for finding trimmed-mean, median, maximum or minimum values, and identification of outliers through simulation. It is shown that the estimated quantities are asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Step-size sequences with proper decay rates are also discussed for convergence analysis. Another measure of central tendency is a mode which represents the most probable value and also be robust to outliers and other contaminations in data. The proposed distributed mode estimation algorithm achieves a global mode by recursively shifting conditional mean of the measurement data until it converges to stationary points of estimated density function. It is also possible to estimate the mode by utilizing grid vector as well as kernel density estimator. The densities are estimated at each grid point, while the points are updated until they converge to a global mode. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2017

Accelerometers deployed within smart structures produce a wealth of vibration data that can be analyzed to infer information about the types of acceleration events that are occurring within the structure. In the case of monitored smart buildings, some of these acceleration events are linked to occupant behavior, such as walking, operating machinery, closing doors, etc. The identification and classification of such events has many potential applications within a smart structure or city. Understanding occupant patterns could be beneficial for operations, retail, or HVAC management, as it could be used to monitor occupancy flow with a relatively sparse sensor network. It may also have detrimental implications in terms of cybersecurity, where such information could be mined for malicious practices if unauthorized access to the data was obtained. This work presents methods for the detection and classification of vibration events in an experimental smart building, Goodwin Hall at Virginia Tech. Goodwin Hall's 200+ accelerometer network is used to gather acceleration data, from which vibration events are automatically detected and clustered. The presence of a vibration event is detected from a raw acceleration signal with an adaptive RMS threshold method. A feature vector is then created for each extracted event as areas under regions of the FFT of the event's acceleration signal. The feature vectors are then mapped into a low-dimensional space using principal component analysis, where they are clustered with various unsupervised algorithms. These processes have shown to be successful when gathering vibration events from a single-sensor setup, but pose challenges when expanded to a multi-sensor network. Because of this, expanded applications such as a semi-supervised classifier for events detected anywhere in the building are currently still under development. This semi-supervised process, combined with the known location of each sensor would allow inferences to be drawn about the frequency of different activity types in regions of the building not captured in the labeled data. Future work intends to address these multi-sensor challenges with adjustments to the algorithm process. / Master of Science / All objects experience vibrations when they are disturbed by some force. In the case of this work, the object is complex, a classroom building, but the principle still stands. When the building is disturbed by a force it will vibrate, even if the force is small, such as a person walking down a hallway or closing a door. The vibrations caused by these 'events' are unique to the type of event, that is, footstep vibrations will be different from door vibrations. These vibrations are observed with accelerometers, and the corresponding signal is used to determine what type of event caused the vibration. First, an event is automatically detected within the signal and separated from it. Second, characteristics unique to the signal are identified, a process known as 'feature extraction.' Finally, those features are used to distinguish the event from others and to identify what had caused it based on previous experimental data. The ability to detect these events and classify them introduces many interesting applications, including any that would stem from occupant detection, including improved security or operations, retail, or HVAC management. The methods here may also be applicable to other applications, such as monitoring bridges and machinery, or for developing cutting-edge smartphone applications with the accelerometer that is built in.

Recent advances in wireless communications along with developments in low-power circuit design and micro-electro mechanical systems (MEMS) have heralded the advent of compact and inexpensive wireless micro-sensor devices. A large network of such sensor nodes capable of communicating with each other provides significant new capabilities for automatically collecting and analyzing data from physical environments. A notable feature of these networks is that more nodes than are strictly necessary may be deployed to cover a given region. This permits the system to provide reliable information, tolerate many types of faults, and prolong the effective service time. Like most wireless systems, achieving low power consumption is a key consideration in the design of these networks. This thesis presents algorithms for managing power at the distributed system level, rather than just at the individual node level. These distributed algorithms allocate work based on user requests to the individual sensor nodes that comprise the network. The primary goal of the algorithms is to provide a robust and scalable approach for tasking nodes that prolongs the effective life of the network. Theoretical analysis and simulation results are presented to characterize the behavior of these algorithms. Results obtained from simulation experiments indicate that the algorithms can achieve a significant increase in the life of the network. In some cases this may be by an order of magnitude. The algorithms are also shown to ensure a good quality of sensor coverage while improving the network life. Finally, they are shown to be robust to faults and scale to large numbers of nodes. / Master of Science

A distributed sensor using an optical fiber for detecting and locating intruders over long perimeters (>10 km) is described. Phase changes resulting from either the pressure of the intruder on the ground immediately above the buried fiber or from seismic disturbances in the vicinity are sensed by a phase-sensitive optical time-domain reflectometer (φ−OTDR). Light pulses from a cw laser operating in a single longitudinal mode and with low (MHz/min range) frequency drift are injected into one end of the single mode fiber, and the backscattered light is monitored with a photodetector. In laboratory tests with 12 km of fiber on reels, the effects of localized phase perturbations induced by a piezoelectric fiber stretcher on φ−OTDR traces were characterized. In field tests in which the sensing element is a single mode fiber in a 3-mm diameter cable buried in an 8 to 18 inch deep, 4 inch wide trench in clay soil, detection of intruders on foot up to 15 ft from the cable line was achieved. In desert terrain field tests in which the sensing fiber is in a 4.5-mm diameter cable buried in a 1 ft deep, 2.5 ft wide trench filled with loose sand, high sensitivity and consistent detection of intruders on foot and of vehicles traveling down a road near the cable line was realized over a cable length of 8.5 km and a total fiber path of 19 km in real time. In a final series of field tests in clay soil, phase changes produced by the steps of a person walking up to 15 ft away from the buried cable were observed, and vehicles traveling at 10 mph were consistently detected up to 300 ft away. Based on these results, this technology may be regarded as a candidate for providing low-cost perimeter security for nuclear power plants, electrical power distribution centers, storage facilities for fuel and volatile chemicals, communication hubs, airports, government offices, military bases, embassies, and national borders.