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

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

Master of Science / Department of Electrical and Computer Engineering / Balasubramaniam Natarajan / Decentralized sensor networks are collections of individual local sensors that observe a common phenomenon, quantize their observations, and send this quantized information to a central processor (fusion center) which then makes a global decision about the phenomenon. Most of the existing literature in this field consider only the data fusion aspect of this problem, i.e., the statistical hypothesis testing and optimal combining of the information obtained by the local sensors. In this thesis, we look at both the data detection and the data fusion aspects of the decentralized sensor networks. By data detection, we refer to the communication problem of transmitting quantized information from the local sensors to the fusion center through a multiple access channel. This work first analyzes the data fusion problem in decentralized sensor network when the sensor observations are corrupted by additive white gaussian noise. We optimize both local decision rules and fusion rule for this case. After that, we consider same problem when the observations are corrupted by correlated gaussian noise. We propose a novel parallel genetic algorithm which simultaneously optimizes both the local decision and fusion rules and show that our algorithm matches the results from prior work with considerably less computational cost. We also demonstrate that, irrespective of the fusion rule, the system can provide equivalent performance with an appropriate choice of local decision rules. The second part of this work analyzes the data detection problem in distributed sensor networks. We characterize this problem as a multiple input multiple output (MIMO) system problem, where the local sensors represent the multiple input nodes and the fusion center(s) represent the output nodes. This set up, where the number of input nodes (sensors) is greater than the number of output nodes (fusion center(s)), is known as an overloaded array in MIMO terminology. We use a genetic algorithm to solve this overloaded array problem.

The main vision of wearable computing is to make electronic systems an important part of everyday clothing in the future which will serve as intelligent personal assistants. Wearable devices have the potential to be wearable computers and not mere input/output devices for the human body. The present thesis focuses on introducing a new wearable computing paradigm, where the processing elements are closely coupled with the sensors that are distributed using Instruction Systolic Array (ISA) architecture. The thesis describes a novel, multiple sensor, multiple processor system architecture prototype based on the Instruction Systolic Array paradigm for distributed computing on fabrics. The thesis introduces new programming model to implement the distributed computer on fabrics. The implementation of the concept has been validated using parallel algorithms. A real-time shape sensing and reconstruction application has been implemented on this architecture and has demonstrated a physical design for a wearable system based on the ISA concept constructed from off-the-shelf microcontrollers and sensors. Results demonstrate that the real time application executes on the prototype ISA implementation thus confirming the viability of the proposed architecture for fabric-resident computing devices.

Αντικείμενο αυτής της μεταπτυχιακής εργασίας είναι ο εντοπισμός της ύπαρξης μιας κυρίαρχης ευρείας ζώνης ισοτροπικής πηγής κι η εκτίμηση των συντεταγμένων θέσης αυτής, όταν αυτή βρίσκεται σ’ έναν τρισδιάστατο ή δισδιάστατο χώρο, ο οποίος εποπτεύεται και παρακολουθείται από ένα δίκτυο τυχαία διασκορπισμένων αισθητήρων. Οι κόμβοι του δικτύου μπορούν να περιέχουν ακουστικά, παλμικά κι άλλου είδους μικροηλεκτρομηχανολογικά στοιχεία αίσθησης του περιβάλλοντος. Κατά την αίσθηση ενός γεγονότος ενδιαφέροντος μπορούν να αυτοοργανωθούν σ’ ένα συγχρονισμένο ασύρματο ραδιοδίκτυο χρησιμοποιώντας χαμηλής κατανάλωσης πομποδέκτες spread spectrum, ώστε να επικοινωνούν μεταξύ τους και με τους κεντρικούς επεξεργαστές. Ο εντοπισμός της ύπαρξης μιας κυρίαρχης πηγής σ’ ένα δίκτυο αισθητήρων, με τα παραπάνω χαρακτηριστικά, επιτεύχθηκε με τη χρήση μιας τυφλής μεθόδου μορφοποίησης λοβού, γνωστή ως μέθοδος συλλογής της μέγιστης ισχύος. Η μέθοδος αυτή, η οποία υλοποιήθηκε στα πλαίσια αυτής της εργασίας, παρέχει τις εκτιμήσεις των σχετικών χρόνων καθυστέρησης άφιξης του σήματος της κυρίαρχης πηγής στους αισθητήρες του δικτύου ως προς έναν αισθητήρα αναφοράς. Κύριο αντικείμενο μελέτης αυτής της εργασίας είναι ο υπολογισμός του κυρίαρχου ιδιοδιανύσματος του δειγματοληπτημένου πίνακα αυτοσυσχέτισης. Αυτό επιτυγχάνεται στη βιβλιογραφία που μελετήθηκε είτε με χρήση της δυναμικής μεθόδου είτε με χρήση της μεθόδου ιδιοανάλυσης. Ανά στιγμιότυπο δειγμάτων απαιτείται η ανανέωση του πίνακα αυτοσυσχέτισης κι ο υπολογισμός του κυρίαρχου ιδιοδιανύσματος. Όμως, οι δύο παραπάνω μέθοδοι για τον υπολογισμό αυτό χρειάζονται αυξημένη πολυπλοκότητα μιας κι η διάσταση του πίνακα είναι αρκετά μεγάλη. Η συνεισφορά της εργασίας αυτής έγκειται στη μείωση αυτής της πολυπλοκότητας με τη χρήση μιας προσαρμοστικής μεθόδου υπολογισμού του κυρίαρχου ιδιοδιανύσματος. Τέλος, αντικείμενο της εργασίας αυτής είναι και το πρόβλημα εντοπισμού και παρακολούθησης των συντεταγμένων θέσης της κυρίαρχης πηγής από τις εκτιμήσεις των σχετικών χρόνων καθυστέρησης άφιξης. / Object of this postgraduate work are the detection of presence of an isotropic wideband dominant source and the estimate of its coordinates of placement (localization), when the source is found in a three or two dimensional space, which is supervised and watched by a randomly distributed sensor network. The nodes of the network may contain acoustical, vibrational and other MEM-sensing (Micro-Electro-Mechanical) elements. Upon sensing an event of interest, they can self-organize into a synchronized wireless radio network using low-power spread-spectrum transceivers to communicate among themselves and central processors. The detection of presence of a dominant source in a sensor network, with the above characteristics, was achieved with the use of a blind beamforming method, known as the maximum power collection method. This method, which was implemented in the context of this work, provides estimates of the relative time delays of arrival (relative TDEs - Time Delay Estimations) of the dominant source’s signal to the sensors of the network referenced to a reference sensor. The main object of study of the work is the calculation of the dominant eigenvector of the sampled correlation matrix. This is achieved, in the bibliography that was studied, either by using the power method or with use of the SVD method (Singular Value Decomposition). Per snapshot of samples it is required to update the autocorrelation matrix and to calculate the dominant eigenvector. However, the above two methods for this calculation have an increased complexity because the dimension of the matrix is high enough. The contribution of this work lies in the reduction of that complexity by using an adaptive method for the dominant eigenvector calculation. Finally, this work also focuses on the problem of localization and tracking of the coordinates of placement of the dominant source from the estimates of the relative time delays of arrival.

This research proposes a novel approach for improving autonomous mobile system navigation in dynamic and potentially occluded environments. The research introduces a tracking framework that combines data from stationary sensing units and on-board sensors, addressing challenges of computational efficiency, reliability, and scalability. The work innovates by integrating spatially-distributed LiDAR and RGB-D Camera sensors, with the optional inclusion of on-board IMU-based dead-reckoning, forming a robust and efficient coordination framework for autonomous systems. Two key developments are achieved. Firstly, a point cloud object detection technique, "Generalized L-Shape Fitting”, is advanced, enhancing bounding box fitting over point cloud data. Secondly, a new estimation framework, the Distributed Edge Node Switching Filter (DENS-F), is established. The DENS-F optimizes resource utilization and coordination, while minimizing reliance on on-board computation. Furthermore, it incorporates a short-term predictive feature, thanks to the Adaptive-Constant Acceleration motion model, which utilizes behaviour-based control inputs. The findings indicate that the DENS-F substantially improves accuracy and computational efficiency compared to the Kalman Consensus Filter (KCF), particularly when additional inertial data is provided by the vehicle. The type of sensor deployed and the consistency of the vehicle's path are also found to significantly influence the system's performance. The research opens new viewpoints for enhancing autonomous vehicle tracking, highlighting opportunities for future exploration in prediction models, sensor selection, and precision coordination. / Denna forskning föreslår en ny metod för att förbättra autonom mobil systemsnavigering i dynamiska och potentiellt skymda miljöer. Forskningen introducerar ett spårningsramverk som kombinerar data från stationära sensorenheter och ombordssensorer, vilket hanterar utmaningar med beräkningsefektivitet, tillförlitlighet och skalbarhet. Arbetet innoverar genom att integrera spatialt distribuerade LiDAR- och RGB-D-kamerasensorer, med det valfria tillägget av ombord IMU-baserad dödräkning, vilket skapar ett robust och efektivt samordningsramverk för autonoma system. Två nyckelutvecklingar uppnås. För det första avanceras en punktmolnsobjektdetekteringsteknik, “Generaliserad L-formig anpassning”, vilket förbättrar anpassning av inneslutande rutor över punktmolnsdata. För det andra upprättas ett nytt uppskattningssystem, det distribuerade kantnodväxlingsfltret (DENSF). DENS-F optimerar resursanvändning och samordning, samtidigt som det minimerar beroendet av ombordberäkning. Vidare införlivar det en kortsiktig prediktiv funktion, tack vare den adaptiva konstanta accelerationsrörelsemodellen, som använder beteendebaserade styrentréer. Resultaten visar att DENS-F väsentligt förbättrar noggrannhet och beräknings-efektivitet jämfört med Kalman Consensus Filter (KCF), särskilt när ytterligare tröghetsdata tillhandahålls av fordonet. Den typ av sensor som används och fordonets färdvägs konsekvens påverkar också systemets prestanda avsevärt. Forskningen öppnar nya synvinklar för att förbättra spårning av autonoma fordon, och lyfter fram möjligheter för framtida utforskning inom förutsägelsemodeller, sensorval och precisionskoordinering. / Questa ricerca propone un nuovo approccio per migliorare la navigazione dei sistemi mobili autonomi in ambienti dinamici e potenzialmente ostruiti. La ricerca introduce un sistema di tracciamento che combina dati da unità di rilevazione stazionarie e sensori di bordo, afrontando le sfde dell’effcienza computazionale, dell’affdabilità e della scalabilità. Il lavoro innova integrando sensori LiDAR e telecamere RGB-D distribuiti nello spazio, con l’inclusione opzionale di una navigazione inerziale basata su IMU di bordo, formando un robusto ed effciente quadro di coordinamento per i sistemi autonomi. Vengono raggiunti due sviluppi chiave. In primo luogo, viene perfezionata una tecnica di rilevazione di oggetti a nuvola di punti, “Generalized L-Shape Fitting”, migliorando l’adattamento del riquadro di delimitazione sui dati della nuvola di punti. In secondo luogo, viene istituito un nuovo framework di stima, il Distributed Edge Node Switching Filter (DENS-F). Il DENS-F ottimizza l’utilizzo delle risorse e il coordinamento, riducendo al minimo la dipendenza dal calcolo di bordo. Inoltre, incorpora una caratteristica di previsione a breve termine, grazie al modello di movimento Adaptive-Constant Acceleration, che utilizza input di controllo basati sul comportamento del veicolo. I risultati indicano che il DENS-F migliora notevolmente l’accuratezza e l’effcienza computazionale rispetto al Kalman Consensus Filter (KCF), in particolare quando il veicolo fornisce dati inerziali aggiuntivi. Si scopre anche che il tipo di sensore impiegato e la coerenza del percorso del veicolo infuenzano signifcativamente le prestazioni del sistema. La ricerca apre nuovi punti di vista per migliorare il tracciamento dei veicoli autonomi, evidenziando opportunità per future esplorazioni nei modelli di previsione, nella selezione dei sensori e nel coordinamento di precisione.