Array communications in wireless sensor networks

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Sensors and Systems for Indoor Positioning

This reprint is a reprint of the articles that appeared in Sensors' (MDPI) Special Issue on “Sensors and Systems for Indoor Positioning". The published original contributions focused on systems and technologies to enable indoor applications

A Hybrid Localization Approach in Wireless Sensor Networks by Resolving Flip Ambiguity

Localization has received considerable attention because many wireless sensor network applications require accurate knowledge of the locations of the sensors in the network. In the process the location calculation is achieved by either distance measurements or angle-of‐arrival measurement. However, the former technique suffers from flip ambiguity due to either the presence of insufficient reference points or uncertainties in the inter‐nodal distance measurements in a triangular network structure. A recently proposed quadrilateral structure (an extended complex version of a trilateration structure) can resolve flip ambiguity of a node in dense deployments under restricted orientations for anchors. However, the technique leaves open issues to consider imprecise inter‐nodal distances between all pairs of nodes as complexity increases due to measurement uncertainties in determining the locations. Moreover, both the structures (trilateral and quadrilateral) completely fail to resolve flip ambiguity in sparse node deployments as sufficient nodes are not available in order to determine the signs in calculated angles. On the other hand, AOA can provide the sign of the angles but requires expensive hardware calibration to provide a high‐level of accuracy in the measured angles. Therefore, there is a need of a localization technique that is cheaper, less complex, and robust by considering measurement uncertainties between all pair of nodes and more importantly, involves fewer reference nodes. The primary contributions of this thesis include a hybrid technique that uses low‐accuracy (cheap) AOA measurements along with erroneous distance measurements between each pair of nodes in a much simpler triangular network that corresponds to a sparse deployment. In our initial phase we develop mathematical models involving only two reference nodes that are able to resolve flip ambiguity a unknown node with a high probability of success even with an RMS error as high as 150 in the line‐of‐bearing estimate, which avoids the need for calibration in many practical situations. In later phases, we modelled our hybrid localization technique to accommodate imprecise inter‐nodal measurements between all pairs of nodes. In the final phase, we intend our localization technique to solve ambiguity in extremely sparse scenarios with non‐closed network structure that are yet to be solved by existing localizations approaches. Resolution of flip ambiguity is useful, not only to develop lower‐complexity localization techniques, but also for many lower‐layer network functionalities such as geographic routing, topology control, coverage and tracking, and controlled mobility when a large number of these nodes have to be deployed

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)

Applications of compressive sensing to direction of arrival estimation

Die Schätzung der Einfallsrichtungen (Directions of Arrival/DOA) mehrerer ebener Wellenfronten mit Hilfe eines Antennen-Arrays ist eine der prominentesten Fragestellungen im Gebiet der Array-Signalverarbeitung. Das nach wie vor starke Forschungsinteresse in dieser Richtung konzentriert sich vor allem auf die Reduktion des Hardware-Aufwands, im Sinne der Komplexität und des Energieverbrauchs der Empfänger, bei einem vorgegebenen Grad an Genauigkeit und Robustheit gegen Mehrwegeausbreitung. Diese Dissertation beschäftigt sich mit der Anwendung von Compressive Sensing (CS) auf das Gebiet der DOA-Schätzung mit dem Ziel, hiermit die Komplexität der Empfängerhardware zu reduzieren und gleichzeitig eine hohe Richtungsauflösung und Robustheit zu erreichen. CS wurde bereits auf das DOA-Problem angewandt unter der Ausnutzung der Tatsache, dass eine Superposition ebener Wellenfronten mit einer winkelabhängigen Leistungsdichte korrespondiert, die über den Winkel betrachtet sparse ist. Basierend auf der Idee wurden CS-basierte Algorithmen zur DOA-Schätzung vorgeschlagen, die sich durch eine geringe Rechenkomplexität, Robustheit gegenüber Quellenkorrelation und Flexibilität bezüglich der Wahl der Array-Geometrie auszeichnen. Die Anwendung von CS führt darüber hinaus zu einer erheblichen Reduktion der Hardware-Komplexität, da weniger Empfangskanäle benötigt werden und eine geringere Datenmenge zu verarbeiten und zu speichern ist, ohne dabei wesentliche Informationen zu verlieren. Im ersten Teil der Arbeit wird das Problem des Modellfehlers bei der CS-basierten DOA-Schätzung mit gitterbehafteten Verfahren untersucht. Ein häufig verwendeter Ansatz um das CS-Framework auf das DOA-Problem anzuwenden ist es, den kontinuierlichen Winkel-Parameter zu diskreditieren und damit ein Dictionary endlicher Größe zu bilden. Da die tatsächlichen Winkel fast sicher nicht auf diesem Gitter liegen werden, entsteht dabei ein unvermeidlicher Modellfehler, der sich auf die Schätzalgorithmen auswirkt. In der Arbeit wird ein analytischer Ansatz gewählt, um den Effekt der Gitterfehler auf die rekonstruierten Spektra zu untersuchen. Es wird gezeigt, dass sich die Messung einer Quelle aus beliebiger Richtung sehr gut durch die erwarteten Antworten ihrer beiden Nachbarn auf dem Gitter annähern lässt. Darauf basierend wird ein einfaches und effizientes Verfahren vorgeschlagen, den Gitterversatz zu schätzen. Dieser Ansatz ist anwendbar auf einzelne Quellen oder mehrere, räumlich gut separierte Quellen. Für den Fall mehrerer dicht benachbarter Quellen wird ein numerischer Ansatz zur gemeinsamen Schätzung des Gitterversatzes diskutiert. Im zweiten Teil der Arbeit untersuchen wir das Design kompressiver Antennenarrays für die DOA-Schätzung. Die Kompression im Sinne von Linearkombinationen der Antennensignale, erlaubt es, Arrays mit großer Apertur zu entwerfen, die nur wenige Empfangskanäle benötigen und sich konfigurieren lassen. In der Arbeit wird eine einfache Empfangsarchitektur vorgeschlagen und ein allgemeines Systemmodell diskutiert, welches verschiedene Optionen der tatsächlichen Hardware-Realisierung dieser Linearkombinationen zulässt. Im Anschluss wird das Design der Gewichte des analogen Kombinations-Netzwerks untersucht. Numerische Simulationen zeigen die Überlegenheit der vorgeschlagenen kompressiven Antennen-Arrays im Vergleich mit dünn besetzten Arrays der gleichen Komplexität sowie kompressiver Arrays mit zufällig gewählten Gewichten. Schließlich werden zwei weitere Anwendungen der vorgeschlagenen Ansätze diskutiert: CS-basierte Verzögerungsschätzung und kompressives Channel Sounding. Es wird demonstriert, dass die in beiden Gebieten durch die Anwendung der vorgeschlagenen Ansätze erhebliche Verbesserungen erzielt werden können.Direction of Arrival (DOA) estimation of plane waves impinging on an array of sensors is one of the most important tasks in array signal processing, which have attracted tremendous research interest over the past several decades. The estimated DOAs are used in various applications like localization of transmitting sources, massive MIMO and 5G Networks, tracking and surveillance in radar, and many others. The major objective in DOA estimation is to develop approaches that allow to reduce the hardware complexity in terms of receiver costs and power consumption, while providing a desired level of estimation accuracy and robustness in the presence of multiple sources and/or multiple paths. Compressive sensing (CS) is a novel sampling methodology merging signal acquisition and compression. It allows for sampling a signal with a rate below the conventional Nyquist bound. In essence, it has been shown that signals can be acquired at sub-Nyquist sampling rates without loss of information provided they possess a sufficiently sparse representation in some domain and that the measurement strategy is suitably chosen. CS has been recently applied to DOA estimation, leveraging the fact that a superposition of planar wavefronts corresponds to a sparse angular power spectrum. This dissertation investigates the application of compressive sensing to the DOA estimation problem with the goal to reduce the hardware complexity and/or achieve a high resolution and a high level of robustness. Many CS-based DOA estimation algorithms have been proposed in recent years showing tremendous advantages with respect to the complexity of the numerical solution while being insensitive to source correlation and allowing arbitrary array geometries. Moreover, CS has also been suggested to be applied in the spatial domain with the main goal to reduce the complexity of the measurement process by using fewer RF chains and storing less measured data without the loss of any significant information. In the first part of the work we investigate the model mismatch problem for CS based DOA estimation algorithms off the grid. To apply the CS framework a very common approach is to construct a finite dictionary by sampling the angular domain with a predefined sampling grid. Therefore, the target locations are almost surely not located exactly on a subset of these grid points. This leads to a model mismatch which deteriorates the performance of the estimators. We take an analytical approach to investigate the effect of such grid offsets on the recovered spectra showing that each off-grid source can be well approximated by the two neighboring points on the grid. We propose a simple and efficient scheme to estimate the grid offset for a single source or multiple well-separated sources. We also discuss a numerical procedure for the joint estimation of the grid offsets of closer sources. In the second part of the thesis we study the design of compressive antenna arrays for DOA estimation that aim to provide a larger aperture with a reduced hardware complexity and allowing reconfigurability, by a linear combination of the antenna outputs to a lower number of receiver channels. We present a basic receiver architecture of such a compressive array and introduce a generic system model that includes different options for the hardware implementation. We then discuss the design of the analog combining network that performs the receiver channel reduction. Our numerical simulations demonstrate the superiority of the proposed optimized compressive arrays compared to the sparse arrays of the same complexity and to compressive arrays with randomly chosen combining kernels. Finally, we consider two other applications of the sparse recovery and compressive arrays. The first application is CS based time delay estimation and the other one is compressive channel sounding. We show that the proposed approaches for sparse recovery off the grid and compressive arrays show significant improvements in the considered applications compared to conventional methods

Analysis of the IEEE 802.15.4a ultra wideband physical layer through wireless sensor network simulations in OMNET++

Wireless Sensor Networks are the main representative of pervasive computing in large-scale physical environments. These networks consist of a large number of small, wireless devices embedded in the physical world to be used for surveillance, environmental monitoring or other data capture, processing and transfer applications. Ultra wideband has emerged as one of the newest and most promising concepts for wireless technology. Considering all its advantages it seems a likely communication technology candidate for future wireless sensor networks. This paper considers the viability of ultra wideband technology in wireless sensor networks by employing an IEEE 802.15.4a low-rate ultra wideband physical layer model in the OMNET++ simulation environment. An elaborate investigation into the inner workings of the IEEE 802.15.4a UWB physical layer is performed. Simulation experiments are used to provide a detailed analysis of the performance of the IEEE 802.15.4a UWB physical layer over several communication distances. A proposal for a cognitive, adaptive communication approach to optimize for speed and distance is also presented. AFRIKAANS : Draadlose Sensor Netwerke is die hoof verteenwoordiger vir deurdringende rekenarisering in groot skaal fisiese omgewings. Hierdie tipe netwerke bestaan uit ’n groot aantal klein, draadlose apparate wat in die fisiese wêreld ingesluit word vir die doel van bewaking, omgewings monitering en vele ander data opvang, verwerk en oordrag applikasies. Ultra wyeband het opgestaan as een van die nuutste en mees belowend konsepte vir draadlose kommunikasie tegnologie. As al die voordele van dié kommunikasie tegnologie in ag geneem word, blyk dit om ’n baie goeie kandidaat te wees vir gebruik in toekomstige draadlose sensor netwerke. Hierdie verhandeling oorweeg die vatbaarheid van die gebruik van die ultra wyeband tegnologie in draadlose sensor netwerke deur ’n IEEE 802.15.4a lae-tempo ultra wyeband fisiese laag model in die OMNET++ simulasie omgewing toe te pas. ’n Breedvoerige ondersoek word geloots om die fyn binneste werking van die IEEE 802.15.4a UWB fisiese laag te verstaan. Simulasie eksperimente word gebruik om ’n meer gedetaileerde analiese omtrent die werkverrigting van die IEEE 802.15.4a UWB fisiese laag te verkry oor verskillende kommunikasie afstande. ’n Voorstel vir ’n omgewings bewuste, aanpasbare kommunikasie tegniek word bespreek met die doel om die spoed en afstand van kommunikasie te optimiseer.Dissertation (MEng)--University of Pretoria, 2011.Electrical, Electronic and Computer Engineeringunrestricte