Massive deployment of wireless IoT (Internet of Things) devices makes replacement or recharge of batteries expensive and impractical for some applications. Energy harvesting is a promising solution, and various designs are proposed to harvest power from ambient resources including thermal, vibrational, solar, wind, and RF sources. Among these ambient resources, AC powerlines are a stable energy source in an urban environment. Many researchers investigated methods to exploit this stable source of energy to power wireless IoT devices. The proposed circuit aims to harvest energy from AC powerlines with a wide input range of from 10 to 50 A. The proposed system includes a wake-up circuit and is capable of cold-start. A buck-boost converter operating in DCM is adopted for impedance matching, where the impedance is rather independent of the operation conditions. So, the proposed system can be applied to various types of wireless sensor nodes with different internal impedances. Experimental results show that the proposed system achieves an efficiency of 80.99% under the powerline current of 50 A. / M.S. / Nowadays, with the magnificent growth of IoT devices, a reliable, and efficient energy supply system becomes more and more important, because, for some applications, battery replacement is very expensive and sometimes even impossible. At this time, a well-designed self-contained energy harvesting system is a good solution. The energy harvesting system can extend the service life of the IoT devices and reduce the frequency of charging or checking the device. In this work, the proposed circuit aims to harvest energy from the AC power lines, and the harvested power intends to power wireless sensor nodes (WSNs). By utilizing the efficient and self-contained EH system, WSNs can be used to monitor the temperature, pressure, noise level and humidity etc. The proposed energy harvesting circuit was implemented with discrete components on a printed circuit board (PCB). Under a power line current of 50 A @ 50 Hz, the proposed energy harvesting circuit can harvest 156.6 mW, with a peak efficiency of 80.99 %.

Structural Health Monitoring (SHM) of automobile tires has been an active area of research in the last few years. Within this area, the monitoring of strain on tires using wireless devices and networks is gaining prominence because these techniques do not require any wired connections. Various tire manufacturers are looking into SHM of automobile tires due to the Transportation Recall Enhancement, Accountability and Documentation (TREAD) act which demands installation of tire pressure monitoring devices within the tire. Besides measuring tire pressures, tire manufactures are also examining ways to measure strain and temperature as well to enhance overall safety of an automobile. A sensor system that can measure the overall strain of a tire is known as a centralized strain sensing system. However, a centralized strain sensing system cannot find the location and severity of the damage on the tire, which is a basic requirement. Various sensors such as acceleration and optical sensors have also been proposed to be used together to get more local damage information on the tire. In this thesis we have developed a strain sensing system that performs local strain measurements on the tire and transmits them to a console inside the vehicle wirelessly. Our sensing system utilizes a new sensing material called Metal RubberTM which is shown to be conductive like metal, and flexible as rubber. Also, we have also developed a reliable and an energy efficient geographic routing protocol for transporting strain data wirelessly from a tire surface to the driver of the automobile. / Master of Science

Low-frequency vibrations typically occur in many practical structures and systems when in use, for example, in aerospaces and industrial machines. Piezoelectric materials feature compactness, lightweight, high integration potential, and permit to transduce mechanical energy from vibrations into electrical energy. Because of their properties, piezoelectric materials have been receiving growing interest during the last decades as potential vibration- harvested energy generators for the proliferating number of embeddable wireless sensor systems in applications such as structural health monitoring (SHM). The basic idea behind piezoelectric energy harvesting (PEH) powered architectures, or energy harvesting (EH) more in general, is to develop truly “fit and forget” solutions that allow reducing physical installations and burdens to maintenance over battery-powered systems. However, due to the low mechanical energy available under low-frequency conditions and the relatively high power consumption of wireless sensor nodes, PEH from low-frequency vibrations is a challenge that needs to be addressed for the majority of the practical cases. Simply saying, the energy harvested from low-frequency vibrations is not high enough to power wireless sensor nodes or the power consumption of the wireless sensor nodes is higher than the harvested energy. This represents a main barrier to the widespread use of PEH technology at the current state of the development, despite the advantages it may offer. The main contribution of this research work concerns the proposal of a novel EH circuitry, which is based on a whole-system approach, in order to develop enhanced PEH powered wireless sensor nodes, hence to compensate the existing mismatch between harvested and demanded energy. By whole-system approach, it is meant that this work develops an integrated system-of-systems rather than a single EH unit, thus getting closer to the industrial need of a ready- to-use energy-autonomous solution for wireless sensor applications such as SHM. To achieve so, this work introduces: Novel passive interfaces in connection with the piezoelectric harvester that permit to extract more energy from it (i.e., a complex conjugate impedance matching (CCIM) interface, which uses a PC permalloy toroidal coil to achieve a large inductive reactance with a centimetre- scaled size at low frequency; and interfaces for resonant PEH applications, which exploit the harvester‟s displacement to achieve a mechanical amplification of the input force, a magnetic and a mechanical activation of a synchronised switching harvesting on inductor (SSHI) mechanism). A novel power management approach, which permits to minimise the power consumption for conditioning the transduced signal and optimises the flow of the harvested energy towards a custom-developed wireless sensor communication node (WSCN) through a dedicated energy-aware interface (EAI); where the EAI is based on a voltage sensing device across a capacitive energy storage. Theoretical and experimental analyses of the developed systems are carried in connection with resistive loads and the WSCN under excitations of low frequency and strain/acceleration levels typical of two potential energy- autonomous applications, that are: 1) wireless condition monitoring of commercial aircraft wings through non-resonant PEH based on Macro-Fibre Composite (MFC) material bonded to aluminium and composite substrates; and wireless condition monitoring of large industrial machinery through resonant PEH based on a cantilever structure. shown that under similar testing conditions the developed systems feature a performance in comparison with other architectures reported in the literature or currently available on the market. Power levels up to 12.16 mW and 116.6 µW were respectively measured across an optimal resistive load of 66 277 kΩ for an implemented non-resonant MFC energy harvester on aluminium substrate and a resonant cantilever-based structure when no interfaces were added into the circuits. When the WSCN was connected to the harvesters in place of the resistive loads, data transmissions as fast as 0.4 and s were also respectively measured. By use of the implemented passive interfaces, a maximum power enhancement of around 95% and 452% was achieved in the two tested cases and faster data transmissions obtained with a maximum percentage improvement around 36% and 73%, respectively. By the use of the EAI in connection with the WSCN, results have also shown that the overall system‟s power consumption is as low as a few microwatts during non- active modes of operation (i.e., before the WSCN starts data acquisition and transmission to a base station). Through the introduction of the developed interfaces, this research work takes a whole-system approach and brings about the capability to continuously power wireless sensor nodes entirely from vibration-harvested energy in time intervals of a few seconds or fractions of a second once they have been firstly activated. Therefore, such an approach has potential to be used for real-world energy- autonomous applications of SHM.

This study focused on using magnetic sensors for localization and identification of targets with a wireless sensor network (WSN). A wireless sensor network with MICAz motes was set up utilizing a centralized tree-based system. The MTS310, which is equipped with a 2-axis magnetic sensor was used as the sensor board on MICAz motes. The use of magnetic sensors in wireless sensor networks is a topic that has gained limited attention in comparison to that of other sensors. Research has generally focused on the detection of large ferromagnetic targets (e.g., cars and airplanes). Moreover, the changes in the magnetic field intensity measured by the sensor have been used to obtain simple information, such as target direction or whether or not the target has passed a certain point. This work aims at understanding the sensing limitations of magnetic sensors by considering small-scale targets moving within a 30 cm radius. Four heavy iron bars were used as test targets in this study. Target detection, identification and sequential localization were accomplished using the Minimum Euclidean Distance (MED) method. The results show the accuracy of this method for this job. Different forms of sensor sensing region discretization were considered. Target identification was done on the boundaries of sensing regions. Different gateways were selected as entrance point for identification point and the results of them were compared with each other. An online ILS system was implemented and continuous movements of the ferromagnetic objects were monitored. The undesirable factors which affect the measurements were discussed and techniques to reduce or eliminate faulty measurements are presented. A magnetic sensor orientation detector and set/reset strap have been designed and fabricated. Orthogonal Matching Pursuit (OMP) algorithm was proposed for multiple sensors multiple target case in ILS systems as a future work. This study can then be used to design energy-efficient, intelligent magnetic sensor networks

Les progrès technologiques permettent aujourd'hui l’intégration à bas coût d'objets multi-capteurs hétérogènes communicants sans fil notamment pour la surveillance dans les environnements considérés à risques ou non accessibles. Le but de ces travaux de thèse est de contribuer à la sécurisation des personnes et de leur environnement de vie par la mise en réseau de dispositifs multi-capteurs de mesures sans fil. Ceux-ci doivent être spécifiés et configurés pour rendre par exemple l'environnement qu'ils surveillent intelligent et sécurisé. Le travail effectué porte sur la conception et le prototypage réel d’un réseau composé de dispositifs hétérogènes autonomes en énergie. Nos contributions comportent trois volets essentiels :Le premier volet concerne la conception d’un modèle de réseau ambiant adapté : nous avons proposé un modèle qui repose sur une architecture multi-niveaux caractérisée par des nœuds hétérogènes dont le captage (détection), le traitement et le stockage des données sont distribués par niveau. Cette architecture hiérarchique offre plusieurs avantages par rapport aux architectures linéaires classiques en termes d’évolutivité, de faible coût, de meilleure couverture, de hautes fonctionnalités et de fiabilité. Nous avons défini le comportement adapté pour chaque nœud dans le modèle et montré l’avantage de la solution par la simulation.Le deuxième volet concerne la proposition originale d’un protocole d’accès au médium efficace en énergie nommé « T-TMAC » et adapté à l’application, permettant d'organiser les échanges des messages dans l’architecture du réseau retenu. L’originalité du protocole est qu’il est composé de mécanismes de maintenance performants permettant la gestion de la mobilité et la reconfiguration du réseau (ajout et suppression d’un capteur). Pour cela, une adaptation et un paramétrage du standard IEEE 802.15.4 sont proposés.Le dernier volet présente l’évaluation et l’analyse de performances du protocole développé dans le cadre de scénarios de tests. Nous avons étudié en particulier l’impact de la taille des données et la périodicité de transfert sur l’énergie et le délai. Le protocole est validé à l’aide d’un modèle analytique dont les résultats ont été comparés à ceux obtenus par prototypage matériel. / Today technological advances allow low-cost deployment of wireless heterogeneous sensors in specific environments such as those considered risky or not accessible. The aim of this thesis is to contribute to the application of Wireless Sensor Networks (WSN) for health-care monitoring. Currently the integrated sensors must be specified and configured to make the monitored environment intelligent and secured. Our work focuses on the design of this network and the prototyping of the real devices that constitute it. Our contributions include three key components:The first part concerns the design of an ambient adapted network: we proposed a model based on a network architecture characterized by multiple tiers with heterogeneous nodes distributed: sensing, processing and data storage. This architecture offers more advantages than classical single tier architecture in terms of scalability, low cost, coverage, functionality and reliability. We have defined the appropriate behavior for each node in this network model and we showed the advantages of our solution through simulation.The second part deals with the proposition of an energy efficient medium access protocol named "T-TMAC": the protocol is adapted to the application requirements. It permits to organize the data exchange in the chosen network architecture. The originality of this protocol is that it includes efficient maintenance mechanisms that allow managing mobility and network reconfiguration (addition of a sensor, removing a sensor). In this way an adaptation and a parameterization of the IEEE 802.15.4 Standard are proposed.The final part of this work presents the performance evaluation and analysis of the proposed MAC protocol in use cases. We studied the impact of packets size and dissemination interval parameters on energy and delay. The protocol is validated by an analytical model. We proposed a reel evaluation by prototyping. A comparison of results obtained from the different approaches is finally presented.

The new and upcoming field of wireless sensor networking is unfortunately still lacking in terms of both digital forensics and security. All communications between different nodes (also known as motes) are sent out in a broadcast fashion. These broadcasts make it quite difficult to capture data packets forensically and, at the same time, retain their integrity and authenticity. The study presents several attacks that can be executed successfully on a wireless sensor network, after which the dissertation delves more deeply into the flooding attack as it is one of the most difficult attacks to address in wireless sensor networks. Furthermore, a set of factors is presented to take into account while attempting to achieve digital forensic readiness in wireless sensor networks. The set of factors is subsequently discussed critically and a model is proposed for implementing digital forensic readiness in a wireless sensor network. The proposed model is next transformed into a working prototype that is able to provide digital forensic readiness to a wireless sensor network. The main contribution of this research is the digital forensic readiness prototype that can be used to add a digital forensics layer to any existing wireless sensor network. The prototype ensures the integrity and authenticity of each of the data packets captured from the existing wireless sensor network by using the number of motes in the network that have seen a data packet to determine its integrity and authenticity in the network. The prototype also works on different types of wireless sensor networks that are in the frequency range of the network on which the prototype is implemented, and does not require any modifications to be made to the existing wireless sensor network. Flooding attacks pose a major problem in wireless sensor networks due to the broadcasting of communication between motes in wireless sensor networks. The prototype is able to address this problem by using a solution proposed in this dissertation to determine a sudden influx of data packets within a wireless sensor network. The prototype is able to detect flooding attacks while they are occurring and can therefore address the flooding attack immediately. Finally, this dissertation critically discusses the advantages of having such a digital forensic readiness system in place in a wireless sensor network environment. Copyright / Dissertation (MSc)--University of Pretoria, 2012. / Computer Science / unrestricted

This thesis investigates the problem of detecting an intruder in the presence of clutter in a Passive Infra-Red (PIR) based Wireless Sensor Network (WSN). As one of the major objectives in a WSN is to maximize battery life, data transmission and local computations must be kept to a minimum as they are expensive in terms of energy. But, as intrusion being a rare event and cannot be missed, local computations expend more energy than data transmission. Hence, the need for a low-complexity algorithm for intrusion detection is inevitable. A low-complexity algorithm for intrusion detection in the presence of clutter arising from wind-blown vegetation, using PIR sensors is presented. The algorithm is based on a combination of Haar Transform (HT) and Support Vector Machine (SVM) based training. The amplitude and frequency of the intruder signature is used to differentiate it from the clutter signal. The HT was preferred to Discrete Fourier Transform (DFT) in computing the spectral signature because of its computational simplicity -just additions and subtractions suffice (scaling coefficients taken care appropriately). Intruder data collected in a laboratory and clutter data collected from various types of vegetation were fed into SVM for training. The optimal decision rule returned by SVM was then used to separate intruder from clutter. Simulation results along with some representative samples in which intrusions were detected and the clutter being rejected by the algorithm is presented. The implementation of the proposed intruder-detection algorithm in a network setting comprising of 20 sensing nodes is discussed. The field testing performance of the algorithm is then discussed. The limitations of the algorithm is also discussed. A closed-form analytical expression for the signature generated by a human moving along a straight line in the vicinity of the PIR sensor at constant velocity is provided. It is shown to be a good approximation by showing a close match with the real intruder waveforms. It is then shown how this expression can be exploited to track the intruder from the signatures of three well-positioned sensing nodes.

Das drahtlose Sensornetzwerk (WSN) ist eine Technologie, die Umgebungsbedingungen oder physikalische Parameter misst, weiterleitet und per Fernüberwachung zur Verfügung stellt. Normalerweise werden die Sensorknoten, die diese Netzwerke bilden, von Batterien gespeist. Diese sollen aus verschiedenen Gründen nicht mehr verwendet werden, sondern es wird auf eine eigenständige Stromversorgung gesetzt. Dies soll den aufwendigen Austausch und die Wartung minimieren. Energy Harvesting kann mit den Knoten verwendet werden, um die Batterien zu unterstützen und die Lebensdauer der Netzwerke zu verlängern. Aufgrund der hohen Leistungsdichte der Solarenergie im Vergleich zu verschiedenen anderen Umweltenergien sind Solarzellen die am häufigsten eingesetzten Wandler, allerdings stellt die schwankende und intermittierende Natur der Solarenergie eine Herausforderung dar, einen funktionalen und zuverlässigen Sensorknoten zu versorgen. Um den Sensorknoten effektiv zu betreiben, sollte sein Energieverbrauch sinnvoll gesteuert werden. Ein interessanter Ansatz zu diesem Zweck ist die Steuerung der Aktivitäten des Knotens in Abhängigkeit von der zukünftig verfügbaren Energie. Dies erfordert eine Vorhersage der wandelbaren Sonnenenergie für die kommenden Betriebszeiten einschließlich der freien Zeiten der Sonne. Einige Vorhersagealgorithmen wurden mit stochastischen und statistischen Prinzipien sowie mit Methoden der künstlichen Intelligenz (KI) erstellt. Durch diese Algorithmen bleibt ein erheblicher Vorhersagefehler von 5-70%, der den zuverlässigen Betrieb der Knoten beeinträchtigt. Beispielsweise verwenden die stochastischen Methoden einen diskreten Energiezustand, der meist nicht zu den tatsächlichen Messwerten passt. Die statistischen Methoden verwenden einen Gewichtungsfaktor für die zuvor registrierten Messwerte. Daher sind sie nur geeignet, um Energieprofile bei konstanten Wetterbedingungen vorherzusagen. KI-Methoden erfordern große Beobachtungen im Trainingsprozess, die den benötigten Speicherplatz erhöhen. Dementsprechend ist die Leistung hinsichtlich der Vorhersagegenauigkeit dieser Algorithmen nicht ausreichend. In dieser Arbeit wird ein Vorhersagealgorithmus mit einem neuronalen Netzwerk entwickelt und eingebunden in einen Mikrocontroller, um die Verwaltung des Energieverbrauchs von solarzellengesteuerten Sensorknoten zu optimieren. Das verwendete neuronale Netzwerk wurde mit einer Kombination aus meteorologischen und statistischen Eingangsparametern realisiert. Dies hat zum Ziel, die erforderlichen Designkriterien für Sensorknoten zu erfüllen und eine Leistung zu erreichen, die in ihrer Genauigkeit die Leistung der oben genannten traditionellen Algorithmen übersteigt. Die Vorhersagegenauigkeit die durch den Korrelationskoeffizienten repräsentiert wird, wurde für das entwickelte neuronale Netzwerk auf 0,992 bestimmt. Das genaueste traditionelle Netzwerk erreicht nur einen Wert von 0,963. Das entwickelte neuronale Netzwerk wurde in einen Prototyp eines Sensorknotens integriert, um die Betriebszustände oder -modi über einen Simulationszeitraum von einer Woche anzupassen. Während dieser Zeit hat der Sensorknoten 6 Stunden zusätzlich im Normalbetrieb gearbeitet. Dies trug dazu bei, eine effektive Nutzung der verfügbaren Energie um ca. 3,6% besser zu erfüllen als das genaueste traditionelle Netz. Dadurch wird eine längere Lebensdauer und Zuverlässigkeit des Sensorknotens erreicht. / Wireless Sensor Network (WSN) is a technology that measures an environmental or physical parameters in order to use them by decision makers with a possibility of remote monitoring. Normally, sensor nodes that compose these networks are powered by batteries which are no longer feasible, especially when they used as fixed and standalone power source. This is due to the costly replacement and maintenance. Ambient energy harvesting systems can be used with these nodes to support the batteries and to prolong the lifetime of these networks. Due to the high power density of solar energy in comparison with different environmental energies, solar cells are the most utilized harvesting systems. Although that, the fluctuating and intermittent nature of solar energy causes a real challenge against fulfilling a functional and reliable sensor node. In order to operate the sensor node effectively, its energy consumption should be well managed. One interesting approach for this purpose is to control the future node’s activities according to the prospective energy available. This requires performing a prior prediction of the harvestable solar energy for the upcoming operation periods including the sun’s free times. A few prediction algorithms have been created using stochastic and statistical principles as well as artificial intelligence (AI) methods. A considerable prediction error of 5-70% is realized by these algorithms affecting the reliable operation of the nodes. For example, the stochastic ones use a discrete energy states which are mostly do not fit the actual readings. The statistical methods use a weighting factors for the previous registered readings. Thus, they are convenient only to predict energy profiles under consistent weather conditions. AI methods require large observations to be used in the training process which increase the memory space needed. Accordingly, the performance concerning the prediction accuracy of these algorithms is not sufficient. In this thesis, a prediction algorithm using a neural network has been proposed and implemented in a microcontroller for managing energy consumption of solar cell driven sensor nodes. The utilized neural network has been developed using a combination of meteorological and statistical input parameters. This is to meet a required design criteria for the sensor nodes and to fulfill a performance exceeds in its accuracy the performance of aforementioned traditional algorithms. The prediction accuracy represented by the correlation coefficient has been registered for the developed neural network to be 0.992, which increases the most accurate traditional network which has a value 0.963. The developed neural network has been embedded into a sensor node prototype to adjust the operating states or modes over a simulation period of one week. During this period, the sensor node has worked 6 hours more towards normal operation mode. This in its role helped to fulfill an effective use of available energy approximately 3.6% better than the most accurate traditional network. Thus, longer lifetime and more reliable sensor node.

This thesis addresses a multi-phase research and development project that spanned nearly four years, targeted at providing an ultra high-efficiency, user-friendly, and economic intelligent lighting solution for commercial facility applications, initially targeting underground parking specifically. The system would leverage the strengths of four key technologies: high brightness white Light Emitting Diodes (LEDs), wireless sensor and actuator networks, single board computers, and cloud computing. An introduction to these technologies and an overview of how they were combined to build an intelligent lighting solution is given, followed by an in-depth description of the design and implementation of one of the main subsystems – the Sensor Middleware System – residing on a single board computer. Newly-available LED luminaires (a.k.a. light fixtures) bring the combination of high efficiency, reliability, illumination quality, and long-lifetime to the lighting market. Emerging low-power – and recently low-cost – 802.15.4 wireless networks offer high controllability and responsiveness to deployed luminaires and sensors. The cost- associativity, low maintenance, and easy build-up of Internet Data Center “cloud” computing resources make data collection and remote management infrastructure for Building Automation Systems accessible to even small companies. Additionally, these resources can be much more appropriately sized and allocated, which reduces energy use. These technologies are combined to form an Intelligent Lighting System (ILS). Fitting well within the Internet of Things paradigm, this highly distributed messaging-based “system of systems” was designed to be reliable through loose coupling – spanning multiple network layers and messaging protocols. Its goal was to deliver significant energy savings over incumbent technologies, configurable and responsive lighting service behaviour, and improved experience for users within the facility (pedestrians and drivers) and those interacting with its web-based tools (building managers and ILS administrators). The ILS was partitioned into three main subsystems as follows. The installed Wireless Field Network (WFN) of luminaires and sensors provided coordinated scheduled and real-time output level adjustment (i.e. dimming), with the help of motion sensor triggers. The Monitoring and Configuration System (MCS) in the cloud provided remote data collection and a web-based monitoring and configuration Graphical User Interface application. Network hardware and Message-Oriented Middleware (MOM) were responsible for tying these subsystems together. The MOM layer that provided the message brokering, translating, envelope wrapping, and guaranteed delivery services between the WFN and MCS, as well as field supervisory and quality-of-service functions for the WFN, was called the Sensor Middleware System (SMS). It was hosted on a single board computer located at the facility. / Graduate

[ES] La industria se encuentra inmersa de pleno en la cuarta revolución industrial, y es gracias a la capacidad de digitalización y de procesamiento de grandes cantidades de datos, que se consigue mejorar y optimizar el rendimiento de los sistemas industriales actuales. Son muchos los paradigmas y conceptos que están dando forma a lo que se conoce como Industria 4.0, y uno de ellos ha sido el Internet de las Cosas (IoT: Internet of Things) o más concretamente el Internet Industrial de las Cosas (IIoT: Industrial Internet of Things), como se ha llamado al subconjunto con determinados requisitos orientados al sector industrial. Las redes de sensores inalámbricos (WSN: Wireless Sensor Networks) son tecnologías habilitadoras para estos sistemas IoT, ya que gracias a su fácil escalabilidad ofrecen gran capacidad de sensorización con un coste energético reducido. En el ámbito industrial, estas redes de sensores deben cumplir con requisitos estrictos de fiabilidad, y su aceptación está siendo lenta debido a que su robustez y facilidad de configuración no han podido rivalizar con las tecnologías clásicas. Con el desarrollo de esta tesis, se pretende hacer frente a determinados aspectos de mejora de las redes industriales de sensores inalámbricos. Para ello, se diseñan nuevos mecanismos para la sincronización, evaluando metodologías alternativas de enrutamiento y proponiendo modelos analíticos que permitan caracterizar fielmente el proceso de despliegue de estas redes, con el objetivo de cubrir aquellas lagunas que dejan los estándares y protocolos bajo estudio. Los mecanismos propuestos por el estándar IEEE 802.15.4e, en concreto el método de acceso al medio mediante TSCH (Time-Slotted Channel Hopping), presentan las bases sobre las que construir una red WSN fiable y robusta, y mediante los desarrollos propuestos en esta tesis se facilita su implantación en sistemas de Industria 4.0. / [CA] La indústria es troba de ple en la quarta revolució industrial, i és gràcies a la capacitat de digitalització i de processament de grans quantitats de dades, que s'aconsegueix millorar i optimitzar el rendiment dels sistemes industrials actuals. Són moltes els paradigmes i conceptes que estan donant forma al que es coneix com a Indústria 4.0, i una d'elles ha sigut la Internet de les Coses (IoT: Internet of Things) o més concretament la Internet Industrial de les Coses (IIoT: Industrial Internet of Things), com s'ha anomenat al subconjunt amb determinats requisits orientats al sector industrial. Les xarxes de sensors sense fils (WSN: Wireless Sensor Networks) són tecnologies habilitadores per a sistemes IoT, ja que gràcies a la seua fàcil escalabilitat ofereixen gran capacitat de digitalització amb un cost energètic reduït. En l'àmbit industrial, aquestes xarxes de sensors han de complir amb requisits estrictes, i la seua acceptació està sent lenta a causa de factors que fan que aquests sistemes no substituïsquen a les tecnologies clàssiques. Amb el desenvolupament d'aquesta tesi, es pretén fer front a determinats aspectes de millora de les xarxes industrials de sensors sense fils. Per a això, es dissenyen nous mecanismes per a la sincronització, avaluant metodologies alternatives d'encaminament i proposant models analítics que permeten caracteritzar fidelment el procés de desplegament d'aquestes xarxes, amb l'objectiu de cobrir aquelles llacunes que deixa l'estàndard. Els mecanismes proposats per l'estàndard IEEE 802.15.4, en concret el mètode d'accés al mitjà TSCH, presenten les bases sobre les quals construir una xarxa WSN fiable i robusta, i mitjançant els desenvolupaments proposats en aquesta tesi es facilita la seua implantació en sistemes d'Indústria 4.0. / [EN] The industry is fully engaged in the fourth industrial revolution. Due to digital transformation and the processing of large amounts of data, it is possible to improve the value chain and provide a real-time optimization for the current industrial systems. There are many paradigms and concepts that that fall under the umbrella of Industry 4.0, and one of them is the Internet of Things (IoT) or more specifically the Industrial Internet of Things (IIoT), as the subset with certain industry-oriented requirements is known. Wireless Sensor Networks (WSN) are an enabling technology for IoT systems, since its easy scalability offers a great sensorization capacity with a reduced energy cost. In the industrial field, these sensor networks must meet strict reliability requirements, so growth in the industrial market is slow as these systems fail to replace legacy technologies. This thesis addresses different aspects of improving industrial wireless sensor networks. To that end, new mechanisms for synchronization have been designed, evaluating alternative routing methodologies and proposing analytical models that allow a comprehensive characterization of the deployment process of these networks, aiming of covering those gaps left by the standard. The mechanisms proposed by the IEEE 802.15.4 standard, specifically the medium access control method Time-Slotted Channel Hopping (TSCH), present the bases to deploy a reliable and robust WSN network, and through the developments proposed in the thesis, it is possible smooth the way for its implementation in Industry 4.0 systems. / Vera Pérez, J. (2021). Diseño y evaluación de mecanismos de optimización en redes de sensores inalámbricas industriales [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/179700 / TESIS