Die wechselhafte Energiebereitstellung für drahtlose Sensorknoten durch Solarzellen stellt das Energiemanagement dieser Systeme vor große Herausforderungen. Bedingt durch saisonale und kurzfristige Effekte treten kontinuierlich Schwankungen in der Eingangsleistung auf, gleichzeitig soll jedoch eine zuverlässige und konstante Systemfunktion realisiert werden. Um dies miteinander zu vereinbaren, wird ein Modell zur Beschreibung der erwarteten Eingangsleistung aufgestellt, mit welchem der planmäßige Energieverlauf bestimmt werden kann. Dieser kann wiederum mit der realen Eingangsleistung verglichen werden, um den tatsächlichen energetischen Zustand des Sensorknotens zu bestimmen. Daraus lassen sich beispielsweise Entscheidungskriterien für die Steuerung der Energieverteilung oder Betriebszustände ableiten. Im Rahmen der Arbeit werden die physikalischen Hintergründe zur Modellierung der eingehenden Sonnenenergie beschrieben, der Stand der Technik zur Modellierung aufgezeigt und ein Modell als Basis für die weiteren Untersuchungen ausgewählt. Dieses wird auf die stark limitierte Hardware von drahtlosen Sensorknoten angepasst. Die Herausforderungen liegen dabei hauptsächlich in der geringen verfügbaren Rechenleistung, wenig Datenspeicher im System und dem Ziel, möglichst wenig Energie für die Berechnung zu verbrauchen. Im Ergebnis zeigt sich, dass ein angepasstes Modell auf drahtlosen Sensorsystemen umgesetzt werden kann und trotz der starken Limitierungen lauffähig ist. Es wird eine deutliche Verbesserung in der Verteilung der Energie über den Tag ermöglicht, wodurch sich trotz wechselhafter Quelle eine konstante Systemfunktion ergibt. Nebenher wird die Zuverlässigkeit und Ausfallsicherheit erhöht und Überdimensionierungen in Energiespeicher und Solarzelle können verringert werden. Das modellbasierte Energiemanagement stellt somit einen wichtigen Baustein für eine gesicherte Energieversorgung drahtloser Sensorsysteme dar. / The volatile energy supply by solar cells for wireless sensor nodes causes vast challenges for the energy management of such systems. Conditioned by seasonal and short time effects, the incoming power continuously varies. Simultaneously a reliable and constant function of the system has to be realized. To reconcile this, a model for the expected incoming solar power has been derived, which enables the estimation of the planned energy curve. This curve can be compared with the real progression of incoming power measured in parallel, to determine the current state of energy of a sensor node. This comparison is used to derive decision criteria for the control of the energy distribution or operating conditions. Within this work, the physical backgrounds for the modelling of the incoming solar energy and the state of the art of modelling solar power are described. A model is chosen as basis for further investigations and adapted to the limited hardware of wireless sensor nodes. The main challenges are the reduced processing power, few data memory in the system and the objective to consume as few energy as possible for the calculation. The results show that an adapted model can be implemented on wireless sensor systems and that it is executable despite the heavy limitations. This enables a distinct improvement of the distribution of energy across the day, which results in a constant systems function, despite the varying incoming power. At the same time the reliability and failure safety are being improved and the oversizing of the solar cell and the storage elements can be reduced. Therefore the model based energy management is an important component for a stable power supply of wireless sensor systems.

Health technology or e-Health is one of the most rapidly growing areas in healthcare today and it has been an important requirement as a new concept of healthcare industry. Since global society has been changed to aging society and the healthcare cost has been increasing in the 21st century since 2007. As the total number of people aged 65 or older is expected to increase from 12% to 22% in 2050 which is double the rate, and at the same time there is a decrease in in-fertility rates and increase in life expectancy due to the increase in life quality, there is a need to investigate the needs and requirements of an intelligent embedded sensor systems in health applications, and to develop a new communication protocol or set of protocols that can be used to send data collected from a hub within a house, home-care or a complex and send it securely and reliably to a central database where the gathered data can be monitored by a medical professional to make decisions for further interventions. The employed communication protocol should also be able to securely transmit confidential parameters from the hospital network to a central server outside of the hospital network. The final protocol must be inline with the regulations of the EU. This thesis is done in collaboration with Tjeders AB, Stille AB, and Embedded Sensor Systems for Health Plus (ESS-H+) research profile at Mälardalen University. In this thesis, different communication protocols such as IPSec and TLS and algorithms such as AES and RSA are examined and based on the requirements provided by the companies certain of these protocols and algorithms will be used in the final implementation. Different performance metrics such as overhead, round trip delay and throughput will be measured for the chosen communication protocols and recommendations will be given on which of the protocols and algorithms needs to be used to obtain an optimized, secure and reliable network.

The project explores the domain of tactical sensor systems, focusing on SAAB Gripen’s sensor technologies such as radar, RWR (Radar Warning Receiver), and IRST (InfraRed Search and Track). The study employs structured hypothesis testing and model based diagnostics to examine the effectiveness of identifying and isolating deviations within these systems. The central question addressed is whether structured hypothesis testing reliably detects and isolates anomalies in a tactical sensor system. The research employs a framework involving sensor modeling of radar, RWR, and IRST, alongside a sensor fusion model, applied on a linear target tracking model as well as a real target flight track obtained from SAAB Test Flight and Verification. Test quantities are derived from the modeled data, and synthetic faults are intentionally introduced into the system. These test quantities are then compared to predefined thresholds, thereby facilitating structured hypothesis testing. The robustness and reliability of the diagnostics model are established through a series of simulations. Multiple scenarios with varied fault introductions across different sensor measurements are examined. Key results include the successful creation of a tactical sensor model and sensor fusion environment, showcasing the ability to introduce and detect faults. The thesis provides arguments supporting the advantages of model based diagnosis through structured hypothesis testing for assessing sensor fusion data. The results of this research are applicable beyond this specific context, facilitating improved sensor data analysis across diverse tracking scenarios, including applications beyond SAAB Gripen. As sensor technologies continue to evolve, the insights gained from this thesis could offer guidance for refining sensor models and hypothesis testing techniques, ultimately enhancing the efficiency and accuracy of sensor data analysis in various domains. / Denna rapport undersöker området inom taktiska sensorsystem och fokuserar på SAAB Gripens sensorteknik, såsom radar, RWR (Radar Warning Receiver) och IRST (InfraRed Search- and Track). Studien använder strukturerad hypotesprövning och modellbaserad diagnostik för att undersöka effektiviteten av att identifiera och isolera avvikelser inom dessa system. Den centrala frågan som behandlas är om strukturerad hypotesprövning tillförlitligt upptäcker och isolerar avvikelser i ett taktiskt sensorsystem. För att tackla denna utmaning används sensormodellering av radar, RWR och IRST, tillsammans med en sensorfusionsmodell som appliceras på en linjär målspårningsmodell samt verklig målflygbana erhållen från SAAB. Testkvantiteter härleds från den resulterande datan, och syntetiska fel introduceras avsiktligt i systemet. Dessa testskvantiteter jämförs sedan med fördefinierade trösklar vilket lägger grunden för strukturerad hypotesprövning. Tillförlitligheten och pålitligheten hos diagnostikmodellen etableras genom en serie av simuleringar bestående av flera scenarier med varierade felintroduktioner över olika sensorinmätningar. Huvudresultat inkluderar skapandet av en taktisk sensormodell och en sensorfusionsmiljö, som visar förmågan att introducera och upptäcka fel på ett effektivt sätt. Avhandlingen ger argument som stödjer fördelarna med modellbaserad diagnostik genom strukturerad hypotestestning för bedömning av sensorfusionsdata. Resultaten av denna forskning är tillämpliga utanför detta specifika sammanhang, vilket underlättar förbättrad sensordataanalys över olika spårningsscenarier, inklusive applikationer bortom SAAB Gripen. I takt med att sensorteknologier fortsätter att utvecklas kan insikterna från denna avhandling ge vägledning för att förbättra sensormodeller och hypotestestningstekniker, vilket i slutändan förbättrar effektiviteten och noggrannheten för sensordataanalys inom olika områden.

Evaluating the social impacts of engineered products, or effects products have on the daily lives of individuals, is critical to ensuring that products are having positive impacts while avoiding negative impacts and to learning how to improve product designs for a more positive social impact. One approach to quantifying a product's social impact is to use social impact indicators that combine user data in a meaningful way to give insight into the current social condition of an individual or population. However, determining social impact indicators relative to engineered products and individuals in developing countries can be difficult when there is a large geographical distance between the users of a product and those designing them and since many conventional methods of user data collection require direct human interaction with or observation of users of a product. This means user data may only be collected at a single instance in time and infrequently due to the large human resources and cost associated with obtaining them. Alternatively, internet-connected, remote data collection devices paired with deep learning models can provide an effective way to use in-situ sensors to collect data required to calculate social impact indicators remotely, continuously, and less expensively than other methods. This research has identified key principles that can enable researchers, designers, and practitioners to avoid pitfalls and challenges that could be encountered at various stages of the process of using remote sensor devices and deep learning to evaluate social impact indicators of products in developing countries. Chapter 2 introduces a framework that outlines how low-fidelity user data often obtainable using remote sensors or digital technology can be collected and correlated with high-fidelity, infrequently collected user data to enable continuous, remote monitoring of engineered products using deep learning. An example application of this framework demonstrates how it can be used to collect data for calculating several social impact indicators related to water hand pumps in Uganda during a 4 day study. Chapter 3 builds on the framework established in Chapter 2 to provide principles for enabling insights when engaging in long-term deployment of using in-situ sensors and deep learning to monitor the social impact indicators of products in developing countries. These principles were identified while using this approach to monitor the social impact indicators of a water hand pump in Uganda over a 5 month data collection period. Chapter 4 provides principles for successfully developing remote data collection devices used to collect user data for determining social impact indicators. A design tool called the "Social Impact Sensor Canvas" is provided to guide device development along with a discussion of the key decisions, critical questions, common options, and considerations that should be addressed during each stage of device development to increase the likelihood of success. Lastly, Chapter 5 discusses the conclusions made possible through this research along with proposed future work.

The IoT sensing system plays an important role in the field of the smart city. IoT devices are generally constrained nodes due to their limited power and memory. How to save energy has been a challenge for the scalability of sensing networks. Previous studies introduce the dynamic sink and three dynamic sink deployment strategies. It has been proved by simulation experiments that the sensing network with dynamic sinks can reduce energy consumption. Further investigations on new dynamic sink deployment strategies are needed to explore the full potential of dynamic sinks. This work investigates three new deployment strategies, namely Determinisitic Strategy, Prediction Strategy, and Improved Prediction Strategy. We design experiments with different scenarios and evaluate the packet delivery ratio (PDR) and power consumption performances using emulated IoT devices on the Cooja simulator. The results show that the setups with these three new deployment strategies have good performance in terms of PDR and power consumption. Furthermore, we compare the performance difference between these three new strategies. The Improved Prediction Strategy has advantages over the other two strategies and has application prospects in reality. / IoT-baserade sensorsystem spelar en viktig roll för smarta städer. IoT-enheter är i allmänhet begränsade noder vad gäller till exempel kraftförsörjning och minnesutrymme. Hur man kan spara energi har varit en utmaning för skalbarheten hos sensornätverk. I tidigare studier introduceras dynamiska sänknoder och tre strategier för utplacering av sådana sänknoder. Det har visat sig genom simuleringsexperiment att ett nätverk med dynamiska sänknoder kan minska energiförbrukningen. Ytterligare undersökningar av nya strategier för utplacering av sänknoder behövs för att utforska den fulla potentialen hos dynamiska sänknoder. I det här arbetet undersöks tre nya strategier, nämligen Determinisitic Strategy, Prediction Strategy och Improved Prediction Strategy. Vi utformar experiment med olika scenarier och utvärderar andelen levererade paket (Packet Delivery Ration", PDR) och energiförbrukningen med hjälp av emulerade IoT-enheter i Cooja-simulatorn. Resultaten visar att uppställningarna med dessa tre nya strategier har bra prestanda när det gäller PDR och energiförbrukning. Dessutom jämför vi prestandaskillnaden mellan dessa tre nya strategier. Improved Prediction Strategy har fördelar jämfört med de andra två strategierna och bedöms ha goda tillämpningsmöjligheter i verkliga miljöer.

This thesis work deals with the development of three different categories of interrogation techniques for Fiber Bragg Grating based sensor networks. Such networks are used for structural health monitoring and other applications. A bulk grating based interrogation system is described first, which includes an optical source, switch, circulator, embedded controller, and software. The center wavelength determination technique employed is detailed and is shown to be highly accurate from test data. The comparison with resistance strain gauges is presented which shows that the system developed provides an accurate strain reconstruction. The system is also compared with a commercial optical spectrum analyzer and is found to exhibit good accuracy and fidelity. The system has been field tested on an aircraft structure with 14 sensors spread over 4 channels. Strain data reconstruction from these tests is shown to accurately reproduce the loading conditions. A second system developed is based on the matched filter technique using a mechanical fiber stretcher; the details of this system are presented with a mathematical treatment of the technique. The design of the fiber stretcher is also described. This design is regarded to be novel since it tries to provide large interrogation bandwidths using a parallel topology. The results of tests have shown good resolution and comparative tests with resistance strain gauges have shown accurate reproduction of strain. Finally, an interrogation system based on a wavelength tunable source is presented. This system is a precursor to a time division multiplexed interrogation system, which has also been described. Three laser configurations have been set up and characterized. The laser sweep tests have been performed on two configurations and a sensor grating reconstruction test has also been carried out.

Die Nutzung von Umgebungsenergie ermöglicht bei einer zunehmenden Anzahl von Anwendungen drahtloser Sensorknoten eine autarke Energieversorgung. Wartungseingriffe sowie unvorhergesehene Energieengpässe begrenzen den autarken Einsatz derartiger Sensorknoten oder erfordern eine deutliche Überdimensionierung der Energy Harvester und Energiespeicher. Anwendungen, wie der Einsatz in unzugänglichen Bereichen, Sensornetzwerken mit einer großen Anzahl von Knoten oder sehr kleinen Knoten / Smart Dust, können unwirtschaftlich werden, wenn kein wartungsfreier Betrieb gewährleistet werden kann. Erreicht ein drahtloser Sensorknoten einen energetisch niedrigen Zustand, ist es für einen erneuten Start erforderlich, dass zusätzlich zu einer ausreichenden Spannung genügend Energie für den Startvorgang zur Verfügung steht. Bei zu zeitiger Aktivierung wird der Startvorgang aufgrund der einbrechenden Spannung nicht abgeschlossen und verhindert die Aufnahme des Betriebs. Ein Voltage Supervisor wird benötigt, um den Startvorgang so lange zu verzögern, bis ausreichend Energie für diesen zur Verfügung steht. Im Rahmen der vorliegenden Arbeit wird ein Voltage Supervisor für das Energiemanagement umgebungsenergieversorgter Sensorknoten vorgeschlagen. Die Herausforderungen liegen in der Realisierung einer definierten Abschalt- und Startcharakteristik, auch bei langsamen oder variablen Spannungsanstiegen, in der Implementierung einer Kaltstartfähigkeit und im zuverlässigen Schaltverhalten unter widrigen Umgebungsbedingungen oder niedrigem Energieeintrag. Für eine hohe Effizienz wird bei jeder Spannung ein geringer Eigenleistungsverbrauch gefordert. Im Ergebnis wird ein Konzept vorgestellt, welches einen zuverlässigen wartungsfreien Betrieb ermöglicht. Funktionen, wie die Abschaltung des Mikrocontrollers nach Erledigung der Aufgabe, ermöglichen darüber hinaus, weniger Energie als im Schlafmodus zu verbrauchen. Mit dieser Methode kann ein intermittierender Betrieb in Abhängigkeit der verfügbaren Energie realisiert werden. Simulation, experimentelle Untersuchung und die Einbettung in einen umgebungsversorgten drahtlosen Sensorknoten validieren die Funktionsfähigkeit unter allen gestellten Anforderungen. Der mittlere Eigenleistungsverbrauch der vorgeschlagenen Schaltung liegt bei 5,58 µW. Bei niedrigen Spannungen von 0 V – 1,4 V werden lediglich 568 nW benötigt. / The use of ambient energy enables an autonomous energy supply for an increasing number of wireless sensor node applications. Maintenance interventions or unforeseen energy input drops limit the autonomous use of such sensor nodes or require significantly oversized energy harvesters and energy storages. Applications such as the use in inaccessible areas, sensor networks with a large number of nodes or very small nodes / Smart Dust can become uneconomical, if no maintenance-free operation can be guaranteed. If a wireless sensor node reaches an energetically low state, it is necessary for a restart, that sufficient voltage and in addition sufficient energy is available for the starting process. In the case of too early activation, the starting process will not be completed due to the voltage drop and will prevent the start of operation. A Voltage Supervisor is required to delay the start until sufficient energy is available. Within the scope of this thesis, a voltage supervisor for the energy management of environmental-powered wireless sensor nodes is proposed. The challenges are in the realization of a defined switch-off and start characteristic even with slow or variable voltage slopes, a cold start capability and in reliable behaviour under adverse environmental conditions or low energy input. For high efficiency, a low power consumption is required at every voltage. As result, a concept is presented which enables reliable maintenance-free operation. In addition, the microcontroller can switch itself off completely after completion of the task and thus consume even less energy than in sleep mode. With this method, intermittent operation depending on the available energy, can be realized as well. Simulation, experimental investigation and inclusion in an energy harvesting supplied wireless sensor node validate the functionality under all given conditions. The average power consumption of the proposed circuit is 5.58 µW. At low voltages in the range 0 V - 1.4 V only 568 nW are required.

This contribution will introduce carbon-reinforced concrete components (so-called carbon concrete composites, or C³) with sensor functionalities for innovative building envelopes. For a continuous in situ structural monitoring, these textile-reinforced concrete components are equipped with textile sensor networks consisting of resistive carbon fiber sensors (CFSs), which are integrated into the carbon fiber non-crimp fabrics of the concrete reinforcement by multiaxial warp-knitting. The in situ CFSs, consisting of 1 k or 50 k carbon fiber roving with added staple fiber/multifilament dielectric cladding, are later integral to the load-distributing elements of the concrete component, and elongations within these are easy to record with good correlation to ohmic resistance changes. Gage factors of k = 0.52–1.23 at linearity deviations of ALin=4.0–8.7% are feasible. This allows a monitoring of C³ building envelopes for structural mechanical changes caused by physical changes within the component through mechanical or thermal loads or deformation and cracks.

In smart item environments, multitude of sensors are applied to capture data about product conditions and usage to guide business decisions as well as production automation processes. A big issue in this application area is posed by the restricted quality of sensor data due to limited sensor precision as well as sensor failures and malfunctions. Decisions derived on incorrect or misleading sensor data are likely to be faulty. The issue of how to efficiently provide applications with information about data quality (DQ) is still an open research problem. In this paper, we present a flexible model for the efficient transfer and management of data quality for streaming as well as static data. We propose a data stream metamodel to allow for the propagation of data quality from the sensors up to the respective business application without a significant overhead of data. Furthermore, we present the extension of the traditional RDBMS metamodel to permit the persistent storage of data quality information in a relational database. Finally, we demonstrate a data quality metadata mapping to close the gap between the streaming environment and the target database. Our solution maintains a flexible number of DQ dimensions and supports applications directly consuming streaming data or processing data filed in a persistent database.

This dissertation describes how state of the art computer music technology can be used to digitize, analyze, preserve and extend North Indian classical music performance. Custom built controllers, influenced by the Human Computer Interaction (HCI) community, serve as new interfaces to gather musical gestures from a performing artist. Designs on how to modify a Tabla, Dholak, and Sitar with sensors and electronics are described. Experiments using wearable sensors to capture ancillary gestures of a human performer are also included. A twelve-armed solenoid-based robotic drummer was built to perform on a variety of traditional percussion instruments from around India. The dissertation also describes experimentation on interfacing a human sitar performer with the robotic drummer. Experiments include automatic tempo tracking and accompaniment methods. A framework is described for digitally transcribing performances of masters using custom designed hardware and software to aid in preservation. This work draws on knowledge from many disciplines including: music, computer science, electrical engineering, mechanical engineering and psychology. The goal is to set a paradigm on how to use technology to aid in the preservation of traditional art and culture.