7,648 research outputs found
Deep generative models for network data synthesis and monitoring
Measurement and monitoring are fundamental tasks in all networks, enabling the down-stream management and optimization of the network.
Although networks inherently
have abundant amounts of monitoring data, its access and effective measurement is
another story. The challenges exist in many aspects. First, the inaccessibility of network monitoring data for external users, and it is hard to provide a high-fidelity dataset
without leaking commercial sensitive information. Second, it could be very expensive
to carry out effective data collection to cover a large-scale network system, considering the size of network growing, i.e., cell number of radio network and the number of
flows in the Internet Service Provider (ISP) network. Third, it is difficult to ensure fidelity and efficiency simultaneously in network monitoring, as the available resources
in the network element that can be applied to support the measurement function are
too limited to implement sophisticated mechanisms. Finally, understanding and explaining the behavior of the network becomes challenging due to its size and complex
structure. Various emerging optimization-based solutions (e.g., compressive sensing)
or data-driven solutions (e.g. deep learning) have been proposed for the aforementioned challenges. However, the fidelity and efficiency of existing methods cannot yet
meet the current network requirements.
The contributions made in this thesis significantly advance the state of the art in
the domain of network measurement and monitoring techniques. Overall, we leverage
cutting-edge machine learning technology, deep generative modeling, throughout the
entire thesis. First, we design and realize APPSHOT , an efficient city-scale network
traffic sharing with a conditional generative model, which only requires open-source
contextual data during inference (e.g., land use information and population distribution). Second, we develop an efficient drive testing system â GENDT, based on generative model, which combines graph neural networks, conditional generation, and quantified model uncertainty to enhance the efficiency of mobile drive testing. Third, we
design and implement DISTILGAN, a high-fidelity, efficient, versatile, and real-time
network telemetry system with latent GANs and spectral-temporal networks. Finally,
we propose SPOTLIGHT , an accurate, explainable, and efficient anomaly detection system of the Open RAN (Radio Access Network) system. The lessons learned through
this research are summarized, and interesting topics are discussed for future work in
this domain. All proposed solutions have been evaluated with real-world datasets and
applied to support different applications in real systems
Design and Evaluation of a Hardware System for Online Signal Processing within Mobile Brain-Computer Interfaces
Brain-Computer Interfaces (BCIs) sind innovative Systeme, die eine direkte Kommunikation zwischen dem Gehirn und externen GerĂ€ten ermöglichen. Diese Schnittstellen haben sich zu einer transformativen Lösung nicht nur fĂŒr Menschen mit neurologischen Verletzungen entwickelt, sondern auch fĂŒr ein breiteres Spektrum von Menschen, das sowohl medizinische als auch nicht-medizinische Anwendungen umfasst. In der Vergangenheit hat die Herausforderung, dass neurologische Verletzungen nach einer anfĂ€nglichen Erholungsphase statisch bleiben, die Forscher dazu veranlasst, innovative Wege zu beschreiten. Seit den 1970er Jahren stehen BCIs an vorderster Front dieser BemĂŒhungen. Mit den Fortschritten in der Forschung haben sich die BCI-Anwendungen erweitert und zeigen ein groĂes Potenzial fĂŒr eine Vielzahl von Anwendungen, auch fĂŒr weniger stark eingeschrĂ€nkte (zum Beispiel im Kontext von Hörelektronik) sowie völlig gesunde Menschen (zum Beispiel in der Unterhaltungsindustrie). Die Zukunft der BCI-Forschung hĂ€ngt jedoch auch von der VerfĂŒgbarkeit zuverlĂ€ssiger BCI-Hardware ab, die den Einsatz in der realen Welt gewĂ€hrleistet.
Das im Rahmen dieser Arbeit konzipierte und implementierte CereBridge-System stellt einen bedeutenden Fortschritt in der Brain-Computer-Interface-Technologie dar, da es die gesamte Hardware zur Erfassung und Verarbeitung von EEG-Signalen in ein mobiles System integriert. Die Architektur der Verarbeitungshardware basiert auf einem FPGA mit einem ARM Cortex-M3 innerhalb eines heterogenen ICs, was FlexibilitĂ€t und Effizienz bei der EEG-Signalverarbeitung gewĂ€hrleistet. Der modulare Aufbau des Systems, bestehend aus drei einzelnen Boards, gewĂ€hrleistet die Anpassbarkeit an unterschiedliche Anforderungen. Das komplette System wird an der Kopfhaut befestigt, kann autonom arbeiten, benötigt keine externe Interaktion und wiegt einschlieĂlich der 16-Kanal-EEG-Sensoren nur ca. 56 g. Der Fokus liegt auf voller MobilitĂ€t.
Das vorgeschlagene anpassbare Datenflusskonzept erleichtert die Untersuchung und nahtlose Integration von Algorithmen und erhöht die FlexibilitĂ€t des Systems. Dies wird auch durch die Möglichkeit unterstrichen, verschiedene Algorithmen auf EEG-Daten anzuwenden, um unterschiedliche Anwendungsziele zu erreichen. High-Level Synthesis (HLS) wurde verwendet, um die Algorithmen auf das FPGA zu portieren, was den Algorithmenentwicklungsprozess beschleunigt und eine schnelle Implementierung von Algorithmusvarianten ermöglicht. Evaluierungen haben gezeigt, dass das CereBridge-System in der Lage ist, die gesamte Signalverarbeitungskette zu integrieren, die fĂŒr verschiedene BCI-Anwendungen erforderlich ist. DarĂŒber hinaus kann es mit einer Batterie von mehr als 31 Stunden Dauerbetrieb betrieben werden, was es zu einer praktikablen Lösung fĂŒr mobile Langzeit-EEG-Aufzeichnungen und reale BCI-Studien macht.
Im Vergleich zu bestehenden Forschungsplattformen bietet das CereBridge-System eine bisher unerreichte LeistungsfĂ€higkeit und Ausstattung fĂŒr ein mobiles BCI. Es erfĂŒllt nicht nur die relevanten Anforderungen an ein mobiles BCI-System, sondern ebnet auch den Weg fĂŒr eine schnelle Ăbertragung von Algorithmen aus dem Labor in reale Anwendungen. Im Wesentlichen liefert diese Arbeit einen umfassenden Entwurf fĂŒr die Entwicklung und Implementierung eines hochmodernen mobilen EEG-basierten BCI-Systems und setzt damit einen neuen Standard fĂŒr BCI-Hardware, die in der Praxis eingesetzt werden kann.Brain-Computer Interfaces (BCIs) are innovative systems that enable direct communication between the brain and external devices. These interfaces have emerged as a transformative solution not only for individuals with neurological injuries, but also for a broader range of individuals, encompassing both medical and non-medical applications. Historically, the challenge of neurological injury being static after an initial recovery phase has driven researchers to explore innovative avenues. Since the 1970s, BCIs have been at one forefront of these efforts. As research has progressed, BCI applications have expanded, showing potential in a wide range of applications, including those for less severely disabled (e.g. in the context of hearing aids) and completely healthy individuals (e.g. entertainment industry). However, the future of BCI research also depends on the availability of reliable BCI hardware to ensure real-world application.
The CereBridge system designed and implemented in this work represents a significant leap forward in brain-computer interface technology by integrating all EEG signal acquisition and processing hardware into a mobile system. The processing hardware architecture is centered around an FPGA with an ARM Cortex-M3 within a heterogeneous IC, ensuring flexibility and efficiency in EEG signal processing. The modular design of the system, consisting of three individual boards, ensures adaptability to different requirements. With a focus on full mobility, the complete system is mounted on the scalp, can operate autonomously, requires no external interaction, and weighs approximately 56g, including 16 channel EEG sensors.
The proposed customizable dataflow concept facilitates the exploration and seamless integration of algorithms, increasing the flexibility of the system. This is further underscored by the ability to apply different algorithms to recorded EEG data to meet different application goals. High-Level Synthesis (HLS) was used to port algorithms to the FPGA, accelerating the algorithm development process and facilitating rapid implementation of algorithm variants. Evaluations have shown that the CereBridge system is capable of integrating the complete signal processing chain required for various BCI applications. Furthermore, it can operate continuously for more than 31 hours with a 1800mAh battery, making it a viable solution for long-term mobile EEG recording and real-world BCI studies.
Compared to existing research platforms, the CereBridge system offers unprecedented performance and features for a mobile BCI. It not only meets the relevant requirements for a mobile BCI system, but also paves the way for the rapid transition of algorithms from the laboratory to real-world applications. In essence, this work provides a comprehensive blueprint for the development and implementation of a state-of-the-art mobile EEG-based BCI system, setting a new benchmark in BCI hardware for real-world applicability
Neuromodulatory effects on early visual signal processing
Understanding how the brain processes information and generates simple to complex behavior constitutes one of the core objectives in systems neuroscience. However, when studying different neural circuits, their dynamics and interactions researchers often assume fixed connectivity, overlooking a crucial factor - the effect of neuromodulators. Neuromodulators can modulate circuit activity depending on several aspects, such as different brain states or sensory contexts. Therefore, considering the modulatory effects of neuromodulators on the functionality of neural circuits is an indispensable step towards a more complete picture of the brainâs ability to process information. Generally, this issue affects all neural systems; hence this thesis tries to address this with an experimental and computational approach to resolve neuromodulatory effects on cell type-level in a well-define system, the mouse retina. In the first study, we established and applied a machine-learning-based classification algorithm to identify individual functional retinal ganglion cell types, which enabled detailed cell type-resolved analyses. We applied the classifier to newly acquired data of light-evoked retinal ganglion cell responses and successfully identified their functional types. Here, the cell type-resolved analysis revealed that a particular principle of efficient coding applies to all types in a similar way. In a second study, we focused on the issue of inter-experimental variability that can occur during the process of pooling datasets. As a result, further downstream analyses may be complicated by the subtle variations between the individual datasets. To tackle this, we proposed a theoretical framework based on an adversarial autoencoder with the objective to remove inter-experimental variability from the pooled dataset, while preserving the underlying biological signal of interest. In the last study of this thesis, we investigated the functional effects of the neuromodulator nitric oxide on the retinal output signal. To this end, we used our previously developed retinal ganglion cell type classifier to unravel type-specific effects and established a paired recording protocol to account for type-specific time-dependent effects. We found that certain
retinal ganglion cell types showed adaptational type-specific changes and that nitric oxide had a distinct modulation of a particular group of retinal ganglion cells.
In summary, I first present several experimental and computational methods that allow to
study functional neuromodulatory effects on the retinal output signal in a cell type-resolved manner and, second, use these tools to demonstrate their feasibility to study the neuromodulator nitric oxide
A Survey on IEEE 1588 Implementation for RISC-V Low-Power Embedded Devices
IEEE 1588, also known as the Precision Time Protocol (PTP), is a standard protocol for clock synchronization in distributed systems. While it is not architecture-specific, implementing IEEE 1588 on Reduced Instruction Set Computer-V (RISC-V) low-power embedded devices demands considering the system requirements and available resources. This paper explores various approaches and techniques to achieve accurate time synchronization in such instruments. The analysis covers software and hardware implementations, discussing each methodâs challenges, benefits, and trade-offs. By examining the state-of-the-art in this field, this paper provides valuable insights and guidance for researchers and engineers working on time-critical applications in RISC-V-based embedded systems, aiding in selecting the most-suitable stack for their designs.This work was partially supported by the ECSEL Joint Undertaking in the H2020 project IMOCO4.E, grant agreement No.10100731, and by the Basque Government within the fund for research groups of the Basque University System IT1440-22 and KK-2023/00015
New Signal and Algorithms for 5G/6G High Precision Train Positioning in Tunnel with Leaky Coaxial Cable
High precision train positioning is a crucial component of intelligent transportation systems. Tunnels are commonly encountered in subways and mountainous regions. As part of the communication system infrastructure, Leaky CoaXial (LCX) Cable is widely equipped as antenna in tunnels with many advantages. LCX positioning holds great promise as a technology for rail applications in the upcoming B5G (beyond-5G) and 6G eras. This paper focuses on the LCX positioning methodology and proposes two novel algorithms along with a novel communication-positioning integration signal. Firstly, a novel algorithm called Multiple Slot Distinction (MSD) LCX positioning algorithm is proposed. The algorithm utilizes a generated pseudo spectrum to fully utilize the coupled signals radiated from different slots of LCX. This approach offers higher time resolution compared to traditional methods. To further improve the positioning accuracy to centimeter-level and increase the measuring frequency for fast trains, a novel communication-positioning integration signal is designed. It consists of traditional Positioning Reference Signal (PRS) and a significantly low power Fine Ranging Signal (FRS). FRS is configured to be continuous and superposed onto the cellular signal using Non-Orthogonal Multiple Access (NOMA) principle to minimize its interference to communication. A two-stage LCX positioning method is then executed: At the first stage, the closest slot between the receiver and LCX is estimated by the proposed MSD algorithm using PRS; At the second stage, centimeter-level positioning is achieved by tracking the carrier phase of the continuous FRS. This process is assisted by the closest slot estimation, which helps mitigate interference between neighboring slots and eliminate the integer ambiguities. Simulation results show our proposed LCX position methodology outperforms the existing ones and offer great potentials for future implementations
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Efficient abstraction of clock synchronization at the operating system level
Distributed embedded systems are emerging and gaining importance in various domains, including industrial control applications where time determinism â hence network clock synchronization â is fundamental. In modern applications, moreover, this core functionality is required by many different software components, from OS kernel and radio stack up to applications. An abstraction layer devoted to handling time needs therefore introducing, and to encapsulate time corrections at the lowest possible level, the said layer should take the form of a timer device driver offering a Virtual Clock to the entire system. In this paper we show that doing so introduces a nonlinearity in the dynamics of the clock, and we design a controller based on feedback linearization to handle the issue. To put the idea to work, we extend the Miosix RTOS with a generic interface allowing to implement virtual clocks, including the newly designed controller that we call FLOPSYNC-3 after its ancestor. Also, we introduce the resulting virtual clock in the TDMH [20] real-time wireless mesh protocol
On Age-of-Information Aware Resource Allocation for Industrial Control-Communication-Codesign
Unter dem Ăberbegriff Industrie 4.0 wird in der industriellen Fertigung die zunehmende Digitalisierung und Vernetzung von industriellen Maschinen und Prozessen zusammengefasst. Die drahtlose, hoch-zuverlĂ€ssige, niedrig-latente Kommunikation (engl. ultra-reliable low-latency communication, URLLC) â als Bestandteil von 5G gewĂ€hrleistet höchste DienstgĂŒten, die mit industriellen drahtgebundenen Technologien vergleichbar sind und wird deshalb als Wegbereiter von Industrie 4.0 gesehen. Entgegen diesem Trend haben eine Reihe von Arbeiten im Forschungsbereich der vernetzten Regelungssysteme (engl. networked control systems, NCS) gezeigt, dass die hohen DienstgĂŒten von URLLC nicht notwendigerweise erforderlich sind, um eine hohe RegelgĂŒte zu erzielen. Das Co-Design von Kommunikation und Regelung ermöglicht eine gemeinsame Optimierung von RegelgĂŒte und Netzwerkparametern durch die Aufweichung der Grenze zwischen Netzwerk- und Applikationsschicht. Durch diese VerschrĂ€nkung wird jedoch eine fundamentale (gemeinsame) Neuentwicklung von Regelungssystemen und Kommunikationsnetzen nötig, was ein Hindernis fĂŒr die Verbreitung dieses Ansatzes darstellt. Stattdessen bedient sich diese Dissertation einem Co-Design-Ansatz, der beide DomĂ€nen weiterhin eindeutig voneinander abgrenzt, aber das Informationsalter (engl. age of information, AoI) als bedeutenden Schnittstellenparameter ausnutzt.
Diese Dissertation trĂ€gt dazu bei, die EchtzeitanwendungszuverlĂ€ssigkeit als Folge der Ăberschreitung eines vorgegebenen Informationsalterschwellenwerts zu quantifizieren und fokussiert sich dabei auf den Paketverlust als Ursache. Anhand der Beispielanwendung eines fahrerlosen Transportsystems wird gezeigt, dass die zeitlich negative Korrelation von Paketfehlern, die in heutigen Systemen keine Rolle spielt, fĂŒr Echtzeitanwendungen Ă€uĂerst vorteilhaft ist. Mit der Annahme von schnellem Schwund als dominanter Fehlerursache auf der Luftschnittstelle werden durch zeitdiskrete Markovmodelle, die fĂŒr die zwei Netzwerkarchitekturen Single-Hop und Dual-Hop prĂ€sentiert werden, Kommunikationsfehlerfolgen auf einen Applikationsfehler abgebildet. Diese Modellierung ermöglicht die analytische Ableitung von anwendungsbezogenen ZuverlĂ€ssigkeitsmetriken wie die durschnittliche Dauer bis zu einem Fehler (engl. mean time to failure). FĂŒr Single-Hop-Netze wird das neuartige Ressourcenallokationsschema State-Aware Resource Allocation (SARA) entwickelt, das auf dem Informationsalter beruht und die AnwendungszuverlĂ€ssigkeit im Vergleich zu statischer Multi-KonnektivitĂ€t um GröĂenordnungen erhöht, wĂ€hrend der Ressourcenverbrauch im Bereich von konventioneller EinzelkonnektivitĂ€t bleibt.
Diese ZuverlĂ€ssigkeit kann auch innerhalb eines Systems von Regelanwendungen, in welchem mehrere Agenten um eine begrenzte Anzahl Ressourcen konkurrieren, statistisch garantiert werden, wenn die Anzahl der verfĂŒgbaren Ressourcen pro Agent um ca. 10 % erhöht werden. FĂŒr das Dual-Hop Szenario wird darĂŒberhinaus ein Optimierungsverfahren vorgestellt, das eine benutzerdefinierte Kostenfunktion minimiert, die niedrige AnwendungszuverlĂ€ssigkeit, hohes Informationsalter und hohen durchschnittlichen Ressourcenverbrauch bestraft und so das benutzerdefinierte optimale SARA-Schema ableitet. Diese Optimierung kann offline durchgefĂŒhrt und als Look-Up-Table in der unteren Medienzugriffsschicht zukĂŒnftiger industrieller Drahtlosnetze implementiert werden.:1. Introduction 1
1.1. The Need for an Industrial Solution . . . . . . . . . . . . . . . . . . . 3
1.2. Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2. Related Work 7
2.1. Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2. Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3. Codesign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3.1. The Need for Abstraction â Age of Information . . . . . . . . 11
2.4. Dependability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3. Deriving Proper Communications Requirements 17
3.1. Fundamentals of Control Theory . . . . . . . . . . . . . . . . . . . . 18
3.1.1. Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.1.2. Performance Requirements . . . . . . . . . . . . . . . . . . . 21
3.1.3. Packet Losses and Delay . . . . . . . . . . . . . . . . . . . . . 22
3.2. Joint Design of Control Loop with Packet Losses . . . . . . . . . . . . 23
3.2.1. Method 1: Reduced Sampling . . . . . . . . . . . . . . . . . . 23
3.2.2. Method 2: Markov Jump Linear System . . . . . . . . . . . . . 25
3.2.3. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.3. Focus Application: The AGV Use Case . . . . . . . . . . . . . . . . . . 31
3.3.1. Control Loop Model . . . . . . . . . . . . . . . . . . . . . . . 31
3.3.2. Control Performance Requirements . . . . . . . . . . . . . . . 33
3.3.3. Joint Modeling: Applying Reduced Sampling . . . . . . . . . . 34
3.3.4. Joint Modeling: Applying MJLS . . . . . . . . . . . . . . . . . 34
3.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4. Modeling Control-Communication Failures 43
4.1. Communication Assumptions . . . . . . . . . . . . . . . . . . . . . . 43
4.1.1. Small-Scale Fading as a Cause of Failure . . . . . . . . . . . . 44
4.1.2. Connectivity Models . . . . . . . . . . . . . . . . . . . . . . . 46
4.2. Failure Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.2.1. Single-hop network . . . . . . . . . . . . . . . . . . . . . . . . 49
4.2.2. Dual-hop network . . . . . . . . . . . . . . . . . . . . . . . . 51
4.3. Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.3.1. Mean Time to Failure . . . . . . . . . . . . . . . . . . . . . . . 54
4.3.2. Packet Loss Ratio . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.3.3. Average Number of Assigned Channels . . . . . . . . . . . . . 57
4.3.4. Age of Information . . . . . . . . . . . . . . . . . . . . . . . . 57
4.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5. Single Hop â Single Agent 61
5.1. State-Aware Resource Allocation . . . . . . . . . . . . . . . . . . . . 61
5.2. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
5.3. Erroneous Acknowledgments . . . . . . . . . . . . . . . . . . . . . . 67
5.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
6. Single Hop â Multiple Agents 71
6.1. Failure Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
6.1.1. Admission Control . . . . . . . . . . . . . . . . . . . . . . . . 72
6.1.2. Transition Probabilities . . . . . . . . . . . . . . . . . . . . . . 73
6.1.3. Computational Complexity . . . . . . . . . . . . . . . . . . . 74
6.1.4. Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . 75
6.2. Illustration Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
6.3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
6.3.1. Verification through System-Level Simulation . . . . . . . . . 78
6.3.2. Applicability on the System Level . . . . . . . . . . . . . . . . 79
6.3.3. Comparison of Admission Control Schemes . . . . . . . . . . 80
6.3.4. Impact of the Packet Loss Tolerance . . . . . . . . . . . . . . . 82
6.3.5. Impact of the Number of Agents . . . . . . . . . . . . . . . . . 84
6.3.6. Age of Information . . . . . . . . . . . . . . . . . . . . . . . . 84
6.3.7. Channel Saturation Ratio . . . . . . . . . . . . . . . . . . . . 86
6.3.8. Enforcing Full Channel Saturation . . . . . . . . . . . . . . . 86
6.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
7. Dual Hop â Single Agent 91
7.1. State-Aware Resource Allocation . . . . . . . . . . . . . . . . . . . . 91
7.2. Optimization Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
7.3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
7.3.1. Extensive Simulation . . . . . . . . . . . . . . . . . . . . . . . 96
7.3.2. Non-Integer-Constrained Optimization . . . . . . . . . . . . . 98
7.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
8. Conclusions and Outlook 105
8.1. Key Results and Conclusions . . . . . . . . . . . . . . . . . . . . . . . 105
8.2. Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
A. DC Motor Model 111
Bibliography 113
Publications of the Author 127
List of Figures 129
List of Tables 131
List of Operators and Constants 133
List of Symbols 135
List of Acronyms 137
Curriculum Vitae 139In industrial manufacturing, Industry 4.0 refers to the ongoing convergence of the real and virtual worlds, enabled through intelligently interconnecting industrial machines and processes through information and communications technology. Ultrareliable low-latency communication (URLLC) is widely regarded as the enabling technology for Industry 4.0 due to its ability to fulfill highest quality-of-service (QoS) comparable to those of industrial wireline connections. In contrast to this trend, a range of works in the research domain of networked control systems have shown that URLLCâs supreme QoS is not necessarily required to achieve high quality-ofcontrol; the co-design of control and communication enables to jointly optimize and balance both quality-of-control parameters and network parameters through blurring the boundary between application and network layer. However, through the tight interlacing, this approach requires a fundamental (joint) redesign of both control systems and communication networks and may therefore not lead to short-term widespread adoption. Therefore, this thesis instead embraces a novel co-design approach which keeps both domains distinct but leverages the combination of control and communications by yet exploiting the age of information (AoI) as a valuable interface metric.
This thesis contributes to quantifying application dependability as a consequence of exceeding a given peak AoI with the particular focus on packet losses. The beneficial influence of negative temporal packet loss correlation on control performance is demonstrated by means of the automated guided vehicle use case. Assuming small-scale fading as the dominant cause of communication failure, a series of communication failures are mapped to an application failure through discrete-time Markov models for single-hop (e.g, only uplink or downlink) and dual-hop (e.g., subsequent uplink and downlink) architectures. This enables the derivation of application-related dependability metrics such as the mean time to failure in closed form. For single-hop networks, an AoI-aware resource allocation strategy termed state-aware resource allocation (SARA) is proposed that increases the application reliability by orders of magnitude compared to static multi-connectivity while keeping the resource consumption in the range of best-effort single-connectivity. This dependability can also be statistically guaranteed on a system level â where multiple agents compete for a limited number of resources â if the provided amount of resources per agent is increased by approximately 10 %. For the dual-hop scenario, an AoI-aware resource allocation optimization is developed that minimizes a user-defined penalty function that punishes low application reliability, high AoI, and high average resource consumption. This optimization may be carried out offline and each resulting optimal SARA scheme may be implemented as a look-up table in the lower medium access control layer of future wireless industrial networks.:1. Introduction 1
1.1. The Need for an Industrial Solution . . . . . . . . . . . . . . . . . . . 3
1.2. Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2. Related Work 7
2.1. Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2. Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3. Codesign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3.1. The Need for Abstraction â Age of Information . . . . . . . . 11
2.4. Dependability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3. Deriving Proper Communications Requirements 17
3.1. Fundamentals of Control Theory . . . . . . . . . . . . . . . . . . . . 18
3.1.1. Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.1.2. Performance Requirements . . . . . . . . . . . . . . . . . . . 21
3.1.3. Packet Losses and Delay . . . . . . . . . . . . . . . . . . . . . 22
3.2. Joint Design of Control Loop with Packet Losses . . . . . . . . . . . . 23
3.2.1. Method 1: Reduced Sampling . . . . . . . . . . . . . . . . . . 23
3.2.2. Method 2: Markov Jump Linear System . . . . . . . . . . . . . 25
3.2.3. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.3. Focus Application: The AGV Use Case . . . . . . . . . . . . . . . . . . 31
3.3.1. Control Loop Model . . . . . . . . . . . . . . . . . . . . . . . 31
3.3.2. Control Performance Requirements . . . . . . . . . . . . . . . 33
3.3.3. Joint Modeling: Applying Reduced Sampling . . . . . . . . . . 34
3.3.4. Joint Modeling: Applying MJLS . . . . . . . . . . . . . . . . . 34
3.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4. Modeling Control-Communication Failures 43
4.1. Communication Assumptions . . . . . . . . . . . . . . . . . . . . . . 43
4.1.1. Small-Scale Fading as a Cause of Failure . . . . . . . . . . . . 44
4.1.2. Connectivity Models . . . . . . . . . . . . . . . . . . . . . . . 46
4.2. Failure Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.2.1. Single-hop network . . . . . . . . . . . . . . . . . . . . . . . . 49
4.2.2. Dual-hop network . . . . . . . . . . . . . . . . . . . . . . . . 51
4.3. Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.3.1. Mean Time to Failure . . . . . . . . . . . . . . . . . . . . . . . 54
4.3.2. Packet Loss Ratio . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.3.3. Average Number of Assigned Channels . . . . . . . . . . . . . 57
4.3.4. Age of Information . . . . . . . . . . . . . . . . . . . . . . . . 57
4.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5. Single Hop â Single Agent 61
5.1. State-Aware Resource Allocation . . . . . . . . . . . . . . . . . . . . 61
5.2. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
5.3. Erroneous Acknowledgments . . . . . . . . . . . . . . . . . . . . . . 67
5.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
6. Single Hop â Multiple Agents 71
6.1. Failure Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
6.1.1. Admission Control . . . . . . . . . . . . . . . . . . . . . . . . 72
6.1.2. Transition Probabilities . . . . . . . . . . . . . . . . . . . . . . 73
6.1.3. Computational Complexity . . . . . . . . . . . . . . . . . . . 74
6.1.4. Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . 75
6.2. Illustration Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
6.3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
6.3.1. Verification through System-Level Simulation . . . . . . . . . 78
6.3.2. Applicability on the System Level . . . . . . . . . . . . . . . . 79
6.3.3. Comparison of Admission Control Schemes . . . . . . . . . . 80
6.3.4. Impact of the Packet Loss Tolerance . . . . . . . . . . . . . . . 82
6.3.5. Impact of the Number of Agents . . . . . . . . . . . . . . . . . 84
6.3.6. Age of Information . . . . . . . . . . . . . . . . . . . . . . . . 84
6.3.7. Channel Saturation Ratio . . . . . . . . . . . . . . . . . . . . 86
6.3.8. Enforcing Full Channel Saturation . . . . . . . . . . . . . . . 86
6.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
7. Dual Hop â Single Agent 91
7.1. State-Aware Resource Allocation . . . . . . . . . . . . . . . . . . . . 91
7.2. Optimization Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
7.3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
7.3.1. Extensive Simulation . . . . . . . . . . . . . . . . . . . . . . . 96
7.3.2. Non-Integer-Constrained Optimization . . . . . . . . . . . . . 98
7.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
8. Conclusions and Outlook 105
8.1. Key Results and Conclusions . . . . . . . . . . . . . . . . . . . . . . . 105
8.2. Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
A. DC Motor Model 111
Bibliography 113
Publications of the Author 127
List of Figures 129
List of Tables 131
List of Operators and Constants 133
List of Symbols 135
List of Acronyms 137
Curriculum Vitae 13
Privacy-Preserving by Design: Indoor Positioning System Using Wi-Fi Passive TDOA
Indoor localization systems have become increasingly important in a wide
range of applications, including industry, security, logistics, and emergency
services. However, the growing demand for accurate localization has heightened
concerns over privacy, as many localization systems rely on active signals that
can be misused by an adversary to track users' movements or manipulate their
measurements. This paper presents PassiFi, a novel passive Wi-Fi time-based
indoor localization system that effectively balances accuracy and privacy.
PassiFi uses a passive WiFi Time Difference of Arrival (TDoA) approach that
ensures users' privacy and safeguards the integrity of their measurement data
while still achieving high accuracy. The system adopts a fingerprinting
approach to address multi-path and non-line-of-sight problems and utilizes deep
neural networks to learn the complex relationship between TDoA and location.
Evaluation in a real-world testbed demonstrates PassiFi's exceptional
performance, surpassing traditional multilateration by 128%, achieving
sub-meter accuracy on par with state-of-the-art active measurement systems, all
while preserving privacy
Unveiling the frontiers of deep learning: innovations shaping diverse domains
Deep learning (DL) enables the development of computer models that are
capable of learning, visualizing, optimizing, refining, and predicting data. In
recent years, DL has been applied in a range of fields, including audio-visual
data processing, agriculture, transportation prediction, natural language,
biomedicine, disaster management, bioinformatics, drug design, genomics, face
recognition, and ecology. To explore the current state of deep learning, it is
necessary to investigate the latest developments and applications of deep
learning in these disciplines. However, the literature is lacking in exploring
the applications of deep learning in all potential sectors. This paper thus
extensively investigates the potential applications of deep learning across all
major fields of study as well as the associated benefits and challenges. As
evidenced in the literature, DL exhibits accuracy in prediction and analysis,
makes it a powerful computational tool, and has the ability to articulate
itself and optimize, making it effective in processing data with no prior
training. Given its independence from training data, deep learning necessitates
massive amounts of data for effective analysis and processing, much like data
volume. To handle the challenge of compiling huge amounts of medical,
scientific, healthcare, and environmental data for use in deep learning, gated
architectures like LSTMs and GRUs can be utilized. For multimodal learning,
shared neurons in the neural network for all activities and specialized neurons
for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table
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