320 research outputs found
On Age-of-Information Aware Resource Allocation for Industrial Control-Communication-Codesign
Unter dem Überbegriff Industrie 4.0 wird in der industriellen Fertigung die zunehmende Digitalisierung und Vernetzung von industriellen Maschinen und Prozessen zusammengefasst. Die drahtlose, hoch-zuverlässige, niedrig-latente Kommunikation (engl. ultra-reliable low-latency communication, URLLC) – als Bestandteil von 5G gewährleistet höchste Dienstgüten, 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 Dienstgüten von URLLC nicht notwendigerweise erforderlich sind, um eine hohe Regelgüte zu erzielen. Das Co-Design von Kommunikation und Regelung ermöglicht eine gemeinsame Optimierung von Regelgüte und Netzwerkparametern durch die Aufweichung der Grenze zwischen Netzwerk- und Applikationsschicht. Durch diese Verschränkung wird jedoch eine fundamentale (gemeinsame) Neuentwicklung von Regelungssystemen und Kommunikationsnetzen nötig, was ein Hindernis für die Verbreitung dieses Ansatzes darstellt. Stattdessen bedient sich diese Dissertation einem Co-Design-Ansatz, der beide Domänen weiterhin eindeutig voneinander abgrenzt, aber das Informationsalter (engl. age of information, AoI) als bedeutenden Schnittstellenparameter ausnutzt.
Diese Dissertation trägt dazu bei, die Echtzeitanwendungszuverlässigkeit als Folge der Überschreitung 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, für Echtzeitanwendungen äußerst vorteilhaft ist. Mit der Annahme von schnellem Schwund als dominanter Fehlerursache auf der Luftschnittstelle werden durch zeitdiskrete Markovmodelle, die für die zwei Netzwerkarchitekturen Single-Hop und Dual-Hop präsentiert werden, Kommunikationsfehlerfolgen auf einen Applikationsfehler abgebildet. Diese Modellierung ermöglicht die analytische Ableitung von anwendungsbezogenen Zuverlässigkeitsmetriken wie die durschnittliche Dauer bis zu einem Fehler (engl. mean time to failure). Für Single-Hop-Netze wird das neuartige Ressourcenallokationsschema State-Aware Resource Allocation (SARA) entwickelt, das auf dem Informationsalter beruht und die Anwendungszuverlässigkeit im Vergleich zu statischer Multi-Konnektivität um Größenordnungen erhöht, während der Ressourcenverbrauch im Bereich von konventioneller Einzelkonnektivität bleibt.
Diese Zuverlässigkeit kann auch innerhalb eines Systems von Regelanwendungen, in welchem mehrere Agenten um eine begrenzte Anzahl Ressourcen konkurrieren, statistisch garantiert werden, wenn die Anzahl der verfügbaren Ressourcen pro Agent um ca. 10 % erhöht werden. Für das Dual-Hop Szenario wird darüberhinaus ein Optimierungsverfahren vorgestellt, das eine benutzerdefinierte Kostenfunktion minimiert, die niedrige Anwendungszuverlässigkeit, hohes Informationsalter und hohen durchschnittlichen Ressourcenverbrauch bestraft und so das benutzerdefinierte optimale SARA-Schema ableitet. Diese Optimierung kann offline durchgeführt und als Look-Up-Table in der unteren Medienzugriffsschicht zukünftiger 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 – 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 – 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 – 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 – 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 URLLC’s 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 – where multiple agents compete for a limited number of resources – 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 – 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 – 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 – 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 – 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
Adaptive Data-driven Optimization using Transfer Learning for Resilient, Energy-efficient, Resource-aware, and Secure Network Slicing in 5G-Advanced and 6G Wireless Systems
Title from PDF of title page, viewed January 31, 2023Dissertation advisor: Cory BeardVitaIncludes bibliographical references (pages 134-141)Dissertation (Ph.D)--Department of Computer Science and Electrical Engineering. University of Missouri--Kansas City, 20225G–Advanced is the next step in the evolution of the fifth–generation (5G) technology. It will introduce a new level of expanded capabilities beyond connections and enables a broader range of advanced applications and use cases. 5G–Advanced will support modern applications with greater mobility and high dependability. Artificial intelligence and Machine Learning will enhance network performance with spectral efficiency and energy savings enhancements.
This research established a framework to optimally control and manage an appropriate selection of network slices for incoming requests from diverse applications and services in Beyond 5G networks. The developed DeepSlice model is used to optimize the network and individual slice load efficiency across isolated slices and manage slice lifecycle in case of failure. The DeepSlice framework can predict the unknown connections by utilizing the learning from a developed deep-learning neural network model.
The research also addresses threats to the performance, availability, and robustness of B5G networks by proactively preventing and resolving threats. The study proposed a Secure5G framework for authentication, authorization, trust, and control for a network slicing architecture in 5G systems. The developed model prevents the 5G infrastructure from Distributed Denial of Service by analyzing incoming connections and learning from the developed model. The research demonstrates the preventive measure against volume attacks, flooding attacks, and masking (spoofing) attacks. This research builds the framework towards the zero trust objective (never trust, always verify, and verify continuously) that improves resilience.
Another fundamental difficulty for wireless network systems is providing a desirable user experience in various network conditions, such as those with varying network loads and bandwidth fluctuations. Mobile Network Operators have long battled unforeseen network traffic events. This research proposed ADAPTIVE6G to tackle the network load estimation problem using knowledge-inspired Transfer Learning by utilizing radio network Key Performance Indicators from network slices to understand and learn network load estimation problems. These algorithms enable Mobile Network Operators to optimally coordinate their computational tasks in stochastic and time-varying network states.
Energy efficiency is another significant KPI in tracking the sustainability of network slicing. Increasing traffic demands in 5G dramatically increase the energy consumption of mobile networks. This increase is unsustainable in terms of dollar cost and environmental impact. This research proposed an innovative ECO6G model to attain sustainability and energy efficiency. Research findings suggested that the developed model can reduce network energy costs without negatively impacting performance or end customer experience against the classical Machine Learning and Statistical driven models. The proposed model is validated against the industry-standardized energy efficiency definition, and operational expenditure savings are derived, showing significant cost savings to MNOs.Introduction -- A deep neural network framework towards a resilient, efficient, and secure network slicing in Beyond 5G Networks -- Adaptive resource management techniques for network slicing in Beyond 5G networks using transfer learning -- Energy and cost analysis for network slicing deployment in Beyond 5G networks -- Conclusion and future scop
Integrating Edge Computing and Software Defined Networking in Internet of Things: A Systematic Review
The Internet of Things (IoT) has transformed our interaction with the world by connecting devices, sensors, and systems to the Internet, enabling real-time monitoring, control, and automation in various applications such as smart cities, healthcare, transportation, homes, and grids. However, challenges related to latency, privacy, and bandwidth have arisen due to the massive influx of data generated by IoT devices and the limitations of traditional cloud-based architectures. Moreover, network management, interoperability, security, and scalability issues have emerged due to the rapid growth and heterogeneous nature of IoT devices. To overcome such problems, researchers proposed a new architecture called Software Defined Networking for Edge Computing in the Internet of Things (SDN-EC-IoT), which combines Edge Computing for the Internet of Things (EC-IoT) and Software Defined Internet of Things (SDIoT). Although researchers have studied EC-IoT and SDIoT as individual architectures, they have not yet addressed the combination of both, creating a significant gap in our understanding of SDN-EC-IoT. This paper aims to fill this gap by presenting a comprehensive review of how the SDN-EC-IoT paradigm can solve IoT challenges. To achieve this goal, this study conducted a literature review covering 74 articles published between 2019 and 2023. Finally, this paper identifies future research directions for SDN-EC-IoT, including the development of interoperability platforms, scalable architectures, low latency and Quality of Service (QoS) guarantees, efficient handling of big data, enhanced security and privacy, optimized energy consumption, resource-aware task offloading, and incorporation of machine learnin
Radio Resource Management Scheme for URLLC and EMBB coexistence in a Cell-Less Radio Access network
We address the latency challenges in a high-density and high-load scenario for an ultra-reliable and low-latency communication (URLLC) network which may coexist with enhanced mobile broadband (eMBB) services in the evolving wireless communication networks. We propose a new radio resource management (RRM) scheme consisting of a combination of time domain (TD) and frequency domain (FD) schedulers specific for URLLC and eMBB users. We also develop a user ranking algorithm from a radio unit (RU) perspective, which is employed by the TD scheduler to increase the efficiency of scheduling in terms of resource consumption in large-scale networks. Therefore, the optimized and novel resource scheduling scheme reduces latency for the URLLC users (requesting a URLLC service) in an efficient resource utilization manner to support scenarios with high user density. At the same time, this RRM scheme, while minimizing the latency, it also overcomes another important challenge of eMBB users (requesting an eMBB service), namely the throughput of those who coexist in such highly loaded scenario with URLLC users. The effectiveness of our proposed scheme including time and frequency domain (TD and FD) schedulers is analyzed. Simulation results show that the proposed scheme improves the latency of URLLC users and throughput of the eMBB users compared to the baseline scheme. The proposed scheme has a 29% latency improvement for URLLC and 90% signal-to-interference-plus-noise ratio (SINR) improvement for eMBB users as compared with conventional scheduling policies.This work was supported by the European Union H2020 Research and Innovation Programme funded by the Marie Skłodowska-Curie ITN TeamUp5G Project under Grant 813391
GPS Anomaly Detection And Machine Learning Models For Precise Unmanned Aerial Systems
The rapid development and deployment of 5G/6G networks have brought numerous benefits such as faster speeds, enhanced capacity, improved reliability, lower latency, greater network efficiency, and enablement of new applications. Emerging applications of 5G impacting billions of devices and embedded electronics also pose cyber security vulnerabilities. This thesis focuses on the development of Global Positioning Systems (GPS) Based Anomaly Detection and corresponding algorithms for Unmanned Aerial Systems (UAS). Chapter 1 provides an overview of the thesis background and its objectives. Chapter 2 presents an overview of the 5G architectures, their advantages, and potential cyber threat types. Chapter 3 addresses the issue of GPS dropouts by taking the use case of the Dallas-Fort Worth (DFW) airport. By analyzing data from surveillance drones in the (DFW) area, its message frequency, and statistics on time differences between GPS messages were examined. Chapter 4 focuses on modeling and detecting false data injection (FDI) on GPS. Specifically, three scenarios, including Gaussian noise injection, data duplication, data manipulation are modeled. Further, multiple detection schemes that are Clustering-based and reinforcement learning techniques are deployed and detection accuracy were investigated. Chapter 5 shows the results of Chapters 3 and 4. Overall, this research provides a categorization and possible outlier detection to minimize the GPS interference for UAS enhancing the security and reliability of UAS operations
Heterogeneous Acceleration for 5G New Radio Channel Modelling Using FPGAs and GPUs
L'abstract è presente nell'allegato / the abstract is in the attachmen
A Survey of Scheduling in 5G URLLC and Outlook for Emerging 6G Systems
Future wireless communication is expected to be a paradigm shift from three basic service requirements of 5th Generation (5G) including enhanced Mobile Broadband (eMBB), Ultra Reliable and Low Latency communication (URLLC) and the massive Machine Type Communication (mMTC). Integration of the three heterogeneous services into a single system is a challenging task. The integration includes several design issues including scheduling network resources with various services. Specially, scheduling the URLLC packets with eMBB and mMTC packets need more attention as it is a promising service of 5G and beyond systems. It needs to meet stringent Quality of Service (QoS) requirements and is used in time-critical applications. Thus through understanding of packet scheduling issues in existing system and potential future challenges is necessary. This paper surveys the potential works that addresses the packet scheduling algorithms for 5G and beyond systems in recent years. It provides state of the art review covering three main perspectives such as decentralised, centralised and joint scheduling techniques. The conventional decentralised algorithms are discussed first followed by the centralised algorithms with specific focus on single and multi-connected network perspective. Joint scheduling algorithms are also discussed in details. In order to provide an in-depth understanding of the key scheduling approaches, the performances of some prominent scheduling algorithms are evaluated and analysed. This paper also provides an insight into the potential challenges and future research directions from the scheduling perspective
KuberneTSN: a Deterministic Overlay Network for Time-Sensitive Containerized Environments
The emerging paradigm of resource disaggregation enables the deployment of
cloud-like services across a pool of physical and virtualized resources,
interconnected using a network fabric. This design embodies several benefits in
terms of resource efficiency and cost-effectiveness, service elasticity and
adaptability, etc. Application domains benefiting from such a trend include
cyber-physical systems (CPS), tactile internet, 5G networks and beyond, or
mixed reality applications, all generally embodying heterogeneous Quality of
Service (QoS) requirements. In this context, a key enabling factor to fully
support those mixed-criticality scenarios will be the network and the
system-level support for time-sensitive communication. Although a lot of work
has been conducted on devising efficient orchestration and CPU scheduling
strategies, the networking aspects of performance-critical components remain
largely unstudied. Bridging this gap, we propose KuberneTSN, an original
solution built on the Kubernetes platform, providing support for time-sensitive
traffic to unmodified application binaries. We define an architecture for an
accelerated and deterministic overlay network, which includes kernel-bypassing
networking features as well as a novel userspace packet scheduler compliant
with the Time-Sensitive Networking (TSN) standard. The solution is implemented
as tsn-cni, a Kubernetes network plugin that can coexist alongside popular
alternatives. To assess the validity of the approach, we conduct an
experimental analysis on a real distributed testbed, demonstrating that
KuberneTSN enables applications to easily meet deterministic deadlines,
provides the same guarantees of bare-metal deployments, and outperforms overlay
networks built using the Flannel plugin.Comment: 6 page
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