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Towards a systems-based framework for understanding the diffusion of technology: A case study of a modest technological innovation in the multi-agency context of policing
Technological innovation in policing is being given greater emphasis. In public discourse about technology and policing, there is often a focus on large-scale projects that are known to fail, sometimes at significant cost. The implementation of smaller innovations are often overlooked. This thesis examines practice of innovation and adoption in the context of multi-agency working.
The literature review in this thesis reveals that little is known about contexts where decision making does not rest with the police and exposes potential limitations in the use of diffusion and adoption frameworks/models. The research question is: In the context of multi-agency diffusion and adoption of a technology to enhance policing, can systems thinking techniques enhance, or even replace, existing frameworks and models?
This empirical research study looks at the adoption of a relatively simple technology that scans identification documents. However, the decision to adopt and implement an ID scanner takes place within a complex setting. Tracking an adoption decision requires understanding of the various actors and their roles. The research includes 48 semi-structured interviews with police officers, premises owners and managers and other stakeholders involved in the decision to adopt an ID scanner. Their perceptions of the history leading to an adoption decision, their own role and that of other key actors is examined.
Initial analysis takes place using spray diagrams and further analysis is made through the lenses of existing diffusion and adoption frameworks/models. Subsequently systems thinking techniques are deployed and the additional insights they provide are highlighted. This research finds that systems thinking can extend understanding of multi-agency diffusion and adoption decisions when compared with solely utilising existing frameworks/models. Finally, the research proposes a systems-based framework for collaborative diffusion and adoption analysis
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
The stationary horizon as the central multi-type invariant measure in the KPZ universality class
The Kardar-Parisi-Zhang (KPZ) universality class describes a large class of
2-dimensional models of random growth, which exhibit universal scaling
exponents and limiting statistics. The last ten years has seen remarkable
progress in this area, with the formal construction of two interrelated
limiting objects, now termed the KPZ fixed point and the directed landscape
(DL). This dissertation focuses on a third central object, termed the
stationary horizon (SH). The SH was first introduced (and named) by Busani as
the scaling limit of the Busemann process in exponential last-passage
percolation. Shortly after, in the author's joint work with Sepp\"al\"ainen, it
was independently constructed in the context of Brownian last-passage
percolation. In this dissertation, we give an alternate construction of the SH,
directly from the description of its finite-dimensional distributions and
without reference to Busemann functions. From this description, we give several
exact distributional formulas for the SH. Next, we show the significance of the
SH as a key object in the KPZ universality class by showing that the SH is the
unique coupled invariant distribution for the DL. A major consequence of this
result is that the SH describes the Busemann process for the DL. From this
connection, we give a detailed description of the collection of semi-infinite
geodesics in the DL, from all initial points and in all directions. As a
further evidence of the universality of the SH, we show that it appears as the
scaling limit of the multi-species invariant measures for the totally
asymmetric simple exclusion process (TASEP). This dissertation is adapted from
two joint works with Sepp\"al\"ainen and two joint works with Busani and
Sepp\"al\"ainen.Comment: v2: minor typos corrected, PhD dissertation, University of
Wisconsin--Madison (2023). Contains material adapted from arXiv:2103.01172,
arXiv:2112.10729, arXiv:2203.13242 and arXiv:2211.04651. Chapter 3 gives an
alternate proof of the invariance of the SH shown in arXiv:2203.1324
APPLICABILITY OF FACILITY LAYOUT AND MATERIALS HANDLING MANAGEMENT FOR OPERATIONAL PERFORMANCE OF MANUFACTURING COMPANIES IN NIGERIA: A CASE STUDY
This study examined the application of facility layout and materials handling management for operational performance as case study of a manufacturing company. Three specific objectives were established and data were collected from respondents using an open-ended question survey. The findings revealed that facility layout improves operational performance of production lines, decreases bottleneck rate, minimizes materials handling cost, reduces idle time, increases the efficiency and utilization of labour, equipment and space. Therefore, concluded that facility layout redesign and materials handling management resulted in significant reduction of the following indicators: amount of total workflow, material handling cost, total travel distance of goods, space used for assembly, number of workers, labor cost of workers and the number of stops. We recommended organizations should strictly adhere to management policy on facility layout and computerize their materials management system in line with the global changes for ease to track the movement of materials in the store
Investigating the Effects of Network Dynamics on Quality of Delivery Prediction and Monitoring for Video Delivery Networks
Video streaming over the Internet requires an optimized delivery system given the advances in network architecture, for example, Software Defined Networks. Machine Learning (ML) models have been deployed in an attempt to predict the quality of the video streams. Some of these efforts have considered the prediction of Quality of Delivery (QoD) metrics of the video stream in an effort to measure the quality of the video stream from the network perspective. In most cases, these models have either treated the ML algorithms as black-boxes or failed to capture the network dynamics of the associated video streams.
This PhD investigates the effects of network dynamics in QoD prediction using ML techniques. The hypothesis that this thesis investigates is that ML techniques that model the underlying network dynamics achieve accurate QoD and video quality predictions and measurements. The thesis results demonstrate that the proposed techniques offer performance gains over approaches that fail to consider network dynamics. This thesis results highlight that adopting the correct model by modelling the dynamics of the network infrastructure is crucial to the accuracy of the ML predictions. These results are significant as they demonstrate that improved performance is achieved at no additional computational or storage cost. These techniques can help the network manager, data center operatives and video service providers take proactive and corrective actions for improved network efficiency and effectiveness
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