2,983 research outputs found
Towards rapid modeling and prototyping of indoor and outdoor monitoring applications
Nowadays, the capability to remotely monitor indoor and outdoor environments would allow to reduce energy consumption and improve the overall management and users’ experience of network application systems. The most known solutions adopting remote control are related to domotics (e.g., smart homes and industry 4.0 applications). An important stimulus for the development of such smart approaches is the growth of the Internet of Things (IoT) technologies and the increasing investment in the development of green houses, buildings, and, in general, heterogeneous environments. While the benefits for the humans and the environment are evident, a pervasive adoption and distribution of remote monitoring solutions are hindered by the following issue: modeling, designing, prototyping, and further developing the remote applications and underlying architecture require a certain amount of time. Moreover, such systems must be often customized on the basis of the need of the specific domain and involved entities. For such reasons, in this paper, we provide the experience made in addressing some relevant indoor and outdoor case studies through IoT-targeted tools, technologies and protocols, highlighting the advantages and disadvantages of the considered solutions as well as insights that can be useful for future practitioners
Digitalization and Development
This book examines the diffusion of digitalization and Industry 4.0 technologies in Malaysia by focusing on the ecosystem critical for its expansion. The chapters examine the digital proliferation in major sectors of agriculture, manufacturing, e-commerce and services, as well as the intermediary organizations essential for the orderly performance of socioeconomic agents.
The book incisively reviews policy instruments critical for the effective and orderly development of the embedding organizations, and the regulatory framework needed to quicken the appropriation of socioeconomic synergies from digitalization and Industry 4.0 technologies. It highlights the importance of collaboration between government, academic and industry partners, as well as makes key recommendations on how to encourage adoption of IR4.0 technologies in the short- and long-term.
This book bridges the concepts and applications of digitalization and Industry 4.0 and will be a must-read for policy makers seeking to quicken the adoption of its technologies
MECHANICAL ENERGY HARVESTER FOR POWERING RFID SYSTEMS COMPONENTS: MODELING, ANALYSIS, OPTIMIZATION AND DESIGN
Finding alternative power sources has been an important topic of study worldwide. It is vital to find substitutes for finite fossil fuels. Such substitutes may be termed renewable energy sources and infinite supplies. Such limitless sources are derived from ambient energy like wind energy, solar energy, sea waves energy; on the other hand, smart cities megaprojects have been receiving enormous amounts of funding to transition our lives into smart lives. Smart cities heavily rely on smart devices and electronics, which utilize small amounts of energy to run. Using batteries as the power source for such smart devices imposes environmental and labor cost issues. Moreover, in many cases, smart devices are in hard-to-access places, making accessibility for disposal and replacement difficult. Finally, battery waste harms the environment.
To overcome these issues, vibration-based energy harvesters have been proposed and implemented. Vibration-based energy harvesters convert the dynamic or kinetic energy which is generated due to the motion of an object into electric energy. Energy transduction mechanisms can be delivered based on piezoelectric, electromagnetic, or electrostatic methods; the piezoelectric method is generally preferred to the other methods, particularly if the frequency fluctuations are considerable. In response, piezoelectric vibration-based energy harvesters (PVEHs), have been modeled and analyzed widely. However, there are two challenges with PVEH: the maximum amount of extractable voltage and the effective (operational) frequency bandwidth are often insufficient. In this dissertation, a new type of integrated multiple system comprised of a cantilever and spring-oscillator is proposed to improve and develop the performance of the energy harvester in terms of extractable voltage and effective frequency bandwidth. The new energy harvester model is proposed to supply sufficient energy to power low-power electronic devices like RFID components. Due to the temperature fluctuations, the thermal effect over the performance of the harvester is initially studied. To alter the resonance frequency of the harvester structure, a rotating element system is considered and analyzed. In the analytical-numerical analysis, Hamilton’s principle along with Galerkin’s decomposition approach are adopted to derive the governing equations of the harvester motion and corresponding electric circuit. It is observed that integration of the spring-oscillator subsystem alters the boundary condition of the cantilever and subsequently reforms the resulting characteristic equation into a more complicated nonlinear transcendental equation. To find the resonance frequencies, this equation is solved numerically in MATLAB. It is observed that the inertial effects of the oscillator rendered to the cantilever via the restoring force effects of the spring significantly alter vibrational features of the harvester. Finally, the voltage frequency response function is analytically and numerically derived in a closed-from expression. Variations in parameter values enable the designer to mutate resonance frequencies and mode shape functions as desired. This is particularly important, since the generated energy from a PVEH is significant only if the excitation frequency coming from an external source matches the resonance (natural) frequency of the harvester structure. In subsequent sections of this work, the oscillator mass and spring stiffness are considered as the design parameters to maximize the harvestable voltage and effective frequency bandwidth, respectively. For the optimization, a genetic algorithm is adopted to find the optimal values. Since the voltage frequency response function cannot be implemented in a computer algorithm script, a suitable function approximator (regressor) is designed using fuzzy logic and neural networks. The voltage function requires manual assistance to find the resonance frequency and cannot be done automatically using computer algorithms. Specifically, to apply the numerical root-solver, one needs to manually provide the solver with an initial guess. Such an estimation is accomplished using a plot of the characteristic equation along with human visual inference. Thus, the entire process cannot be automated. Moreover, the voltage function encompasses several coefficients making the process computationally expensive. Thus, training a supervised machine learning regressor is essential. The trained regressor using adaptive-neuro-fuzzy-inference-system (ANFIS) is utilized in the genetic optimization procedure. The optimization problem is implemented, first to find the maximum voltage and second to find the maximum widened effective frequency bandwidth, which yields the optimal oscillator mass value along with the optimal spring stiffness value. As there is often no control over the external excitation frequency, it is helpful to design an adaptive energy harvester. This means that, considering a specific given value of the excitation frequency, energy harvester system parameters (oscillator mass and spring stiffness) need to be adjusted so that the resulting natural (resonance) frequency of the system aligns with the given excitation frequency. To do so, the given excitation frequency value is considered as the input and the system parameters are assumed as outputs which are estimated via the neural network fuzzy logic regressor. Finally, an experimental setup is implemented for a simple pure cantilever energy harvester triggered by impact excitations. Unlike the theoretical section, the experimental excitation is considered to be an impact excitation, which is a random process. The rationale for this is that, in the real world, the external source is a random trigger. Harmonic base excitations used in the theoretical chapters are to assess the performance of the energy harvester per standard criteria. To evaluate the performance of a proposed energy harvester model, the input excitation type consists of harmonic base triggers. In summary, this dissertation discusses several case studies and addresses key issues in the design of optimized piezoelectric vibration-based energy harvesters (PVEHs). First, an advanced model of the integrated systems is presented with equation derivations. Second, the proposed model is decomposed and analyzed in terms of mechanical and electrical frequency response functions. To do so, analytic-numeric methods are adopted. Later, influential parameters of the integrated system are detected. Then the proposed model is optimized with respect to the two vital criteria of maximum amount of extractable voltage and widened effective (operational) frequency bandwidth. Corresponding design (influential) parameters are found using neural network fuzzy logic along with genetic optimization algorithms, i.e., a soft computing method. The accuracy of the trained integrated algorithms is verified using the analytical-numerical closed-form expression of the voltage function. Then, an adaptive piezoelectric vibration-based energy harvester (PVEH) is designed. This final design pertains to the cases where the excitation (driving) frequency is given and constant, so the desired goal is to match the natural frequency of the system with the given driving frequency. In this response, a regressor using neural network fuzzy logic is designed to find the proper design parameters. Finally, the experimental setup is implemented and tested to report the maximum voltage harvested in each test execution
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
IoT Transmission Technologies for Distributed Measurement Systems in Critical Environments
Distributed measurement systems are spread in the most diverse application scenarios, and Internet of Things (IoT) transmission equipment is usually the enabling technologies for such measurement systems that need to feature wireless connectivity to ensure pervasiveness. Because wireless measurement systems have been deployed for the last years even in critical environments, assessing transmission technologies performances in such contexts is fundamental. Indeed, they are the most challenging ones for wireless data transmission due to their intrinsic attenuation capabilities.
Several scenarios in which measurement systems can be deployed are analysed. Firstly, marine contexts are treated by considering above-the-sea wireless links. Such setting can be experienced in whichever application requiring remote monitoring of facilities and assets that are offshore installed. Some instances are offshore sea farming plants, or remote video monitoring systems installed on seamark buoys. Secondly, wireless communications taking place from the underground to the aboveground are covered. This scenario is typical of precision agriculture applications, where the accurate measurement of underground physical parameters is needed to be remotely sent to optimise crops reducing the wastefulness of fundamental resources (e.g., irrigation water). Thirdly, wireless communications occurring from the underwater to the abovewater are addressed. Such situation is inevitable for all those infrastructures monitoring conservation status of underwater species like algae, seaweeds and reef. Then, wireless links happening traversing metal surfaces and structures are tackled. Such context is commonly encountered in asset tracking and monitoring (e.g., containers), or in smart metering applications (e.g., utility meters). Lastly, sundry harsh environments that are typical of industrial monitoring (e.g., vibrating machineries, harsh temperature and humidity rooms, corrosive atmospheres) are tested to validate pervasive measurement infrastructures even in such contexts that are usually experienced in Industrial Internet of Things (IIoT) applications. The performances of wireless measurement systems in such scenarios are tested by sorting out ad-hoc measurement campaigns. Finally, IoT measurement infrastructures respectively deployed in above-the-sea and underground-to-aboveground settings are described to provide real applications in which such facilities can be effectively installed. Nonetheless, the aforementioned application scenarios are only some amid their sundry variety. Indeed, nowadays distributed pervasive measurement systems have to be thought in a broad way, resulting in countless instances: predictive maintenance, smart healthcare, smart cities, industrial monitoring, or smart agriculture, etc.
This Thesis aims at showing distributed measurement systems in critical environments to set up pervasive monitoring infrastructures that are enabled by IoT transmission technologies. At first, they are presented, and then the harsh environments are introduced, along with the relative theoretical analysis modelling path loss in such conditions. It must be underlined that this Thesis aims neither at finding better path loss models with respect to the existing ones, nor at improving them. Indeed, path loss models are exploited as they are, in order to derive estimates of losses to understand the effectiveness of the deployed infrastructure. In fact, some transmission tests in those contexts are described, along with providing examples of these types of applications in the field, showing the measurement infrastructures and the relative critical environments serving as deployment sites. The scientific relevance of this Thesis is evident since, at the moment, the literature lacks a comparative study like this, showing both transmission performances in critical environments, and the deployment of real IoT distributed wireless measurement systems in such contexts
Design and Real-World Evaluation of Dependable Wireless Cyber-Physical Systems
The ongoing effort for an efficient, sustainable, and automated interaction between humans, machines, and our environment will make cyber-physical systems (CPS) an integral part of the industry and our daily lives. At their core, CPS integrate computing elements, communication networks, and physical processes that are monitored and controlled through sensors and actuators. New and innovative applications become possible by extending or replacing static and expensive cable-based communication infrastructures with wireless technology. The flexibility of wireless CPS is a key enabler for many envisioned scenarios, such as intelligent factories, smart farming, personalized healthcare systems, autonomous search and rescue, and smart cities.
High dependability, efficiency, and adaptivity requirements complement the demand for wireless and low-cost solutions in such applications. For instance, industrial and medical systems should work reliably and predictably with performance guarantees, even if parts of the system fail. Because emerging CPS will feature mobile and battery-driven devices that can execute various tasks, the systems must also quickly adapt to frequently changing conditions. Moreover, as applications become ever more sophisticated, featuring compact embedded devices that are deployed densely and at scale, efficient designs are indispensable to achieve desired operational lifetimes and satisfy high bandwidth demands.
Meeting these partly conflicting requirements, however, is challenging due to imperfections of wireless communication and resource constraints along several dimensions, for example, computing, memory, and power constraints of the devices. More precisely, frequent and correlated message losses paired with very limited bandwidth and varying delays for the message exchange significantly complicate the control design. In addition, since communication ranges are limited, messages must be relayed over multiple hops to cover larger distances, such as an entire factory. Although the resulting mesh networks are more robust against interference, efficient communication is a major challenge as wireless imperfections get amplified, and significant coordination effort is needed, especially if the networks are dynamic.
CPS combine various research disciplines, which are often investigated in isolation, ignoring their complex interaction. However, to address this interaction and build trust in the proposed solutions, evaluating CPS using real physical systems and wireless networks paired with formal guarantees of a system’s end-to-end behavior is necessary. Existing works that take this step can only satisfy a few of the abovementioned requirements. Most notably, multi-hop communication has only been used to control slow physical processes while providing no guarantees. One of the reasons is that the current communication protocols are not suited for dynamic multi-hop networks.
This thesis closes the gap between existing works and the diverse needs of emerging wireless CPS. The contributions address different research directions and are split into two parts. In the first part, we specifically address the shortcomings of existing communication protocols and make the following contributions to provide a solid networking foundation:
• We present Mixer, a communication primitive for the reliable many-to-all message exchange in dynamic wireless multi-hop networks. Mixer runs on resource-constrained low-power embedded devices and combines synchronous transmissions and network coding for a highly scalable and topology-agnostic message exchange. As a result, it supports mobile nodes and can serve any possible traffic patterns, for example, to efficiently realize distributed control, as required by emerging CPS applications.
• We present Butler, a lightweight and distributed synchronization mechanism with formally guaranteed correctness properties to improve the dependability of synchronous transmissions-based protocols. These protocols require precise time synchronization provided by a specific node. Upon failure of this node, the entire network cannot communicate. Butler removes this single point of failure by quickly synchronizing all nodes in the network without affecting the protocols’ performance.
In the second part, we focus on the challenges of integrating communication and various control concepts using classical time-triggered and modern event-based approaches. Based on the design, implementation, and evaluation of the proposed solutions using real systems and networks, we make the following contributions, which in many ways push the boundaries of previous approaches:
• We are the first to demonstrate and evaluate fast feedback control over low-power wireless multi-hop networks. Essential for this achievement is a novel co-design and integration of communication and control. Our wireless embedded platform tames the imperfections impairing control, for example, message loss and varying delays, and considers the resulting key properties in the control design. Furthermore, the careful orchestration of control and communication tasks enables real-time operation and makes our system amenable to an end-to-end analysis. Due to this, we can provably guarantee closed-loop stability for physical processes with linear time-invariant dynamics.
• We propose control-guided communication, a novel co-design for distributed self-triggered control over wireless multi-hop networks. Self-triggered control can save energy by transmitting data only when needed. However, there are no solutions that bring those savings to multi-hop networks and that can reallocate freed-up resources, for example, to other agents. Our control system informs the communication system of its transmission demands ahead of time so that communication resources can be allocated accordingly. Thus, we can transfer the energy savings from the control to the communication side and achieve an end-to-end benefit.
• We present a novel co-design of distributed control and wireless communication that resolves overload situations in which the communication demand exceeds the available bandwidth. As systems scale up, featuring more agents and higher bandwidth demands, the available bandwidth will be quickly exceeded, resulting in overload. While event-triggered control and self-triggered control approaches reduce the communication demand on average, they cannot prevent that potentially all agents want to communicate simultaneously. We address this limitation by dynamically allocating the available bandwidth to the agents with the highest need. Thus, we can formally prove that our co-design guarantees closed-loop stability for physical systems with stochastic linear time-invariant dynamics.:Abstract
Acknowledgements
List of Abbreviations
List of Figures
List of Tables
1 Introduction
1.1 Motivation
1.2 Application Requirements
1.3 Challenges
1.4 State of the Art
1.5 Contributions and Road Map
2 Mixer: Efficient Many-to-All Broadcast in Dynamic Wireless Mesh Networks
2.1 Introduction
2.2 Overview
2.3 Design
2.4 Implementation
2.5 Evaluation
2.6 Discussion
2.7 Related Work
3 Butler: Increasing the Availability of Low-Power Wireless Communication Protocols
3.1 Introduction
3.2 Motivation and Background
3.3 Design
3.4 Analysis
3.5 Implementation
3.6 Evaluation
3.7 Related Work
4 Feedback Control Goes Wireless: Guaranteed Stability over Low-Power Multi-Hop Networks
4.1 Introduction
4.2 Related Work
4.3 Problem Setting and Approach
4.4 Wireless Embedded System Design
4.5 Control Design and Analysis
4.6 Experimental Evaluation
4.A Control Details
5 Control-Guided Communication: Efficient Resource Arbitration and Allocation in Multi-Hop Wireless Control Systems
5.1 Introduction
5.2 Problem Setting
5.3 Co-Design Approach
5.4 Wireless Communication System Design
5.5 Self-Triggered Control Design
5.6 Experimental Evaluation
6 Scaling Beyond Bandwidth Limitations: Wireless Control With Stability Guarantees Under Overload
6.1 Introduction
6.2 Problem and Related Work
6.3 Overview of Co-Design Approach
6.4 Predictive Triggering and Control System
6.5 Adaptive Communication System
6.6 Integration and Stability Analysis
6.7 Testbed Experiments
6.A Proof of Theorem 4
6.B Usage of the Network Bandwidth for Control
7 Conclusion and Outlook
7.1 Contributions
7.2 Future Directions
Bibliography
List of Publication
Підвищення продуктивності низькоенергетичних безпроводових каналів зв’язку сенсорних телекомунікаційних систем
Шмігель Б. О. Підвищення продуктивності низькоенергетичних
безпроводових каналів зв’язку сенсорних телекомунікаційних систем. –
Кваліфікаційна наукова праця на правах рукопису.
Дисертація на здобуття наукового ступеня доктора філософії за
спеціальністю 172 – Телекомунікації та радіотехніка. – Національний технічний
університет України «Київський політехнічний інститут імені Ігоря
Сікорського», Київ, 2022.
В сучасному світі, життя людини нерозривно пов’язане з обміном
інформацією. Надзвичайно швидкими темпами зростають і вимоги користувачів
до якості, швидкості і безпеки прийому та передачі даних, зберігаючи при цьому
можливість вільного пересування. Задовольняти ці потреби – власне і є ціллю
безпроводових мереж.
Сенсорні мережі займають ключову роль у разі необхідності оперативного
розгортання, мобільності, гнучкості організації мережі і широті можливих
додатків, у багатьох випадках будучи єдиним економічно виправданим
рішенням. Однією із ключових задач забезпечення функціонування сенсорної
мережі є забезпечення надійного та продуктивного передавання інформації в
умовах обмежених ресурсів, зокрема, енергетичних.
Враховуючи розмір сенсора, основною вимогою до сенсорних мереж – є
забезпечення низького енергоспоживання та достовірного прийому.
Автономність роботи залежить від енергії, що споживається вузлами системи.
Для досягнення достовірного зв’язку, мають місце наступні компоненти:
- достатня енергетика (обмежена для сенсорних мереж)
- мала швидкість передачі (обмежена вимогами до сенсорних мереж)
- інструменти для підтримання достовірності (завадостійке
кодування)
Під низькою енергетикою будемо вважати відношення сигнал-шум
h
2 <10. Параметр h
2
є результатом взаємодії трьох незалежних параметрів:
- потужністю сигналу в точці прийому
- спектральної щільності шуму
- швидкості передачі символів
Продуктивність – це фактична швидкість передачі інформації джерела.
Ключ до вирішення задачі отримання максимальної продуктивності лежить у
площині теорії інформації, засновником якої є К. Шеннон.
Інструментом досягнення максимальної продуктивності, як міри
наближення швидкості передачі повідомлень джерела до границі Шеннона є
вибір оптимальних сигнально-кодових конструкцій, які дозволяють передавати
повідомлення з максимально можливою швидкістю і заданою якістю.
Міра наближення продуктивності каналу зв’язку до його пропускної
здатності характеризує інформаційну ефективність системи передачі інформації.
Метою роботи є синтез сигналів, що максимально наближають
продуктивність каналу зв’язку до пропускної здатності при обмежених ресурсах
каналу.
У роботі досліджена актуальна задача підвищення продуктивності
низькоенергетичних безпроводових каналів зв’язку. На відміну від традиційних
систем безпроводового зв’язку, сенсорна мережа включає велику кількість
пристроїв, які повинні передавати інформацію до базової станції. Сенсорні вузли
можуть встановлюватися стаціонарно або мати можливість довільно
пересуватися в певному просторі, тому вони повинні бути автономними,
самоорганізованими та не потребують установки. Область покриття такої мережі
вкрай обмежена і може досягати десятки та сотні метрів. Тому однією з головних
умов до такої мережі – це забезпечення мінімального енергоспоживання та
достовірного прийому в умовах низької енергетики. Основною задачею при
побудови сенсорної мережі є достовірна оцінка енергетичних характеристик
безпроводового каналу зв’язку.
Виходячи із вищесказаного, для розгортання сенсорних мереж актуальним
питанням є ефективне проектування сенсорної мережі: необхідна кількість
пристроїв, їх характеристики, розміщення, енергетичні характеристики каналів,
траси розповсюдження, тощо.
Для вирішення обмежень ресурсу каналу зв’язку, перспективним являється
пошук нових методів передачі інформації, вибору ефективного виду модуляції
та завадостійкого кодування.
Основним інструментом для передачі інформації є сигнали
багатопозиційної маніпуляції. Вибір поєднання типу модуляції і швидкості
завадостійкого коду, забезпечує максимально можливу ефективність,
забезпечуючи відповідну надійність каналу зв’язку. В якості розглянутих
сигналів обрано сигнали багатопозиційної маніпуляції BPSK, QPSK, QAM16.
Високошвидкісні види модуляції не розглядаються, так як сенсорна мережа не
передбачує передачу великих масивів інформації, а також має обмежену енергію
сигналу.
Широкосмугові сигнали є одним з відомих методів для підвищення
завадостійкості каналу, але властивості таких сигналів в умовах обмеженого
ресурсу та енергії сигналу не досліджені. Для визначення найбільш
оптимального способу передачі сигналів в умовах низької енергетики, проведено
дослідження властивостей ШСС на основі моделей оцінки якості каналів зв’язку,
а також порівняння характеристики завадостійкості з ВСС та еквівалентною
енергією сигналу.
Для визначення максимальної продуктивності передачі в безпроводових
низькоенергетичних каналах зв’язку, необхідно дослідити показники
продуктивності використання сигналів заданого виду модуляції та порівняти їх
з широкосмуговими сигналами з різними значеннями бази сигналу B. Виявилося,
що широкосмугові сигнали не забезпечують кращої достовірності в порівнянні з
вузькосмуговими при однаковій потужності випромінювання та способі
обробки.
Класичні формули для оцінки завадостійкості багатопозиційних сигналів
достовірні для високої енергетики, однак для h
2 - 0 не є точними. Тому для
точного визначення точної достовірності прийому для таких умов, пропонується
використання векторно-фазового методу. Векторно-фазовий метод дозволяє
отримати точні розрахунки при будь-якій енергетиці, на відміну від формул
Прокіса, що можуть використовуватись тільки для високої енергетики.
Загальним підсумком дослідження є оцінка продуктивності СКК, яка
дозволяє в каналі з заданими частотно-енергетичними параметрами визначити
ефективність використання визначеного виду модуляції та кодування за
критерієм наближення до границі Шеннона, або максимуму інформаційної
ефективності при заданій достовірності сигналу в точці прийому. Методика
дозволяє оцінити ефективність використання ресурсів каналів зв’язку з
багатопозиційною маніпуляцією та завадостійким кодуванням, а також
кількісно оцінити витрати на реалізацію заходів щодо підвищення достовірності
або продуктивності у вимірі запропонованих показників.
Дослідження питань підвищення продуктивності низькоенергетичних
безпроводових каналів зв’язку забезпечить побудову сенсорних
телекомунікаційних систем для успішного виконання поставлених завдань.
Новими в дисертації є наступні результати:
1. Вдосконалено використання векторно-фазового методу для
визначення завадостійкості багатопозиційних сигналів в умовах
низької енергетики. Класичні формули Прокіса не є точними для
низької енергетики.
2. Вдосконалено методику синтезу сигналу, яка дозволяє знайти
екстремум продуктивності каналу зв’язку та наблизити до його
пропускної здатності – границі Шеннона.
3. Вдосконалено методику оцінки ефективності використання ресурсів
каналу зв’язку.Shmihel B. Increasing the performance of low-power wireless communication
channels of sensor telecommunication systems – Scientific qualification work on the
rights of the manuscript.
Dissertation for the Doctor of Philosophy degree in technical sciences, specialty
172 - Telecommunications and radio engineering. – National Technical University of
Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv, 2022.
In the modern world, human life is inextricably connected with the exchange of
information. User requirements for the quality, speed, and security of receiving and
transmitting data are also growing extremely fast while maintaining the possibility of
mobility. Satisfying these needs is the purpose of wireless networks.
Sensor networks play a key role in the need for rapid deployment, mobility,
networking flexibility and a variety of possible applications, in many cases being the
only cost-effective solution. One of the key tasks of functioning in the sensor network
is to ensure reliable and efficient data transmission in conditions of limited resources.
Given the size of the sensor, the main requirement for sensor networks is to
provide low power consumption and reliable reception. Their autonomy depends on
the energy consumed by the nodes of the system.
To achieve the necessary reliability, the following components take place:
- sufficient energy (limited for sensor networks)
- low transmission rate (limited by requirements for sensor networks)
- tools for maintaining reliability (noise-correcting coding)
By low energy, we will consider the signal-to-noise ratio h
2 <10. Parameter h
2
is
the result of the interaction of three independent parameters:
- signal strength at the receiving point
- noise spectral density
- symbol rate
Performance is the actual speed of information transfer of the source. The key to
solving the problem of obtaining maximum performance lies in the plane of the theory
of information, founded by K. Shannon.
The tool for achieving maximum performance, as the approach of the channel
performance to its bandwidth, is the choice of optimal signal-code constructions that
allow messages to be transmitted at the highest possible speed and with a given quality.
The approach of the channel performance to its bandwidth characterizes the
information efficiency of the information transmission system.
The research aims to synthesize signals that bring the channel performance as
close as possible to the throughput with limited channel resources.
The research contains the actual problem of increasing productivity in lowenergy wireless communication channels. Unlike traditional wireless communication
systems, a sensor network includes many devices that should transmit information to a
base station. Sensor nodes can be placed permanently or be able to move in a certain
space, so they must be autonomous, self-organized and do not require installation. The
coverage area of such a network is extremely limited and can reach tens and hundreds
of meters. The main task in building a sensor network is a reliable assessment of the
energy characteristics of a wireless communication channel.
Based on the foregoing, for the deployment of sensor networks, an important
issue is the effective design of a sensor network: the required number of devices, their
characteristics, placement, energy characteristics of channels, propagation paths, etc.
To solve the limitations of the communication channel, it is promising to search
for new methods for transmitting information, choosing an effective type of modulation
and error-correcting coding.
The basic tool for transmitting information is the signals of multi-position
modulation. The choice of a combination of modulation type and error code rate
provides the highest possible efficiency while providing adequate reliability to the
communication channel. The multi-position modulation signals BPSK, QPSK and
QAM16 were chosen as the considered signals. High-speed modulation types are not
considered, since the sensor network does not involve the transmission of large
amounts of information and has limited signal energy.
Broadband signals are one of the well-known methods for improving the noise
immunity of a channel, but the properties of such signals under conditions of limited
resources and signal energy have not been studied. To determine the most optimal
method of signal transmission in low energy conditions, research on the properties of
the narrowband signals was carried out based on models for assessing the quality of
communication channels and comparing the noise immunity characteristic of wideband
signals with equivalent signal energy.
To determine the maximum transmission performance in wireless low-energy
communication channels, it is necessary to investigate the performance indicators of a
given type of modulation signals and compare them with wideband signals with
different values of the signal base B. It turned out that wideband signals do not provide
better reliability compared to narrowband signals with the same transmission power
and processing method.
The classical formulas for estimating the noise immunity of multi-position
signals are accurate for high energy, but for h2 - 0 they are not accurate. Therefore, to
determine the exact reliability for such conditions, it is proposed to use the vectorphase method. The vector-phase method helps to obtain accurate calculations for any
energy, in contrast to the Prokis formulas, which can only be used for high energy.
The overall result of the research is performance evaluation of signal-code
construction, that allows determining the efficiency of using a certain type of
modulation and coding in a channel with given frequency and energy parameters
according to the criterion of maximum approach to the Shannon bound, or the
maximum of information efficiency for given signal reliability. Using this technique,
it possible to evaluate the efficiency of using the resources of communication channels
with multi-position modulation and error-correcting coding and calculate the costs of
implementing measures to improve the reliability or performance of the proposed
indicators.
Research on improving the performance of low-energy wireless communication
channels will help to build sensor telecommunication systems for the successful
completion of tasks.
Research contains the following new results:
1. The use of the vector-phase method for determining the noise immunity
of multi-position signals under low energy conditions has been improved.
The classic Prokis formulas are not accurate for low energy.
2. The method of signal synthesis has been improved, which makes it is
possible to find the extremum of the communication channel performance
and bring it closer to its capacity – the Shannon bounds.
3. The methodology for evaluating the effectiveness of the communication
channel resources has been improved
- …