List of PhD dissertations
2024
1.
Sabari Nathan Anbalagan
SPECTRUM: Towards Self-aware Industrial IoT Systems PhD Thesis
2024, ISBN: 978-90-365-6335-2.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Fourth Industrial Revolution, Internet of Things, Wireless Communication
@phdthesis{Anbalagan2024,
title = {SPECTRUM: Towards Self-aware Industrial IoT Systems},
author = {Sabari Nathan Anbalagan},
doi = {https://doi.org/10.3990/1.9789036563352},
isbn = {978-90-365-6335-2},
year = {2024},
date = {2024-12-03},
urldate = {2024-12-03},
abstract = {The Fourth Industrial Revolution (4IR) has sparked remarkable technological advancements, particularly in integrating the Internet of Things (IoT) into manufacturing industries, known as the Industrial Internet of Things (IIoT). This integration holds potential for revolutionizing industrial processes and boosting human progress through advanced applications within factory settings.
However, the diverse Quality of Service (QoS) requirements of 4IR applications, including data rate, latency, reliability, power efficiency, scalability, and mobility, present significant challenges. The IIoT network must support these diverse requirements while operating in harsh and dynamic industrial indoor environments characterized by metallic infrastructure, thick concrete pillars, and complex pipe networks. Additionally, environmental fluctuations like temperature and humidity, process dynamics such as spinning and smelting, and the mobility of operators and indoor vehicles contribute to the dynamic nature of these environments. Achieving the diverse QoS requirements in such challenging conditions necessitates intelligent and versatile solutions.
The latest telecommunications standard, 5G, holds promise as a solution to these challenges. This thesis addresses these challenges by focusing on the optimization of industrial use cases for 5G-based IIoT systems. It begins with an exploration of the significance and applications of 4IR in manufacturing industries, identifying the potential of 5G technology augmented by varying levels of Artificial Intelligence (AI) to meet the diverse demands of IIoT networks. The problem statement emphasizes the necessity for intelligent methodologies that adapt to the dynamic nature of industrial environments while supporting a wide range of industrial applications.
The thesis proposes a multifaceted approach to tackle these challenges, incorporating varying levels of context awareness within IIoT systems. Chapters delve into the evolution of IIoT systems, presenting a framework for assessing system maturity and providing practical guidance for implementation. The development of heuristics-based, coarse-grained, and fine-grained Channel Quality Prediction (CQP) techniques is explored, offering insights into optimizing wireless communication parameters for diverse 5G use cases. Simulations and analyses demonstrate the efficacy of these methodologies in enhancing throughput, resource utilization, and power efficiency across various industrial scenarios.
In conclusion, this thesis contributes to the advancement of wireless communication optimization in industrial settings, offering practical solutions and insights for realizing the potential of 4IR initiatives. By addressing a spectrum of challenges, requirements and use case scenarios, the research offers insights for future 5G and 6G releases.},
keywords = {Artificial Intelligence, Fourth Industrial Revolution, Internet of Things, Wireless Communication},
pubstate = {published},
tppubtype = {phdthesis}
}
The Fourth Industrial Revolution (4IR) has sparked remarkable technological advancements, particularly in integrating the Internet of Things (IoT) into manufacturing industries, known as the Industrial Internet of Things (IIoT). This integration holds potential for revolutionizing industrial processes and boosting human progress through advanced applications within factory settings.
However, the diverse Quality of Service (QoS) requirements of 4IR applications, including data rate, latency, reliability, power efficiency, scalability, and mobility, present significant challenges. The IIoT network must support these diverse requirements while operating in harsh and dynamic industrial indoor environments characterized by metallic infrastructure, thick concrete pillars, and complex pipe networks. Additionally, environmental fluctuations like temperature and humidity, process dynamics such as spinning and smelting, and the mobility of operators and indoor vehicles contribute to the dynamic nature of these environments. Achieving the diverse QoS requirements in such challenging conditions necessitates intelligent and versatile solutions.
The latest telecommunications standard, 5G, holds promise as a solution to these challenges. This thesis addresses these challenges by focusing on the optimization of industrial use cases for 5G-based IIoT systems. It begins with an exploration of the significance and applications of 4IR in manufacturing industries, identifying the potential of 5G technology augmented by varying levels of Artificial Intelligence (AI) to meet the diverse demands of IIoT networks. The problem statement emphasizes the necessity for intelligent methodologies that adapt to the dynamic nature of industrial environments while supporting a wide range of industrial applications.
The thesis proposes a multifaceted approach to tackle these challenges, incorporating varying levels of context awareness within IIoT systems. Chapters delve into the evolution of IIoT systems, presenting a framework for assessing system maturity and providing practical guidance for implementation. The development of heuristics-based, coarse-grained, and fine-grained Channel Quality Prediction (CQP) techniques is explored, offering insights into optimizing wireless communication parameters for diverse 5G use cases. Simulations and analyses demonstrate the efficacy of these methodologies in enhancing throughput, resource utilization, and power efficiency across various industrial scenarios.
In conclusion, this thesis contributes to the advancement of wireless communication optimization in industrial settings, offering practical solutions and insights for realizing the potential of 4IR initiatives. By addressing a spectrum of challenges, requirements and use case scenarios, the research offers insights for future 5G and 6G releases.
However, the diverse Quality of Service (QoS) requirements of 4IR applications, including data rate, latency, reliability, power efficiency, scalability, and mobility, present significant challenges. The IIoT network must support these diverse requirements while operating in harsh and dynamic industrial indoor environments characterized by metallic infrastructure, thick concrete pillars, and complex pipe networks. Additionally, environmental fluctuations like temperature and humidity, process dynamics such as spinning and smelting, and the mobility of operators and indoor vehicles contribute to the dynamic nature of these environments. Achieving the diverse QoS requirements in such challenging conditions necessitates intelligent and versatile solutions.
The latest telecommunications standard, 5G, holds promise as a solution to these challenges. This thesis addresses these challenges by focusing on the optimization of industrial use cases for 5G-based IIoT systems. It begins with an exploration of the significance and applications of 4IR in manufacturing industries, identifying the potential of 5G technology augmented by varying levels of Artificial Intelligence (AI) to meet the diverse demands of IIoT networks. The problem statement emphasizes the necessity for intelligent methodologies that adapt to the dynamic nature of industrial environments while supporting a wide range of industrial applications.
The thesis proposes a multifaceted approach to tackle these challenges, incorporating varying levels of context awareness within IIoT systems. Chapters delve into the evolution of IIoT systems, presenting a framework for assessing system maturity and providing practical guidance for implementation. The development of heuristics-based, coarse-grained, and fine-grained Channel Quality Prediction (CQP) techniques is explored, offering insights into optimizing wireless communication parameters for diverse 5G use cases. Simulations and analyses demonstrate the efficacy of these methodologies in enhancing throughput, resource utilization, and power efficiency across various industrial scenarios.
In conclusion, this thesis contributes to the advancement of wireless communication optimization in industrial settings, offering practical solutions and insights for realizing the potential of 4IR initiatives. By addressing a spectrum of challenges, requirements and use case scenarios, the research offers insights for future 5G and 6G releases.