Managing hybrid industrial IoT enterprise wireless networks

Chen, Yu (2024) Managing hybrid industrial IoT enterprise wireless networks. PhD thesis, University of Glasgow.

Abstract

The advent of the Internet of Things has spurred the deployment of Low Power Wide Area Networks (LPWANs) to facilitate a myriad of commercial and private services. However, while LPWANs offer benefits such as low power consumption and wide coverage, their lower data rates and reliability have constrained their utility in industrial processes and high data rate multimedia applications. This thesis addresses these limitations by exploring the integration of LPWANs into the Fifth Generation (5G) cellular networks and enhancing the management of the hybrid network in terms of server offloading, data volume reduction, and scalability management.

The first part of the thesis surveys the challenges and solutions of LPWAN-5G integration, emphasizing hybrid architectures, security, mobility, interoperability, and coexistence with other wireless technologies. Building upon this, the second part of the thesis designs and implements a Long Range Wide Area Network (LoRaWAN)5G integrated network with a collaborative radio access network and a converged core network. The integrated network has been deployed for heating monitoring, demonstrating the feasibility, flexibility, and cost-effectiveness of the hybrid network. The implemented LoRaWAN-5G integrated network has attracted new funding from the European Space Agency for a telemedicine project.

The third part of the thesis proposes a Long Range (LoRa) mesh-5G integrated network to address coverage gaps in railway operations, leveraging 5G for backhaul and computing while extending coverage with LoRa mesh. The integration of edge computing and a cloud-edge-terminal collaborative architecture enhances network efficiency and timeliness, as validated in a proof-of-concept deployment for trackside weather monitoring. The LoRa mesh-based trackside weather monitoring system has been adopted by Network Rail for potential widespread use.

Lastly, the fourth part of the thesis investigates the reliability and scalability of the LoRa mesh-5G integrated network for monitoring linear infrastructure, proposing a deployment strategy and novel 5G-enabled routing algorithm to optimize node placement and enhance network performance within duty cycle regulations. Moreover, a simulation tool has been developed to validate these findings, offering insights into practical deployments.

Collectively, this thesis contributes to the understanding and advancement of LPWAN-5G integration, offering solutions for diverse industrial and infrastructure monitoring applications, e.g., trackside weather monitoring. This thesis serves as a comprehensive exploration of the integration and optimization of LPWAN technologies within 5G networks, paving the way for enhanced Internet of Things deployments in various domains.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Additional Information: Supported by funding from 5G3i Ltd. Corporation.
• Subjects: T Technology > T Technology (General)
• Colleges/Schools: College of Science and Engineering > School of Engineering
• Funder's Name: 5G3i Ltd. Corporation
• Supervisor's Name: Sambo, Dr. Yusuf, Onireti, Dr. Oluwakayode and Imran, Professor Muhammad
• Date of Award: 2024
• Depositing User: Theses Team
• Unique ID: glathesis:2024-84329
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 22 May 2024 08:28
• Last Modified: 22 May 2024 11:15
• Thesis DOI: 10.5525/gla.thesis.84329
• URI: https://theses.gla.ac.uk/id/eprint/84329

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