Date of Award

27-9-2024

Document Type

Doctoral Thesis

Degree Name

Doctor of Philosophy

Department

Technology, Engineering and Mathematics

First Advisor

Professor Joseph Walsh

Second Advisor

Dr Daniel Riordan

Abstract

The numerous uses of cloud, fog, and edge computing in a variety of industries are thoroughly examined in this thesis, with a special emphasis on smart factories, smart cities, and smart agriculture. It starts with an introduction to cloud computing, going over its history, importance in contemporary digital infrastructures, and related security concerns. It emphasizes the value of cloud computing for processing and storing data while addressing issues like guaranteeing low latency applications and possible improvements from container technologies. The conversation then shifts to fog computing, going over its history, designs, and uses. It focuses on how fog computing might improve IoT deployments by putting processing closer to data sources, as well as its advantages and disadvantages. More research is done on edge computing, with a focus on how it might increase privacy and data processing efficiency. Applications such as mobile edge computing and smart agriculture are examined, and integration issues within the IoT ecosystem are examined. This is a detailed analysis of a recently created edge architecture meant to improve the capabilities of IoT devices in the dairy farming sector. This initiative began with an academic-industrial partnership to address latency issues in cloud-edge communication. Deploying over 150,000 devices across 800 farms necessitated a robust and scalable architecture. Insights from preliminary experiments over 18 months were crucial in refining the edge platform, testing various hardware configurations to identify the best features for data synchronization and computational tasks like machine learning. The study then details the practical application and rigorous testing of this novel software architecture for efficient data transport between a cloud database and multiple edge devices, tailored for agricultural settings. Applied in both real-world and simulated farm environments, the system's capability to handle data effectively is demonstrated, showcasing adaptability and resilience under atypical conditions. This highlighted the need for improved data synchronization, leading to the development of the PAIR Mechanism to ensure comprehensive and reliable data synchronization between edge devices and cloud databases. The thesis concludes by revealing significant limitations in existing methods within agricultural settings, which led to the development of the PAIR Mechanism. Extensive testing demonstrated its effectiveness and reliability, ensuring data consistency and operational viability for IIoT technology, highlighting a shift towards anticipating and planning for system failures during the design process.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Access Level

info:eu-repo/semantics/openAccess

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