Intelligent Context-aware Fog Node Discovery and Trust-based Fog Node Selection

Bukhari, Afnan Abdulrahman

Intelligent Context-aware Fog Node Discovery and Trust-based Fog Node Selection

Bukhari, Afnan Abdulrahman

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Publication Type:: Thesis
Issue Date:: 2024

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Field	Value	Language
dc.contributor.author	Bukhari, Afnan Abdulrahman
dc.date.accessioned	2024-06-18T23:18:05Z
dc.date.available	2024-06-18T23:18:05Z
dc.date.issued	2024
dc.identifier.uri	http://hdl.handle.net/10453/179557
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	As cloud computing faces challenges with data latency and processing efficiency, fog computing has emerged as a solution by optimizing processing, storage, and networking closer to edge devices, termed fog consumers in this study. This research introduces an innovative AI-driven framework for intelligent discovery and trust-based selection of fog nodes, developed under the Distributed Fog Registry Consortium (FRC). This framework enhances context-aware discovery through algorithms such as KNN, K-d tree, and brute force within the Fog Node Discovery Engine (FNDE), crucial for meeting quality of service (QoS) metrics by identifying fog nodes based on location. Additionally, the Fog Node Selection Engine (FNSE) utilizes advanced methods including fuzzy logic, logistic regression, and deep neural networks through the Trust Evaluation Engine (TEE) to evaluate and predict fog nodes' trustworthiness. For new nodes, the Bootstrapping Engine (BE) addresses the "cold-start" issue by estimating initial QoS and reputation, facilitating reliable fog node selection. This approach not only boosts the efficiency of fog computing but also sets a foundation for future advancements in resource allocation and service optimization within the fog ecosystem. Simulation results confirm the effectiveness of these engines, underscoring their potential to enhance the role of fog computing in edge technology advancements.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/179557/1/thesis.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2024 Afnan Abdulrahman Bukhari
dc.rights	au.edu.uts.lib/cph
dc.title	Intelligent Context-aware Fog Node Discovery and Trust-based Fog Node Selection	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

As cloud computing faces challenges with data latency and processing efficiency, fog computing has emerged as a solution by optimizing processing, storage, and networking closer to edge devices, termed fog consumers in this study. This research introduces an innovative AI-driven framework for intelligent discovery and trust-based selection of fog nodes, developed under the Distributed Fog Registry Consortium (FRC). This framework enhances context-aware discovery through algorithms such as KNN, K-d tree, and brute force within the Fog Node Discovery Engine (FNDE), crucial for meeting quality of service (QoS) metrics by identifying fog nodes based on location. Additionally, the Fog Node Selection Engine (FNSE) utilizes advanced methods including fuzzy logic, logistic regression, and deep neural networks through the Trust Evaluation Engine (TEE) to evaluate and predict fog nodes' trustworthiness. For new nodes, the Bootstrapping Engine (BE) addresses the "cold-start" issue by estimating initial QoS and reputation, facilitating reliable fog node selection. This approach not only boosts the efficiency of fog computing but also sets a foundation for future advancements in resource allocation and service optimization within the fog ecosystem. Simulation results confirm the effectiveness of these engines, underscoring their potential to enhance the role of fog computing in edge technology advancements.

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http://hdl.handle.net/10453/179557