Hussain, Farookh KhadeerAlshuaibi, Enaam Abdulmonem O2024-07-042024-07-042024-05-28https://hdl.handle.net/20.500.14154/72497Fog computing has emerged as an important game-changing technology to address the resource challenges of the Internet of Things (IoT). However, the rapid increase in computational resource requirements at the edge of the network results in small-to-medium enterprises that provide fog services (FogSMEs) facing challenges in scalability, resource limitations, and network reliability. As a result of these challenges, FogSMEs are unable to meet modern data processing, security, and decision-making requirements. By exploring strategies that allow FogSMEs to maximize the benefits of the distributed nature of fog computing, in this thesis, we discuss volunteer computing as an innovative and cost-effective solution to improve their infrastructure. Leveraging idle computational resources from a global network of volunteer users, FogSMEs can achieve scalable, real-time services without significant investment in physical infrastructure. Research has identified significant gaps in the existing literature, including the absence of intelligent platforms to manage volunteer resources, dynamic selection mechanisms for volunteer nodes, and incentives to increase volunteer recruitment. To bridge the gaps in the recent literature, this thesis proposes an intelligent and reliable framework for selecting and verifying volunteer computing resources for fog scalability, named iBFog, by addressing three critical objectives: developing a trustworthy platform for managing volunteer nodes, designing an incentive mechanism to motivate participation, and implementing an intelligent selection mechanism for optimal node utilization. These objectives aim to overcome the challenges of fog scalability by ensuring efficient, secure, and reliable fog computing networks, especially for FogSMEs. This thesis contributes to the literature along three dimensions by including a systematic literature review to identify the need for an intelligent framework utilizing volunteer computing for fog scalability, the development of the iBFog framework that comprises a blockchain-based fog repository using Hyperledger Fabric, a game-based incentive module using Stackelberg game theory, and a ranking and selection module using three methods: a statistical method, a machine learning method, and a deep learning method. These components collectively address the identified research gaps, offering a comprehensive solution to the challenges of FogSME scalability. By intelligently managing, incentivizing and selecting volunteer computing resources, the iBFog framework advances the field of fog computing using a novel approach to enhancing its scalability. This framework not only addresses the immediate challenges of fog computing scalability but also sets the groundwork for future research and development in distributed computing environments.172enFog ComputingVolunteer ComputingFog Service ProviderSmall and Medium-Sized Enterprise (SME)Design Science Research MethodologyQuality of Service (QoS)Fog Computing-as-a-Service (FCaaS)Fog Service ConsortiumFog Service RepositoryMulti-Criteria Decision Making (MCDM)Analytical Hierarchy Process (AHP)Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)Machine LearningDeep LearningDeep Neural NetworkThesis