Fog computing is an architecture that uses one or more collaborative end-user clients or near-user edge devices to carry out a substantial amount of storage and processing rather than relying on centralised cloud infrastructure. This approach deploys computing nodes throughout IoT networks, on factory floors, in vehicles, on power poles, and at oil rigs, to analyse data locally, reducing latency and network traffic while enhancing security and privacy by avoiding centralised data storage. This project evaluated fog and edge computing architectures, middleware, and distributed analytics approaches for edge-centric IoT deployments, exploring how to push computation closer to data sources and enable real-time decision making in scenarios where cloud connectivity is limited or latency requirements are stringent. The work spans multiple application domains from industrial IoT to smart grid systems.
Key contributions span several areas of distributed IoT analytics. The research evaluated the effectiveness of service decomposition in fog computing architectures and developed spatial-temporal correlation approaches for data reduction in cluster-based sensor networks. These techniques enable more efficient use of bandwidth and storage across geographically distributed sensing deployments.
The project also addressed big data challenges in industrial and smart grid contexts. This included analysing the role of big data analytics in the Industrial Internet of Things and proposing tensor-based management schemes for dimensionality reduction in smart grid systems. Together, this body of work advances understanding of how distributed analytics platforms can be designed and deployed across heterogeneous IoT environments.
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