Intelligent context-aware fog node discovery

Bukhari, AA; Hussain, FK; Hussain, OK

Intelligent context-aware fog node discovery

Bukhari, AA Hussain, FK Hussain, OK

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Internet of Things (Netherlands), 2022, 20, pp. 100607
Issue Date:: 2022-11-01

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Field	Value	Language
dc.contributor.author	Bukhari, AA
dc.contributor.author	Hussain, FK
dc.contributor.author	Hussain, OK
dc.date.accessioned	2023-03-24T00:44:05Z
dc.date.available	2023-03-24T00:44:05Z
dc.date.issued	2022-11-01
dc.identifier.citation	Internet of Things (Netherlands), 2022, 20, pp. 100607
dc.identifier.issn	2199-1073
dc.identifier.issn	2542-6605
dc.identifier.uri	http://hdl.handle.net/10453/168297
dc.description.abstract	Fog computing has been proposed as a mechanism to address certain issues in cloud computing such as latency, storage, network bandwidth, etc. Fog computing brings the processing, storage, and networking to the edge of the network near the edge devices, which we called fog consumers. This decreases latency, network bandwidth, and response time. Discovering the most relevant fog node, the nearest one to the fog consumers, is a critical challenge that is yet to be addressed by the research. In this study, we present the Intelligent and Distributed Fog node Discovery mechanism (IDFD) which is an intelligent approach to enable fog consumers to discover appropriate fog nodes in a context-aware manner. The proposed approach is based on the distributed fog registries between fog consumers and fog nodes that can facilitate the discovery process of fog nodes. In this study, the KNN, K-d tree, and brute force algorithms are used to discover fog nodes based on the context-aware criteria of fog nodes and fog consumers. The proposed framework is simulated using OMNET++, and the performance of the proposed algorithms is compared based on performance metrics and execution time. The accuracy and execution time are the major points of consideration in the selection of an optimal fog search algorithm. The experiment results show that the KNN and K-d tree algorithms achieve the same accuracy results of 95%. However, the K-d tree method takes less time to find the nearest fog nodes than KNN and brute force. Thus, the K-d tree is selected as the fog search algorithm in the IDFD to discover the nearest fog nodes very efficiently and quickly.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Internet of Things (Netherlands)
dc.relation.isbasedon	10.1016/j.iot.2022.100607
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Intelligent context-aware fog node discovery
dc.type	Journal Article
utslib.citation.volume	20
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-24T00:43:56Z
pubs.publication-status	Published
pubs.volume	20

Abstract:

Fog computing has been proposed as a mechanism to address certain issues in cloud computing such as latency, storage, network bandwidth, etc. Fog computing brings the processing, storage, and networking to the edge of the network near the edge devices, which we called fog consumers. This decreases latency, network bandwidth, and response time. Discovering the most relevant fog node, the nearest one to the fog consumers, is a critical challenge that is yet to be addressed by the research. In this study, we present the Intelligent and Distributed Fog node Discovery mechanism (IDFD) which is an intelligent approach to enable fog consumers to discover appropriate fog nodes in a context-aware manner. The proposed approach is based on the distributed fog registries between fog consumers and fog nodes that can facilitate the discovery process of fog nodes. In this study, the KNN, K-d tree, and brute force algorithms are used to discover fog nodes based on the context-aware criteria of fog nodes and fog consumers. The proposed framework is simulated using OMNET++, and the performance of the proposed algorithms is compared based on performance metrics and execution time. The accuracy and execution time are the major points of consideration in the selection of an optimal fog search algorithm. The experiment results show that the KNN and K-d tree algorithms achieve the same accuracy results of 95%. However, the K-d tree method takes less time to find the nearest fog nodes than KNN and brute force. Thus, the K-d tree is selected as the fog search algorithm in the IDFD to discover the nearest fog nodes very efficiently and quickly.

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http://hdl.handle.net/10453/168297