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Health Monitoring and Diagnosis for Geo-Distributed Edge Ecosystem in Smart City

Wen, Wu; Demirbaga, Umit; Singh, Amritpal; Jindal, Anish; Batth, Ranbir Singh; Zhang, Peiying; Aujla, Gagangeet Singh

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Authors

Wu Wen

Umit Demirbaga

Amritpal Singh

Ranbir Singh Batth

Peiying Zhang



Abstract

With the increasing number of Internet of Things (IoT) devices being deployed and used in daily life, the load on computational devices has grown exponentially. This situation is more prevalent in smart cities where such devices are used for autonomous control and monitoring. Smart cities have different kinds of applications that are aided through IoT devices that collect data, send it to computational processing and storage devices, and get back decisions or actuate the actions based on the input data. There has been a stringent requirement to reduce the end-to-end delay in this process owing to the remote deployment of cloud data centres. This eventually led to the revolution of edge computing, wherein nano–micro-processing devices can be deployed closer to the premises of the smart application and process the data generated with a lower turnaround time. However, due to the limited computational power and storage, controlling the workload diverted to the edge devices has been challenging. The workload scheduling policies and task allocation schemes often fail to consider the run time health of the edge devices due to a lack of proper monitoring infrastructure. Thus, in this article, we proposed a health monitoring and diagnosis framework for geo-distributed edge clusters processing big data generated by smart city applications. This framework is built over the Map-Reduce approach for distributed processing of big data on edge clusters deployed across the smart city. Within this framework, SmartMonit (a monitoring agent) is deployed that collects the health statistics of edge devices and predicts the potential failures using an artificial neural network-based self-organising maps approach. The proposed framework is deployed over different clusters to test the efficacy concerning failure detection.

Citation

Wen, W., Demirbaga, U., Singh, A., Jindal, A., Batth, R. S., Zhang, P., & Aujla, G. S. (2023). Health Monitoring and Diagnosis for Geo-Distributed Edge Ecosystem in Smart City. IEEE Internet of Things Journal, 10(21), 18571-18578. https://doi.org/10.1109/jiot.2023.3247640

Journal Article Type Article
Acceptance Date Feb 9, 2023
Online Publication Date Feb 22, 2023
Publication Date Nov 1, 2023
Deposit Date May 13, 2023
Publicly Available Date Oct 27, 2023
Journal IEEE Internet of Things Journal
Electronic ISSN 2327-4662
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 10
Issue 21
Pages 18571-18578
DOI https://doi.org/10.1109/jiot.2023.3247640
Public URL https://durham-repository.worktribe.com/output/1175220

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