Citation

Muhamad Amin, Anang Hudaya and Ahmad, Nazrul Muhaimin and Kannan, Subarmaniam (2019) Event Monitoring using Distributed Pattern Recognition Approach on Integrated IoT-Blockchain Network. Advances in Science, Technology and Engineering Systems Journal, 4 (4). pp. 256-264. ISSN 24156698

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Abstract

With the advancement in the field of Internet-of-Things (IoT), event monitoring applications have rapidly evolved from a simple event data acquisition towards predictive event analytics involving multi-sensory data aggregation in a distributed environment. Existing event monitoring schemes are mainly relying on ine_cient centralized processing mechanism, which may lead to the common single-point of failure for the entire system. In addition, there is no proper method for verifying the event data generated by the monitoring system. In this paper, we present a distributed event monitoring scheme using a Hierarchical Graph Neuron (HGN) distributed pattern recognition algorithm. HGN is a single-cycle learning graph-based recognition scheme that is modelled for in-network deployment. In this work, event data retrieved from multi-sensory IoT devices within a distributed event monitoring network is converted into pattern. To address the event data verification problem, we integrate our proposed scheme with blockchain technology. Combining this IoT event monitoring capabilities with blockchain-based data storage and verification could leads towards a scalable event detection and monitoring model for large-scale network. The results obtained from our simulation shows that the proposed scheme o_ers high event detection accuracy and capable of minimizing the event storage requirements on blockchain network.

Item Type:	Article
Uncontrolled Keywords:	Pattern recognition systems
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	05 May 2021 12:47
Last Modified:	05 May 2021 12:47
URII:	http://shdl.mmu.edu.my/id/eprint/8663

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