Integrating Distributed Pattern Recognition Technique for Event Monitoring within the IoT-Blockchain Network

Citation

Ahmad, Nazrul Muhaimin and Kannan, Subarmaniam and Muhamad Amin, Anang Hudaya (2018) Integrating Distributed Pattern Recognition Technique for Event Monitoring within the IoT-Blockchain Network. In: 2018 International Conference on Intelligent and Advanced System (ICIAS). IEEE, pp. 1-6. ISBN 978-1-5386-7270-9

[img] Text
hudaya2018.pdf - Published Version
Restricted to Repository staff only

Download (989kB)

Abstract

Blockchain technology is currently emerging as the next promising revolutionary technology. Many different applications have started to venture into this technology. The decentralization and distribution nature of blockchain allows dynamic applications to be build on it, including Internet-of-Things (IoT) applications. IoT devices have been commonly linked to smart systems with its applications mainly surrounding on event detection and monitoring. Coupling this IoT capabilities with blockchain network offers great potential for scalable event detection and monitoring for large-scale network. Nevertheless, detection mechanism should be able to scale up with such requirements. In this paper, we propose a conceptual framework on implementation of distributed pattern recognition for event monitoring within IoT-blockchain network. This conceptual framework is supported by a preliminary study on IoT-blockchain implementation and distributed pattern recognition implementation using a Graph Neuron (GN) approach.

Item Type: Book Section
Uncontrolled Keywords: Blockchain, event monitoring, Internet-of-Things, pattern recognition, Graph Neuron(GN)
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 19 Jan 2021 02:10
Last Modified: 19 Jan 2021 02:10
URII: http://shdl.mmu.edu.my/id/eprint/7311

Downloads

Downloads per month over past year

View ItemEdit (login required)