Abstract
The application of blockchain technology for data storage and verification has been expanding from financial applications to other fields such as asset management and event monitoring in Internet-of-Things (IoT). This expansion consequently intensifies the problem of an increasing size of data stored in the blockchain, especially in event monitoring application where streams of data need to be stored and verified accordingly. In this paper, we propose an IoT-blockchain event monitoring framework that utilizes a distributed pattern recognition scheme for event data processing. Event data are treated as patterns comprising individual data retrieved from interconnected IoT sensors within a network composition. Preliminary results obtained indicate that the proposed scheme is capable of reducing the number of data blocks generated in the blockchain network, hence minimizing the needs for intensive storage and verification.
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References
Blockchain demo. https://blockchaindemo.io/. Accessed 28 Jan 2019
Amin, A.H.M., Khan, A.I.: Collaborative-comparison learning for complex event detection using distributed hierarchical graph neuron (DHGN) approach in wireless sensor network. In: Australasian Joint Conference on Artificial Intelligence, pp. 111–120. Springer, Heidelberg (2009)
Amin, A.H.M., Khan, A.I., Nasution, B.B.: Internet-Scale Pattern Recognition: New Techniques for Voluminous Data Sets and Data Clouds. Chapman and Hall/CRC, Boca Raton (2012)
Buterin, V.: Toward a 12-second block time. Ethereum Blog (2014)
Cortez, P., Morais, A.D.J.R.: A data mining approach to predict forest fires using meteorological data (2007)
Diedrich, H.: Ethereum: Blockchains, Digital Assets, Smart Contracts, Decentralized Autonomous Organizations. Wildfire Publishing, Sydney (2016)
Jan, M.A., Nanda, P., He, X., Liu, R.P.: A sybil attack detection scheme for a forest wildfire monitoring application. Future Gen. Comput. Syst. 80, 613–626 (2018)
Khan, A.I., Mihailescu, P.: Parallel pattern recognition computations within a wireless sensor network. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 1, pp. 777–780, August 2004
Kleyko, D., Osipov, E.: On bidirectional transitions between localist and distributed representations: the case of common substrings search using vector symbolic architecture. Procedia Comput. Sci. 41, 104–113 (2014)
Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system (2008)
Nasution, B.B., Khan, A.I.: A hierarchical graph neuron scheme for real-time pattern recognition. IEEE Trans. Neural Netw. 19(2), 212–229 (2008)
Wood, G.: Ethereum: a secure decentralised generalised transaction ledger. Ethereum Proj. Yellow Pap. 151, 1–32 (2014)
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Muhamad Amin, A.H., Alqatawna, J., Paul, S., Kiwanuka, F.N., Akhtar, I.A. (2020). Improving Event Monitoring in IoT Network Using an Integrated Blockchain-Distributed Pattern Recognition Scheme. In: Prieto, J., Das, A., Ferretti, S., Pinto, A., Corchado, J. (eds) Blockchain and Applications. BLOCKCHAIN 2019. Advances in Intelligent Systems and Computing, vol 1010 . Springer, Cham. https://doi.org/10.1007/978-3-030-23813-1_17
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DOI: https://doi.org/10.1007/978-3-030-23813-1_17
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