Efficient utilization of big data using distributed storage, parallel processing, and blockchain technology

Alessandro Giuliano; Waleed Hilal; Naseem Alsadi; Onur Surucu; Andrew Gadsden; John Yawney; Youssef Ziada

doi:10.1117/12.2618891

31 May 2022 Efficient utilization of big data using distributed storage, parallel processing, and blockchain technology

Alessandro Giuliano, Waleed Hilal, Naseem Alsadi, Onur Surucu, Andrew Gadsden, John Yawney, Youssef Ziada

Proceedings Volume 12097, Big Data IV: Learning, Analytics, and Applications; 1209704 (2022) https://doi.org/10.1117/12.2618891
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

As data collected through IoT systems worldwide increases and the deployment of IoT architectures is expanded across multiple domains, novel frameworks that focus on application-based criteria and constraints are needed. In recent years, big data processing has been addressed using cloud-based technology, although such implementations are not suitable for latency-sensitive applications. Edge and Fog computing paradigms have been proposed as a viable solution to this problem, expanding the computation and storage to data centers located at the network's edge and providing multiple advantages over sole cloud-based solutions. However, security and data integrity concerns arise in developing IoT architectures in such a framework, and blockchain-based access control and resource allocation are viable solutions in decentralized architectures. This paper proposes an architecture composed of a multilayered data system capable of redundant distributed storage and processing using encrypted data transmission and logging on distributed internal peer-to-peer networks.

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Citation Download Citation

Alessandro Giuliano, Waleed Hilal, Naseem Alsadi, Onur Surucu, Andrew Gadsden, John Yawney, and Youssef Ziada "Efficient utilization of big data using distributed storage, parallel processing, and blockchain technology", Proc. SPIE 12097, Big Data IV: Learning, Analytics, and Applications, 1209704 (31 May 2022); https://doi.org/10.1117/12.2618891

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