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Convolutional Neural Network-Based Secured Data Storage Mechanism for Big Data Environments

Convolutional Neural Network-Based Secured Data Storage Mechanism for Big Data Environments

Balamurugan Easwaran, Sangeetha Krishnan, Anitha P. T., Jackson Akpojaro, Kirubanand V. B.
Copyright: © 2022 |Pages: 18
ISBN13: 9781799893080|ISBN10: 1799893081|ISBN13 Softcover: 9781799893097|EISBN13: 9781799893103
DOI: 10.4018/978-1-7998-9308-0.ch013
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MLA

Easwaran, Balamurugan, et al. "Convolutional Neural Network-Based Secured Data Storage Mechanism for Big Data Environments." Real-Time Applications of Machine Learning in Cyber-Physical Systems, edited by Balamurugan Easwaran, et al., IGI Global, 2022, pp. 200-217. https://doi.org/10.4018/978-1-7998-9308-0.ch013

APA

Easwaran, B., Krishnan, S., P. T., A., Akpojaro, J., & V. B., K. (2022). Convolutional Neural Network-Based Secured Data Storage Mechanism for Big Data Environments. In B. Easwaran, K. Hiran, S. Krishnan, & R. Doshi (Eds.), Real-Time Applications of Machine Learning in Cyber-Physical Systems (pp. 200-217). IGI Global. https://doi.org/10.4018/978-1-7998-9308-0.ch013

Chicago

Easwaran, Balamurugan, et al. "Convolutional Neural Network-Based Secured Data Storage Mechanism for Big Data Environments." In Real-Time Applications of Machine Learning in Cyber-Physical Systems, edited by Balamurugan Easwaran, et al., 200-217. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-7998-9308-0.ch013

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Abstract

Data types and amounts in human society are growing at an amazing speed, which is caused by emerging new services such as cloud computing and internet of things (IoT). As data has been a fundamental resource, research on big data has attracted much attention. An optimized cluster storage method for big data in IoT is proposed. First, weights of data blocks in each historical accessing period are calculated by temporal locality of data access, and the access frequencies of the data block in next period are predicted by the weights. Second, the hot spot of a data block is determined with a threshold that is calculated by previous data access. In this work, big data is divided into multiple segments based on semantic connectivity-based convolutional neural networks. Each segment will be stored in the different nodes by adapting the blockchain distributed-based local regenerative code technology called BCDLR. Experimental results demonstrate the efficiency of the proposed model in terms of packet delivery ratio, end-to-end delay, energy consumption, and throughput.

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