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Tensor-based Big Data Management Scheme for Dimensionality Reduction Problem in Smart Grid Systems: SDN Perspective

Lookup NU author(s): Professor Raj Ranjan

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This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2018.

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Abstract

IEEE Smart grid (SG) is an integration of traditional power grid with advanced information and communication infrastructure for bidirectional energy flow between grid and end users. A huge amount of data is being generated by various smart devices deployed in SG systems. Such a massive data generation from various smart devices in SG systems may generate issues such as-congestion, and available bandwidth on the networking infrastructure deployed between users and the grid. Hence, an efficient data transmission technique is required for providing desired QoS to the end users in this environment. Generally, the data generated by smart devices in SG has high dimensions in the form of multiple heterogeneous attributes, values of which are changed with time. The high dimensions of data may affect the performance of most of the designed solutions in this environment. Most of the existing schemes reported in the literature have complex operations for data dimensionality reduction problem which may deteriorate the performance of any implemented solution for this problem. To address these challenges, in this paper, a tensor-based big data management scheme is proposed for dimensionality reduction problem of big data generated from various smart devices. In the proposed scheme, firstly the Frobenius


Publication metadata

Author(s): Kaur D, Aujla GS, Kumar N, Zomaya A, Perera C, Ranjan R

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Knowledge and Data Engineering

Year: 2018

Volume: 30

Issue: 10

Pages: 1985-1998

Print publication date: 01/10/2018

Online publication date: 27/02/2018

Acceptance date: 02/04/2016

Date deposited: 10/05/2018

ISSN (print): 1041-4347

ISSN (electronic): 1558-2191

Publisher: IEEE

URL: https://doi.org/10.1109/TKDE.2018.2809747

DOI: 10.1109/TKDE.2018.2809747


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