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Lookup NU author(s): Dr Gagangeet Aujla, Professor Raj Ranjan
This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2018.
For re-use rights please refer to the publisher's terms and conditions.
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
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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