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Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/xmlui/handle/123456789/10185
Title: Versatile Multivariate Data Pruning in Smart Grid IoT Networks
Authors: Tripathi, Sharda
Keywords: EEE
Multivariate data compression
Edge processing
Adaptive compressive sampling
Singular value decomposition
PMU data
Smart grid communication
IoT networks
Issue Date: 2020
Publisher: IEEE
Abstract: With wide scale sensor deployments in smart grid IoT networks, there has been a manyfold increase in the variety and quantity of data generated in the network. In this work, the problem of data reduction in smart grid IoT network is addressed to enhance the resource utilization without hampering the required quality of service. A novel versatile algorithm for multivariate data pruning at the edge devices in smart grid IoT networks is presented. This is achieved via a two stage data reduction mechanism which first exploits the inter-variable correlation to cut down on the number of transmitted variables, followed by adaptive data compression in temporal domain using adaptive compressive sampling. It is shown that with the application of the proposed algorithm at the edge nodes, around 23% savings in bandwidth requirement can be achieved with minimum loss of information.
URI: https://ieeexplore.ieee.org/abstract/document/9027338
http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/10185
Appears in Collections:Department of Electrical and Electronics Engineering

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