Traffic volume reduction in smart grid networks by a cooperative intelligent interpolation technique

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Authors
Boustani, Arash
Alamatsaz, Navid Reza
Alamatsaz, Nima
Boustani, Ashkan
Advisors
Issue Date
2018
Type
Conference paper
Keywords
Smart grid , Capacity planning , Volume reduction , Spread spectrum communication
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Organizational Units
Journal Issue
Citation
Boustani, Arash; Alamatsaz, Navid Reza; Alamatsaz, Nima; Boustani, Ashkan. 2018. Traffic volume reduction in smart grid networks by a cooperative intelligent interpolation technique. 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC)
Abstract

Leveraging a modern communication network, the power industry is moving towards the next generation power grid, the smart grid. This new communication-based power grid is expected to change the way electricity is generated, distributed, and transmitted to the consumers by enhancing the reliability, efficiency, sustainability, and economics of the grid. However, due to the high volume, high granularity, and frequency of the data generated by smart electricity meters, careful planning and management of this communication network is essential. Given the potential large-scale future deployment of the smart grid, power companies face possible network capacity limitations. Therefore, efficient utilization of the Smart Grid Network (SGN) should be studied. In this paper, we introduce a smart interpolation scheme for reducing the volume of information transmitted in a smart grid backhaul network without any precision reduction or loss of bene?t. Utilizing concepts of Spread Spectrum Communications, smart nodes at utility control centers are able to intelligently infer omitted data and interpolate the original message. By means of extensive evaluations, we show that our scheme significantly improves network utilization and decreases volume of the traffic in a smart grid network.

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Publisher
IEEE
Journal
Book Title
Series
2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC);
PubMed ID
DOI
ISSN
2331-9852
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