Different Aspects of Smart Grid: An Overview

Nidhi, Neha; Prasad, Deepak; Nath, Vijay

doi:10.1007/978-981-13-0776-8_41

Cited by 19 publications

(7 citation statements)

References 3 publications

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“…The rest channels of the pool are ranked accordingly based on the proposed method. As a result, the transmitting node has knowledge about all available channels with their priorities ({(J * ) (1) , (J * ) (2) , . .…”

Section: The Proposed Methods For Channel Ratings and Optimal Channel mentioning

confidence: 99%

“…Another problem that still remains is that the sender cannot decide alone on the final channel because the peer receiver does not necessarily have access to all of these detected channels ({(J * ) (1) , (J * ) (2) , . .…”

Section: Channel Negotiation Process Between the Sender And Receivermentioning

confidence: 99%

“…Nowadays, the concept of Smart Grid (SG) networks as the next generation of electricity networks covers various aspects of technical, economical and social affairs. In these networks, a two-way path is considered for both Energy and Data flows [1][2][3][4]. These two flows are not only independent but also have mutual functional effects [5].…”

Section: Introductionmentioning

confidence: 99%

“…The node n Tx who has the data to be sent in its transmission buffer implements the spectrum measurement algorithm and finds the best empty traffic channels, SCI = {(J * ) (1) , (J * ) (2) , . .…”

mentioning

confidence: 99%

“…In C − RTS, in addition to transmitter and receiver identifiers (n ID Tx and n ID Rx ), information on spectral sensing and SCI = {(J * ) (1) , (J * ) (2) , . .…”

mentioning

confidence: 99%

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Image Transmission over Cognitive Radio Networks for Smart Grid Applications

2019

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Today, Smart Grids (SGs), as the goal of the next-generation power grid system, span extremely wide aspects from power generation to end-user utilities. In smart grids, Energy and Information flows are mutually dependent and performance degradation of one side may have a high impact on the other side. In this work, we introduce our architecture for monitoring of Wind Turbine (WT) farms in smart grids. In our proposed system an industrial camera is embedded on a Wireless Cognitive Radio node for each WT to capture appropriate images and stream videos to the cognitive coordinator. Any packet loss in transmission between an embedded cognitive node and the coordinator can degrade peak signal-to-noise ratio (PSNR) of the received images. The image streaming is a delay sensitive transmission which should be done in harsh environments in SGs. To tackle these challenging issues, we introduce our efficient model, called JOPSS, for joint optimization of both packet size and Number of Spectrum Sensing Iterations (NSSI) during image transmission in time-restricted conditions. We define our proposed objective function as the quotient of the Overhead Time and the Effective Transmission Time (ETT). In addition, we introduce our methods based on the Minimum of Overhead Time Channel Selection (MOTS) for the efficient channel selection along with Dynamic Parameter Updating Procedure (DPUP) to benefit different strategies in Mandatory and Proactive Handoffs (MHO/PHO). The obtained results show that noticeable improvements in both PSNR and feature-similarity (FSIM) can be achieved on our models JOPSS and JOPSS-SAFE, respectively.

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Section: The Proposed Methods For Channel Ratings and Optimal Channel mentioning

confidence: 99%

Section: Channel Negotiation Process Between the Sender And Receivermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

“…In C − RTS, in addition to transmitter and receiver identifiers (n ID Tx and n ID Rx ), information on spectral sensing and SCI = {(J * ) (1) , (J * ) (2) , . .…”

mentioning

confidence: 99%

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