Improving the Response Time in Smart Grid Using Fog Computing

Kumar, Shubham; Agarwal, Suyash; Krishnamoorthy, A.; Vijayarajan, V.; Kannadasan, R.

doi:10.1007/978-981-13-1610-4_57

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…Smart grid-based fog computing security and privacy. 50 also proved that their scheme has low computation and communication costs compared to other schemes. This scheme does not discuss the smart-metering approach.…”

Section: F I G U R Ementioning

confidence: 83%

Adversarial learning techniques for security and privacy preservation: A comprehensive review

Hathaliya

Tanwar

Sharma

2022

Security and Privacy

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In recent years, the use of smart devices has increased exponentially, resulting in massive amounts of data. To handle this data, effective data storage and management has required. Cloud computing (CC) is a promising solution to deal with this huge amount of data. Electronic devices are collecting real‐time data from sensors and applications through a wireless communication channel in the digital era. In some cases, CC cannot protect against various malicious attacks in the wireless communication channel. To address this issue, we have used machine learning (ML) and deep learning (DL) techniques for attack detection in a wireless channel on an early basis. It trains a model to predict malicious activities of attackers, which aids in the security of CC's sensitive data. We employed adversarial learning techniques (AL) to add fake data into the model to ensure that the trained model was correct. The trained model can distinguish between the fake and real data from the training samples and improve the training samples' performance. AL provides different defense mechanisms to preserve the privacy of ML‐ and DL‐based model but does not ensure the system's robustness. To improve the system's robustness, we have used federated learning with blockchain technology to make a system more robust, reliable, accurate, and transparent. This integration aids in providing high‐graded security against adversarial attacks. This paper presents a comprehensive review to highlight the recent improvements in AL techniques. Moreover, we explored the various AL applications in security and privacy preservation. Finally, open research issues and future directions are discussed to show future research avenues.

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Section: F I G U R Ementioning

confidence: 83%

Adversarial learning techniques for security and privacy preservation: A comprehensive review

Hathaliya

Tanwar

Sharma

2022

Security and Privacy

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“…Gaolei et al [54] Service Popularity-Based Smart Resources Partitioning Chekired et al [62] Industrial IoT data scheduling service popularity Fu and Liu. [69] Secure Data Storage and Searching for industrial Kumar et al [72] Improving the response time using FC factory workshop and focuses on the data processing, safe storage using unique identity, keys on each node, efficient recovery and dynamic collection. The data are previously processed by the edge server and, then, time-sensitive data are locally used and stored, while non time sensitive data are transferred to the server in the cloud.…”

Section: Author(s) Focusmentioning

confidence: 99%

Fog computing at industrial level, architecture, latency, energy, and security: A review

Caiza

Saeteros

Oñate

et al. 2020

Heliyon

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The industrial applications in the cloud do not meet the requirements of low latency and reliability since variables must be continuously monitored. For this reason, industrial internet of things (IIoT) is a challenge for the current infrastructure because it generates a large amount of data making cloud computing reach the edge and become fog computing (FC). FC can be considered as a new component of Industry 4.0, which aims to solve the problem of big data, reduce energy consumption in industrial sensor networks, improve the security, processing and storage real-time data. It is a promising growing paradigm that offers new opportunities and challenges, beside the ones inherited from cloud computing, which requires a new heterogeneous architecture to improve the network capacity for delivering edge services, that is, providing computing resources closer to the end user. The purpose of this research is to show a systematic review of the most recent studies about the architecture, security, latency, and energy consumption that FC presents at industrial level and thus provide an overview of the current characteristics and challenges of this new technology.

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“…Various researchers [3,14,16,21,70] have proposed features of edge computing that fit to the requirements of smart grid applications implementation. The following are the features: Support for real-time, that is, reducing latency and responsive time [38,[47][48][49][50][51], Support for Scalability [68], Decentralization [6, 16-18, 51, 52, 68], Consistency [19], Heterogeneity [20][21][22] , Intelligent coordination [23][24][25][26][27] , Optimal design / Resource utilization [28][29][30][31][32][33][34][35][36], Privacy and Security [16,17,37,[39][40][41][42], Data Mining and Storage [43][44][45], Virtualization [46], Energy Efficiency [51,71].…”

Section: Research Question Onementioning

confidence: 99%

A Critical Review of Edge and Fog Computing for Smart Grid Applications

Gilbert

Naiman

Kimaro

et al. 2019

IFIP Advances in Information and Communication Technology

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Smart grid and cloud computing architectures have been perfectly suiting each other naturally. As a result, over the years cloud computing architectures have dominated the implementations of smart grid applications to address computing needs. However, due to continuing additions of heterogeneous (sensing and actuating) devices, emergence of Internet of Things (IoT), and massive amount of data collected across the grids for analytics, have contributed to the complexity of smart grids, making cloud computing architectures no longer suitable to provide smart grid services effectively. Edge and Fog computing approaches have relieved the cloud computing architectures of problems related to network congestion, latency and locality by shift of control, intelligence and trust to the edge of the network. In this paper, a systematic literature review is used to explore the research trend of the actual implementations of edge and fog computing for smart grid applications. A total of 70 papers were reviewed from the popular digital repositories. The study has revealed that, there is significant increase in the number of smart grid applications that have exploited the use edge and fog computing approaches. The study also shows that, considerable number of the smart grid applications are related to energy optimizations and intelligent coordination of smart grid resources. There are also challenges and issues that hinder smooth adoption of edge and fog computing for smart grid applications, which include security, interoperability and programming models.

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Improving the Response Time in Smart Grid Using Fog Computing

Cited by 6 publications

References 10 publications

Adversarial learning techniques for security and privacy preservation: A comprehensive review

Adversarial learning techniques for security and privacy preservation: A comprehensive review

Fog computing at industrial level, architecture, latency, energy, and security: A review

A Critical Review of Edge and Fog Computing for Smart Grid Applications

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