scispace - formally typeset
Search or ask a question
Author

B. Prabadevi

Bio: B. Prabadevi is an academic researcher from VIT University. The author has contributed to research in topics: Computer science & Smart grid. The author has an hindex of 6, co-authored 25 publications receiving 180 citations.

Papers
More filters
Journal ArticleDOI
TL;DR: This paper aims to provide a survey-based tutorial on potential applications and supporting technologies of Industry 5.0 from the perspective of different industry practitioners and researchers.

314 citations

Posted Content
TL;DR: A comprehensive survey on blockchain for big data, focusing on up-to-date approaches, opportunities, and future directions is provided, including blockchain for secure big data acquisition, data storage, data analytics, and data privacy preservation.
Abstract: Big data has generated strong interest in various scientific and engineering domains over the last few years. Despite many advantages and applications, there are many challenges in big data to be tackled for better quality of service, e.g., big data analytics, big data management, and big data privacy and security. Blockchain with its decentralization and security nature has the great potential to improve big data services and applications. In this article, we provide a comprehensive survey on blockchain for big data, focusing on up-to-date approaches, opportunities, and future directions. First, we present a brief overview of blockchain and big data as well as the motivation behind their integration. Next, we survey various blockchain services for big data, including blockchain for secure big data acquisition, data storage, data analytics, and data privacy preservation. Then, we review the state-of-the-art studies on the use of blockchain for big data applications in different vertical domains such as smart city, smart healthcare, smart transportation, and smart grid. For a better understanding, some representative blockchain-big data projects are also presented and analyzed. Finally, challenges and future directions are discussed to further drive research in this promising area.

173 citations

Journal ArticleDOI
TL;DR: The proposed scheme RASGD improves the regularization of the classification model by using weight decay methods, namely least absolute shrinkage and selection operator and ridge regression methods, and attains an accuracy of 92%, which is better than the other selected classifiers.
Abstract: Recent technological advancements in information and communication technologies introduced smart ways of handling various aspects of life. Smart devices and applications are now an integral part of our daily life; however, the use of smart devices also introduced various physical and psychological health issues in modern societies. One of the most common health care issues prevalent among almost all age groups is diabetes mellitus. This work aims to propose an artificial intelligence-based intelligent system for earlier prediction of the disease using Ridge-Adaline Stochastic Gradient Descent Classifier (RASGD). The proposed scheme RASGD improves the regularization of the classification model by using weight decay methods, namely least absolute shrinkage and selection operator and ridge regression methods. To minimize the cost function of the classifier, the RASGD adopts an unconstrained optimization model. Further, to increase the convergence speed of the classifier, the Adaline Stochastic Gradient Descent Classifier is integrated with ridge regression. Finally, to validate the effectiveness of the intelligent system, the results of the proposed scheme have been compared with state-of-the-art machine learning algorithms such as support vector machine and logistic regression methods. The RASGD intelligent system attains an accuracy of 92%, which is better than the other selected classifiers.

84 citations

Journal ArticleDOI
TL;DR: In this article, state-of-the-art machine learning algorithms, namely Support Vector Machines (SVM), KNN, Logistic Regression, Naive Bayes, Neural Networks, and Decision Tree classifier, have been deployed for predicting the stability of the smart grid.
Abstract: The global demand for electricity has visualized high growth with the rapid growth in population and economy. It thus becomes necessary to efficiently distribute electricity to households and industries in order to reduce power loss. Smart Grids (SG) have the potential to reduce such power losses during power distribution. Machine learning and artificial intelligence techniques have been successfully implemented on SGs to achieve enhanced accuracy in customer demand prediction. There exists a dire need to analyze and evaluate the various machine learning algorithms, thereby identify the most suitable one to be applied to SGs. In the present work, several state-of-the-art machine learning algorithms, namely Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Logistic Regression, Naive Bayes, Neural Networks, and Decision Tree classifier, have been deployed for predicting the stability of the SG. The SG dataset used in the study is publicly available collected from UC Irvine (UCI) machine learning repository. The experimentation results highlighted the superiority of the Decision Tree classification algorithm, which outperformed the other state of the art algorithms yielding 100% precision, 99.9% recall, 100% F1 score, and 99.96% accuracy.

41 citations

Journal ArticleDOI
TL;DR: In this paper, a novel edge-of-things (BEoT) architecture for supporting industrial applications under the management of blockchain at the network edge in a wide range of IoT use cases such as smart home, smart healthcare, smart grid, and smart transportation is presented.
Abstract: Blockchain is gaining momentum as a promising technology for many application domains, one of them being the Edge-of- Things (EoT) that is enabled by the integration of edge computing and the Internet-of-Things (IoT). Particularly, the amalgamation of blockchain and EoT leads to a new paradigm, called blockchain enabled EoT (BEoT) that is crucial for enabling future low-latency and high-security services and applications. This article envisions a novel BEoT architecture for supporting industrial applications under the management of blockchain at the network edge in a wide range of IoT use cases such as smart home, smart healthcare, smart grid, and smart transportation. The potentials of BEoT in providing security services are also explored, including access authentication, data privacy preservation, attack detection, and trust management. Finally, we point out some key research challenges and future directions in this emerging area.

32 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: This survey classifies the IoT security threats and challenges for IoT networks by evaluating existing defense techniques and provides a comprehensive review of NIDSs deploying different aspects of learning techniques for IoT, unlike other top surveys targeting the traditional systems.
Abstract: Pervasive growth of Internet of Things (IoT) is visible across the globe. The 2016 Dyn cyberattack exposed the critical fault-lines among smart networks. Security of IoT has become a critical concern. The danger exposed by infested Internet-connected Things not only affects the security of IoT but also threatens the complete Internet eco-system which can possibly exploit the vulnerable Things (smart devices) deployed as botnets. Mirai malware compromised the video surveillance devices and paralyzed Internet via distributed denial of service attacks. In the recent past, security attack vectors have evolved bothways, in terms of complexity and diversity. Hence, to identify and prevent or detect novel attacks, it is important to analyze techniques in IoT context. This survey classifies the IoT security threats and challenges for IoT networks by evaluating existing defense techniques. Our main focus is on network intrusion detection systems (NIDSs); hence, this paper reviews existing NIDS implementation tools and datasets as well as free and open-source network sniffing software. Then, it surveys, analyzes, and compares state-of-the-art NIDS proposals in the IoT context in terms of architecture, detection methodologies, validation strategies, treated threats, and algorithm deployments. The review deals with both traditional and machine learning (ML) NIDS techniques and discusses future directions. In this survey, our focus is on IoT NIDS deployed via ML since learning algorithms have a good success rate in security and privacy. The survey provides a comprehensive review of NIDSs deploying different aspects of learning techniques for IoT, unlike other top surveys targeting the traditional systems. We believe that, this paper will be useful for academia and industry research, first, to identify IoT threats and challenges, second, to implement their own NIDS and finally to propose new smart techniques in IoT context considering IoT limitations. Moreover, the survey will enable security individuals differentiate IoT NIDS from traditional ones.

494 citations

Journal ArticleDOI
TL;DR: This paper aims to provide a survey-based tutorial on potential applications and supporting technologies of Industry 5.0 from the perspective of different industry practitioners and researchers.

314 citations

Journal ArticleDOI
07 Apr 2021
TL;DR: In this paper, the authors provide a comprehensive survey of the current developments towards 6G and elaborate the requirements that are necessary to realize the 6G applications, and summarize lessons learned from state-of-the-art research and discuss technical challenges that would shed a new light on future research directions toward 6G.
Abstract: Emerging applications such as Internet of Everything, Holographic Telepresence, collaborative robots, and space and deep-sea tourism are already highlighting the limitations of existing fifth-generation (5G) mobile networks. These limitations are in terms of data-rate, latency, reliability, availability, processing, connection density and global coverage, spanning over ground, underwater and space. The sixth-generation (6G) of mobile networks are expected to burgeon in the coming decade to address these limitations. The development of 6G vision, applications, technologies and standards has already become a popular research theme in academia and the industry. In this paper, we provide a comprehensive survey of the current developments towards 6G. We highlight the societal and technological trends that initiate the drive towards 6G. Emerging applications to realize the demands raised by 6G driving trends are discussed subsequently. We also elaborate the requirements that are necessary to realize the 6G applications. Then we present the key enabling technologies in detail. We also outline current research projects and activities including standardization efforts towards the development of 6G. Finally, we summarize lessons learned from state-of-the-art research and discuss technical challenges that would shed a new light on future research directions towards 6G.

273 citations

Journal ArticleDOI
TL;DR: This paper reviews 96 publications on DDoS attack and defense approaches in cloud computing published between January 2009 and December 2015, and discusses existing research trends.

253 citations

Journal ArticleDOI
TL;DR: This paper is devoted to thoroughly reviewing and critically discussing various ML technology applications, with a particular focus on ANN, to solve function approximation, optimization, monitoring, and control problems in biodiesel research.

203 citations