Next-Generation Smart Electric Vehicles Cyber Physical System for Charging Slots Booking in Charging Stations

Chavhan, Suresh; Dubey, Nikhil; Lal, Abhinav; Khetan, Dev; Gupta, Deepak; Khanna, Ashish; Rodrigues, Joel J. P. C.; Pinheiro, Plácido Rogério

doi:10.1109/access.2020.3020115

Cited by 17 publications

(6 citation statements)

References 23 publications

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“…" In the field of the economy, solutions focus on aspects to develop a sustainable economy, including, improving seaport operations [12], charging solutions for IoE vehicles and EC-based resource allocation solutions [13]. " In the field of mobility, studies focus on solving the challenges of intelligent transportation systems, such as resources allocation solutions for IoE vehicles [14], optimal routing and estimation for vehicles [15]. "…”

Section: Related Workmentioning

confidence: 99%

An Edge Computing Collaboration Solution for Internet of Vehicles

Quy¹,

Anh²,

Nam³

et al. 2022

Proceedings of the Seventh International Conference on Research in Intelligent and Computing in Engineering

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The advent of ÿ þý generation communication systems (5G) in the early ÿ ýþ century has realized real-time Internet of Things applications. 5G has capable of providing network services with extremely-high throughput and extremely low delay and allows a huge device number to connect together based on Internet infrastructure, forming the Internet of Things (IoT). In recent years, IoT has been applied in a variety of fields serving humans, such as smart cities, smart agriculture, e-healthcare, smart education, military, and IoT ecosystems. One of the main challenges of IoT applications is computing solutions to reduce service response times. In this study, we propose an Edge Computing Collaboration Solution for the Internet of Vehicles (IoV). Our solution proposes a small database that allows edge computing servers of IoVs to store each other's information. When the mobile end-users move to the new edge servers' managed coverage, properties related to the EC service are exchanged between the edge servers. The results have shown that our proposed solution improves significantly service response time, by up to 10-20%, compared to the existing solutions. Index Terms45G, Internet of Things, Edge Computing, Internet of Vehicles (IoVs).

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Section: Related Workmentioning

confidence: 99%

An Edge Computing Collaboration Solution for Internet of Vehicles

Quy¹,

Anh²,

Nam³

et al. 2022

Proceedings of the Seventh International Conference on Research in Intelligent and Computing in Engineering

View full text Add to dashboard Cite

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“…In addition to it, the regular maintenance of the whole network also increases the total cost. Thus a complete financial report should be made before investing such a considerable amount in the infrastructure [48]. Table 6 shows cost of the SG in some developing countries.…”

Section: Future Directionsmentioning

confidence: 99%

“…Thus, the way of business is also changing. The new policies to govern the communication with customer benefits need to be deployed [6, 48]. The utility business model should be implemented at the distribution level to integrate the load and the generated power.…”

Section: Future Directionsmentioning

confidence: 99%

A comprehensive review on IoT‐based infrastructure for smart grid applications

Pal

Chavhan

Gupta

et al. 2021

IET Renewable Power Gen

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Currently, there is a huge production in the automotive industries and energy consumption resulted into spike in the cost and emissions of greenhouse gases. Hence, an efficient energy utilisation mechanism has become a prime most important factor. Energy and its conservation problem also have raised and become one of the prominent critical factors needed to address. Therefore, the development of smart grid infrastructure is one of the solutions to address the above issue.This article discusses different methods and mechanisms require to manage energy efficiently within the smart grid network using communication technologies and protocols and proposed an integration method of electric vehicles and smart grid using communication networks for charging or discharging electricity and exchanging relevant information. In addition, this paper discusses different integration strategies and multi-agent system implementation in the smart grid network. The proposed multi-agent system shows the efficient method for monitoring power flow and maintaining the stability of the grid. The paper also presents the optimal scheduling of charging of electric vehicles in the smart grid network. Finally, the authors investigated and presented many standard communication protocols and their comparisons with respect to different scenarios. This paper discuss different methods and concepts which would require to manage energy efficiently by using recent communication technologies, communication protocols, and sensor technologies. INTRODUCTIONDue to the growth of industries and population density, energy demand has been increasing exponentially. Hence, the energy consumption is under focus in order to reduce the energy crisis in the future. In this 21st century, conventional electrical energy and network will not meet up with the industries due to the lack of reliability, efficiency, security, seamless connectivity, etc. Therefore, a lot of new technologies (communication and sensor) have evolved to provide above features. The evolved communication and sensor technologies applied to the power grid to make smarter, that is, Smart Grid (SG) [1,2]. The SG infrastructure is the backbone of the future smart cities and the connected electric mobility. The connected infrastructureThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…This involves using one type of base learning algorithm to produce homogeneous base learners or using different types of learning algorithms to produce heterogeneous learners. The generalization ability of the constituent model(i.e ensemble) is usually much more than any of the individual contributing base learners [31]. Ensemble Learning can further be classified into three main categories such as bagging, boosting, and a bucket of models.…”

Section: B Ensemble Learningmentioning

confidence: 99%

An Empirical Analysis of Deep Learning Architectures for Vehicle Make and Model Recognition

et al. 2021

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Vehicle make and model recognition is an important component of the Intelligent Transport System (ITS), which plays an essential role in vehicle surveillance and traffic monitoring. In this paper, we evaluated the performance of the recent deep neural networks for vehicle recognition to identify 196 different types of vehicles based on their make, model, and year using Stanford Cars data. Transfer learning has been employed to reduce the training time and also added a dropout of 0.5 at the last dense layer before the output layer for improved generalization performance on an applied dataset. Different augmentation methods such as random erasing, horizontal flip, random crop, resize, rotate, shear, and sharpen were applied to the training dataset to increase the number of images. Besides, optimization methods namely mixup and cyclical learning rate were also investigated to train these deep neural networks to avoid overfitting. Finally, the proposed ensemble learning framework using k-fold cross-validation has resulted in the improved accuracy of image classification for the vehicle make and model recognition. The final achieved accuracy is 93.96% through ensembling homogeneous models of DenseNet201, which is higher than previous stateof-the-art approaches for the vehicle make and model recognition. Simultaneously, it has also improved the performance of other models where MobileNetV2 has shown the highest improvement with an increase of 2.8% in the overall accuracy using the proposed ensemble learning technique.INDEX TERMS deep neural networks, vehicle make and model recognition, ensemble learning, and k-fold cross-validation

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Next-Generation Smart Electric Vehicles Cyber Physical System for Charging Slots Booking in Charging Stations

Cited by 17 publications

References 23 publications

An Edge Computing Collaboration Solution for Internet of Vehicles

An Edge Computing Collaboration Solution for Internet of Vehicles

A comprehensive review on IoT‐based infrastructure for smart grid applications

An Empirical Analysis of Deep Learning Architectures for Vehicle Make and Model Recognition

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