Internet of vehicle's resource management in 5G networks using AI technologies: Current status and trends

Elfatih, Nada M.; Hasan, Mohammad Kamrul; Kamal, Zeinab; Gupta, Deepa; Saeed, Rashid A.; Ali, Elmustafa Sayed; Hosain, Md. Sarwar

doi:10.1049/cmu2.12315

Cited by 54 publications

(17 citation statements)

References 129 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…is method however was not able to perform well in datasets that the model is not trained in suggesting that it requires more data to train the model. e authors also did not test the model under heavy occlusion settings [6][7][8].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm

Khalifa

Wajdi

Saeed

et al. 2022

Journal of Advanced Transportation

Self Cite

View full text Add to dashboard Cite

Vehicle detection in Intelligent Transportation Systems (ITS) is a key factor ensuring road safety, as it is necessary for the monitoring of vehicle flow, illegal vehicle type detection, incident detection, and vehicle speed estimation. Despite the growing popularity in research, it remains a challenging problem that must be solved. Hardware-based solutions such as radars and LIDAR are been proposed but are too expensive to be maintained and produce little valuable information to human operators at traffic monitoring systems. Software based solutions using traditional algorithms such as Histogram of Gradients (HOG) and Gaussian Mixed Model (GMM) are computationally slow and not suitable for real-time traffic detection. Therefore, the paper will review and evaluate different vehicle detection methods. In addition, a method of utilizing Convolutional Neural Network (CNN) is used for the detection of vehicles from roadway camera outputs to apply video processing techniques and extract the desired information. Specifically, the paper utilized the YOLOv5s architecture coupled with k-means algorithm to perform anchor box optimization under different illumination levels. Results from the simulated and evaluated algorithm showed that the proposed model was able to achieve a mAP of 97.8 in the daytime dataset and 95.1 in the nighttime dataset.

show abstract

Section: Related Workmentioning

confidence: 99%

“…For this work, the location that is chosen is the Duke Highway at Taman Selasih, Gombak, and Selangor. is area is chosen due to a few reasons [6].…”

Section: Data Collectionmentioning

confidence: 99%

Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm

Khalifa

Wajdi

Saeed

et al. 2022

Journal of Advanced Transportation

Self Cite

View full text Add to dashboard Cite

show abstract

“…The mobile communications and networks ecosystem, to some extent restrict its progress by developing unique technologies. The 5G mobile network would inspire the IT and CT integration to discover more new services and applications to allow all industries types to contribute to the transformation toward digital [32][33]. The customization and openness abilities will growth in the next mobile generation, with more interfaces for API that support agile and flexible businesses for customers to qualify and fulfil the deploying requirement of tailored customized and networks services [34].…”

Section: Openness and Customizationmentioning

confidence: 99%

Green communication in 6G

Hoque¹,

Farhad²,

Mokhtar³

et al. 2022

IET Conf. Proc.

View full text Add to dashboard Cite

Green communication networking is a part of sustainable development. It aims to reduce energy consumption and serve the network to a vast number of servers cost-effectively. Green communication integrates artificial intelligence to solve complex mathematical problems for enhancing energy efficiency, flexibility, security, and quality of life services. One of the most remarkable characteristics of today's telecoms sector is wireless communication, used for more than a century. Infrared, radio frequency, satellite, and other electromagnetic waves are used in wireless communication technologies to send data over the air. More data is necessary for the 6G experience. More data is necessary and more excellent ambient sensing and awareness. Sensing systems in automated cars, for example, are exceedingly complicated and rely on cellular networks. Mobile and wireless networks, such as 6G, can use green communication to balance resource usage and conserve energy. In this study, 6G for green communication has been discussed to justify the necessity of 6G.

show abstract

“…Today's advancements in machine learning allowed the development of autonomous robots which can adapt, respond, and interact with their environment [21]. This adaptation has led to enhanced human-machine collaboration in terms of building companion robots, autonomous vehicles, and exoskeletons, companion robots perform certain tasks which are meant to improve patient's quality of life by health and emotional monitoring, entertainment, and communication and assisting [22].…”

Section: Assistive and Medical Technologiesmentioning

confidence: 99%

Robotics architectures based machine learning and deep learning approaches

Alatabani¹,

Ali²,

Mokhtar³

et al. 2022

IET Conf. Proc.

View full text Add to dashboard Cite

Robotics has been playing a vital role in our daily lives with a wide range of applications to improve the quality of life. With a variety of usable applications in the medical, manufacturing, and transportation industries, there is a continuous need for improving the performance of robotics for the importance of precision in executing commands and tasks. The implementation of precise commands has led to intense research on approaches to improve the performance of robotics. Machine Learning (ML) and Deep Learning (DL) have been drawing attention to applying architectures and algorithms to robotics which imposed a positive impact on the field of robotics. ML and DL applications in robotics include areas of computer vision, imitation learning, self-supervised learning, assistive and medical technologies, multi-agent learning, and manufacturing. This paper provides a comprehensive review of autonomous vs automatic robotics, robotic applications, extreme learning machine methods, and ML for soft robotics applications, in addition, to discussing the challenges, and future trends for AI applications in robotics applications.

show abstract

Internet of vehicle's resource management in 5G networks using AI technologies: Current status and trends

Cited by 54 publications

References 129 publications

Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm

Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm

Green communication in 6G

Robotics architectures based machine learning and deep learning approaches

Contact Info

Product

Resources

About