Short-Term Traffic Flow Prediction of the Smart City Using 5G Internet of Vehicles Based on Edge Computing

Zhou, Shenghan; Wei, Chenxi; Song, Chaofei; Pan, Xing; Chang, Wenbing; Yang, Linchao

doi:10.1109/tits.2022.3147845

Cited by 30 publications

(12 citation statements)

References 42 publications

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“…This method employs both the deep convolutional random forest technique and the edge computing technique to scale down the congestion. Finally, the simulation result signifies that the proposed scheme ensures a good unloading performance while maintaining a higher level of prediction 66 . Besides, the author presented a management model for improving the movement of garbage disposal electric vehicles (GDEVs) within smart cities.…”

Section: Literature Reviewmentioning

confidence: 92%

“…simulation result signifies that the proposed scheme ensures a good unloading performance while maintaining a higher level of prediction. 66 Besides, the author presented a management model for improving the movement of garbage disposal electric vehicles (GDEVs) within smart cities. The model is developed by integrating three different technologies, that is, the IoT, deep neural network (DNN), and the blockchain technique.…”

Section: Intelligent Transportationmentioning

confidence: 99%

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A review on evolving domains of Internet of Things: Architecture, applications, and technical challenges

Sharma,

Sangwan,

Singh

2023

Int J Communication

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SummaryThe Internet of Things (IoT) is a system that includes smart items with different sensors, advanced technologies, analytics, cloud servers, and other wireless devices that integrate and work together to create an intelligent environment that benefits end users. With its wide spectrum of applications, IoT is revolutionizing both the current and future generations of the Internet. IoT systems can be employed for broad‐ranging real applications, such as agriculture, the environment, cities, healthcare, and the industrial sector. In this paper, we briefly discuss the three‐tier architectural view of IoT, its different communication technologies, and the smart sensors. Moreover, we study various application areas of IoT such as the environmental domain, healthcare, agriculture, smart cities, and industrial, commercial, and general aspects. A critical analysis is shown for the existing schemes and techniques related to this work. Further, this paper addresses the basic context, tools and evaluation approaches, future scope, and the advantages and disadvantages of the aforestated IoT applications. A comprehensive analysis is provided for each domain along with its fundamental parameters like the quality of service (QoS), network longevity, scalability, energy efficiency, accuracy, and cost. Finally, this study highlights the technical challenges and open research problems existing in different IoT applications.

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Section: Literature Reviewmentioning

confidence: 92%

Section: Intelligent Transportationmentioning

confidence: 99%

A review on evolving domains of Internet of Things: Architecture, applications, and technical challenges

Sharma,

Sangwan,

Singh

2023

Int J Communication

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“…Today is the age of ubiquitous smart sensors, ubiquitous social networking, and cloud computing 3 . Most of the world's populace currently resides in cities, as reported by the United Nations in 2014, which should serve as a warning to city planners everywhere.…”

Section: Introductionmentioning

confidence: 99%

A vehicular network based intelligent transport system for smart cities using machine learning algorithms

Prakash,

Murali,

Manikandan

et al. 2024

Sci Rep

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Smart cities and the Internet of Things have enabled the integration of communicating devices for efficient decision-making. Notably, traffic congestion is one major problem faced by daily commuters in urban cities. In developed countries, specialized sensors are deployed to gather traffic information to predict traffic patterns. Any traffic updates are shared with the commuters via the Internet. Such solutions become impracticable when physical infrastructure and Internet connectivity are either non-existent or very limited. In case of developing countries, no roadside units are available and Internet connectivity is still an issue in remote areas. Internet traffic analysis is a thriving field of study due to the myriad ways in which it may be put to practical use. In the intelligent Internet-of-Vehicles (IOVs), traffic congestion can be predicted and identified using cutting-edge technologies. Using tree-based decision-tree, random-forest, extra-tree, and XGBoost machine learning (ML) strategies, this research proposes an intelligent-transport-system for the IOVs-based vehicular network traffic in a smart city set-up. The suggested system uses ensemble learning and averages the selection of crucial features to give high detection accuracy at minimal computational costs, as demonstrated by the simulation results. For IOV-based vehicular network traffic, the tree-based ML approaches with feature-selection (FS) outperformed those without FS. When contrasted to the lowest KNN accuracy of 96.6% and the highest SVM accuracy of 98.01%, the Stacking approach demonstrates superior accuracy as 99.05%.

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“…However, cloud computing suffers from limited bandwidth for data transfer and significant latency when transferring large amounts of video data [ 11 , 12 ]. To address these issues, edge computing is emerging as a solution, where deep learning servers are placed closer to the physical locations where video data is generated, allowing data to be processed and analyzed at the edge [ 13 , 14 ]. Edge computing is a horizontal architecture that distributes computing resources on edge servers closer to users and edge devices.…”

Section: Introductionmentioning

confidence: 99%

MDED-Framework: A Distributed Microservice Deep-Learning Framework for Object Detection in Edge Computing

Seo

Jang

Cha

et al. 2023

Sensors

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The demand for deep learning frameworks capable of running in edge computing environments is rapidly increasing due to the exponential growth of data volume and the need for real-time processing. However, edge computing environments often have limited resources, necessitating the distribution of deep learning models. Distributing deep learning models can be challenging as it requires specifying the resource type for each process and ensuring that the models are lightweight without performance degradation. To address this issue, we propose the Microservice Deep-learning Edge Detection (MDED) framework, designed for easy deployment and distributed processing in edge computing environments. The MDED framework leverages Docker-based containers and Kubernetes orchestration to obtain a pedestrian-detection deep learning model with a speed of up to 19 FPS, satisfying the semi-real-time condition. The framework employs an ensemble of high-level feature-specific networks (HFN) and low-level feature-specific networks (LFN) trained on the MOT17Det dataset, achieving an accuracy improvement of up to AP50 and AP0.18 on MOT20Det data.

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Short-Term Traffic Flow Prediction of the Smart City Using 5G Internet of Vehicles Based on Edge Computing

Cited by 30 publications

References 42 publications

A review on evolving domains of Internet of Things: Architecture, applications, and technical challenges

A review on evolving domains of Internet of Things: Architecture, applications, and technical challenges

A vehicular network based intelligent transport system for smart cities using machine learning algorithms

MDED-Framework: A Distributed Microservice Deep-Learning Framework for Object Detection in Edge Computing

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