P2P Network Based Smart Parking System Using Edge Computing

Zhang, Nan; Lu, Xu; Tian, Cong; Duan, Zhenhua; Sun, Zhen; Zhang, Ting

doi:10.1007/s11036-020-01660-x

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…Figure 6 shows an example where two connected This use case requires both V2I and V2V communication since the vehicles need to access an application running typically in the cloud. Note also, that Peer-to-Peer (P2P) network based smart parking systems exist, and because of complexity of big data they require computation at the edge [60]. Hence, AI and cloudification are the key technology trends enabling this use case.…”

Section: Smart Parkingmentioning

confidence: 99%

Sustainable Mobility in B5G/6G: V2X Technology Trends and Use Cases

Roger,

Botella-Mascarell,

Martín-Sacristán

et al. 2024

IEEE Open J. Veh. Technol.

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The concept of sustainability has been recently extended to cover economic and social factors besides the traditional environmental ones. This paper reflects on the potential of mobile communication standards towards achieving sustainable mobility, with focus on vehicular communications and use cases in smart cities scenarios. In this context, intelligent transportation systems, including connected and autonomous vehicles, will be key for developing affordable and sustainable infrastructures and services. We start by identifying three current technology trends, namely, towards climate neutral; cloudification and edge computing; and big data and artificial intelligence, and then we examine their capability to enable sustainable Vehicle-to-Everything (V2X) communication systems in beyond 5G and 6G networks. In the second part of the paper, a set of selected use case categories involving connected and autonomous vehicles is presented, showcasing the potential impact of the selected technology trends. Finally, a review of the estimates of the quantitative savings that could be achieved in environmental-related parameters such as energy/fuel consumption and greenhouse gas emissions is provided.

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Section: Smart Parkingmentioning

confidence: 99%

Sustainable Mobility in B5G/6G: V2X Technology Trends and Use Cases

Roger,

Botella-Mascarell,

Martín-Sacristán

et al. 2024

IEEE Open J. Veh. Technol.

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“…Zhang et al [15] proposed a parking system which makes use of Edge Computing. The objective of this study was to generate a P2P based smart parking management system which uses cloud computing, edge computing and P2P network techniques which helps in navigation, enquiries etc.…”

Section: Data Science and Intelligent Computing Techniquesmentioning

confidence: 99%

A Survey on Smart Parking Management System

Ramchandani¹,

Bansal²

2023

Data Science and Intelligent Computing Techniques

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With the large increase in population, automated industries and need of vehicles, parking of the vehicles is becoming a critical issue in various cities. Unmanaged parking of vehicles leads to noise pollution, air pollution, traffic congestion. During peak hours, it is difficult task to find vacant parking lot and it becomes the major challenge for driver to park the vehicle. A lot of work is being done in the whole world to manage the efficient parking of vehicles. To give the clear overview about efficient parking system, we go through some existing studies over the period of 2009-2022 which proposed various parking solutions. This survey gives an exhaustive study of available parking solutions and also proposed som

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Detection of car parking space by using Hybrid Deep DenseNet Optimization algorithm

Rajyalakshmi

Lakshmanna

2023

Int J Network Mgmt

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Internet of Things (IoT) and related applications have revolutionized most of our societal activities, enhancing the quality of human life. This study presents an IoT‐based model that enables optimized parking space utilization. The paper implements a Hybrid Deep DenseNet Optimization (HDDNO) algorithm for predicting parking spot availability involving Machine Learning (ML) and deep learning techniques. The HDDNO‐based ML model uses secondary data from the National Research Council Park (CNRPark) in Pisa, Italy. Different regression algorithms are employed to forecast parking lot availability for a given time as part of the prediction process. The DenseNet technique has generated promising results, whereas the HDDNO model yielded better accuracy. The use of five optimizers, namely, Adaptive Moment Estimation (Adam), Root Mean Squared Propagation (RMSprop), Adaptive Gradient (AdaGrad), AdaDelta, and Stochastic Gradient Descent (SGD), have played significant roles in minimizing the loss of the model. The part of Adam has enabled the HDDNO model to generate predictions with high accuracy 99.19% and low loss 0.0306%. This proposed methodology would significantly improve environmental safety and act as an initiative toward developing smart cities.

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P2P Network Based Smart Parking System Using Edge Computing

Cited by 4 publications

References 29 publications

Sustainable Mobility in B5G/6G: V2X Technology Trends and Use Cases

Sustainable Mobility in B5G/6G: V2X Technology Trends and Use Cases

A Survey on Smart Parking Management System

Detection of car parking space by using Hybrid Deep DenseNet Optimization algorithm

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