A Data Download Method from RSUs using Fog Computing in Connected Vehicles

Kim, Dae-Young; Kim, Seokhoon

doi:10.32604/cmc.2019.06077

Cited by 11 publications

(6 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In complex networks, it is very important to determine the important nodes of information spreading, which can effectively control the spread of viruses in computer networks and diseases in social networks, as well as the spread of rumors, opinions, economic crisis and so on [1]. As we all know that there are many ways to communicate in Vehicular Ad Hoc Networks (VANETs) [2][3][4], for example between vehicles and vehicles (V2V), vehicles and infrastructure (V2I), vehicles and pedestrians (V2P), and vehicles and networks (V2N) [5]. In this paper, we focus on vehicle to vehicle transmission, and now we know that with the popularization and development of vehicle Internet technology, the information transmission between vehicles becomes more frequent.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Influence of Vehicle Mobility on Information Spreading in VANETs

2021

KSII TIIS

View full text Add to dashboard Cite

With the advent of 5G communications, internet of vehicles technology has been widely used in vehicles. Then the dynamic spread of information between vehicles began to come into focus with more research. It is well known that the identification of nodes with great spread influence has always been a hot topic in the field of information spreading. Most of the existing work measures the propagation influence by degree centrality, betweenness centrality and closeness centrality. In this paper, we will identify influential vehicle nodes based on the mobility characteristics of vehicles to explore the information spreading between vehicles in VANETs. Different from the above methods, we mainly explore the influence of the radius of gyration and vehicle kilometers of travel on information spreading. We use a real vehicle trajectory data to simulate the information transmission process between vehicles based on the susceptible-infected-recovered SIR model. The experimental results show that the influence of information spreading does not enhance with increasing radius of gyration and vehicle kilometers of travel. The fact is that both the radius of gyration and the distance travelled have a significant influence on information spreading when they are close to the median. When the value of both is large or small, it has little influence on information spreading. In view of this results, we can use the radius of gyration and vehicle kilometers of travel to better facilitate the transmission of information between vehicles.

show abstract

Section: Introductionmentioning

confidence: 99%

Exploring the Influence of Vehicle Mobility on Information Spreading in VANETs

2021

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…For data transmissions, the IoV allows Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Pedestrian (V2P) communication, etc. The Vehicle-to-X (V2X) communication uses Cellular-based communication technology (e.g., LTE and 5G) and IEEE 802.11 based dedicated short-range communication (DSRC) technology to communicate each other [ 16 , 17 , 18 ]. The IoV data is classified into two categories according to the location of occurrence: on-board data and on-road data.…”

Section: Introductionmentioning

confidence: 99%

An Internet of Vehicles (IoV) Access Gateway Design Considering the Efficiency of the In-Vehicle Ethernet Backbone

Kim

Jung²,

Kim

2020

Sensors

Self Cite

View full text Add to dashboard Cite

A vehicular network is composed of an in-vehicle network (IVN) and Internet of Vehicles (IoV). IVN exchanges information among in-vehicle devices. IoV constructs Vehicle-to-X (V2X) networks outside vehicles and exchanges information among V2X elements. These days, in-vehicle devices that require high bandwidth is increased for autonomous driving services. Thus, the spread of data for vehicles is exploding. This kind of data is exchanged through IoV. Even if the Ethernet backbone of IVN carries a lot of data in the vehicle, the explosive increase in data from outside the vehicle can affect the backbone. That is, the transmission efficiency of the IVN backbone will be reduced due to excessive data traffic. In addition, when IVN data traffic is transmitted to IoV without considering IoV network conditions, the transmission efficiency of IoV is also reduced. Therefore, in this paper, we propose an IoV access gateway to controls the incoming data traffic to the IVN backbone and the outgoing data traffic to the IoV in the network environment where IVN and IoV are integrated. Computer simulations are used to evaluate the performance of the proposed system, and the proposed system shows better performance in the accumulated average transmission delay.

show abstract

“…As parts of the network, fogs can be routers, switches, servers or mobile stations, and they provide lower delay than cloud computing. Applications such as flow processing, virtual reality/augmented reality, smart traffic control, smart homes and smart cities can benefit from fog computing [3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Support Mobile Fog Computing Test in piFogBedII

Zhang

Togookhuu

2020

Sensors

View full text Add to dashboard Cite

IoT and 5G technologies are making smart devices, medical devices, cameras and various types of sensors become parts of the Internet, which provides feasibility to the realization of infrastructure and services such as smart homes, smart cities, smart medical technology and smart transportation. Fog computing (edge computing) is a new research field and can accelerate the analysis speed and decision-making for these delay-sensitive applications. It is very important to test functions and performances of various applications and services before they are deployed to the production environment, and current evaluations are more based on various simulation tools; however, the fidelity of the experimental results is a problem for most of network simulation tools. PiFogBed is a fog computing testbed built with real devices, but it does not support the testing of mobile end devices and mobile fog applications. The paper proposes the piFogBedII to support the testing of mobile fog applications by modifying some components in the piFogBed, such as extending the range of end devices, adding the mobile and migration management strategy and inserting a container agent to implement the transparent transmission between end devices and containers. The evaluation results show that it is effective and the delay resulting from the migration strategy and container agent is acceptable.

show abstract

A Data Download Method from RSUs using Fog Computing in Connected Vehicles

Cited by 11 publications

References 10 publications

Exploring the Influence of Vehicle Mobility on Information Spreading in VANETs

Exploring the Influence of Vehicle Mobility on Information Spreading in VANETs

An Internet of Vehicles (IoV) Access Gateway Design Considering the Efficiency of the In-Vehicle Ethernet Backbone

Support Mobile Fog Computing Test in piFogBedII

Contact Info

Product

Resources

About