Fog Following Me: Latency and Quality Balanced Task Allocation in Vehicular Fog Computing

Zhu, Chao; Pastor, Giancarlo; Xiao, Yu; Li, Yong; Ylae-Jaeaeski, Antti

doi:10.1109/sahcn.2018.8397129

Cited by 80 publications

(56 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the responding messages, the client vehicle can choose one fog node for offloading. In this paper, we just adopt a simple fog node selection scheme, in which the client vehicle would select the fog node with the shortest communication distance for task offloading [6], [18].…”

Section: B Process Of Chameleonmentioning

confidence: 99%

See 1 more Smart Citation

Chameleon: Latency and Resolution Aware Task Offloading for Visual-Based Assisted Driving

Zhu

Chiang

Mehrabi

et al. 2019

IEEE Trans. Veh. Technol.

Self Cite

View full text Add to dashboard Cite

Emerging visual-based driving assistance systems involve time-critical and data-intensive computational tasks, such as real-time object recognition and scene understanding. Due to the constraints on space and power capacity, it is not feasible to install extra computing devices on all the vehicles. To solve this problem, different scenarios of vehicular fog computing have been proposed, where computational tasks generated by vehicles can be sent to and processed at fog nodes located for example at 5G cell towers or moving buses. In this paper, we propose Chameleon, a novel solution for task offloading for visual-based assisted driving. Chameleon takes into account the spatiotemporal variation in service demand and supply, and provides latency and resolution aware task offloading strategies based on partially observable Markov decision process (POMDP). To evaluate the effectiveness of Chameleon, we simulate the availability of vehicular fog nodes at different times of day based on the bus trajectories collected in Helsinki, and use the real-world performance measurements of visual data transmission and processing. Compared with adaptive and random task offloading strategies, the POMDP-based offloading strategies provided by Chameleon shortens the average service latency of task offloading by up to 65% while increasing the average resolution level of processed images by up to 83%. Index Terms-Vehicular fog computing, task offloading, assisted driving, POMDP. I. INTRODUCTION E MERGING visual-based assisted driving applications, such as see-through and cooperative lane-change, involve time-critical and data-intensive computational tasks, such as real-time object recognition and scene understanding from images/video. Obviously, processing visual data demands for a lot more computing power, compared with other sensor data like Manuscript

show abstract

Section: B Process Of Chameleonmentioning

confidence: 99%

“…To provide low-latency processing for assisted driving, a novel computing paradigm called vehicular fog computing (VFC) [2]- [6] has been proposed. Its key idea is to offload computational tasks from the client vehicles where data is generated to fog nodes located at for example 5G cell towers or buses with extra computing power.…”

mentioning

confidence: 99%

Chameleon: Latency and Resolution Aware Task Offloading for Visual-Based Assisted Driving

Zhu

Chiang

Mehrabi

et al. 2019

IEEE Trans. Veh. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…On the other hand, offloading these tasks to the cloud is not applicable, due to the remarkable transmission delay. Thus, the concept of vehicular fog computing has been proposed and widely explored by plenty of works [18], [19], [20], [21], [22]. Xiao et al [19] proposed to turn commercial fleets into fog nodes to serve neighboring vehicles and passengers, while Hou et al [18] suggested utilizing the extra computing power on slow moving or parked vehicles.…”

Section: B Vehicular Fog Computingmentioning

confidence: 99%

“…Furthermore, Zhu et at. [21], [22] designed a system for latency and performance balanced task offloading for video transmission and processing in vehicular networking.…”

Section: B Vehicular Fog Computingmentioning

confidence: 99%

See 1 more Smart Citation

CrowdParking: Crowdsourcing Based Parking Navigation in Autonomous Driving Era

Zhu

Mehrabi

Xiao

et al. 2019

2019 International Conference on Electromagnetics in Advanced Applications (ICEAA)

Self Cite

View full text Add to dashboard Cite

Finding a free road side parking in urban area is considered as one of the most challenging driving tasks, especially for the autonomous vehicles with limited sight (e.g. short range sensing) and brain (compared with human beings). To assist autonomous vehicle parking in urban area, we propose a novel parking scheme CrowdParking, which applies crowdsourcing and vehicular fog computing to collect parking information from vehicles, locate free parking spaces from crowdsourced data. We also explore the variation of parking availability from a real world data set and find that the availability of specific parking lot has certain relationship with the traffic condition of nearby roads. Based on the observations, we propose the vision of estimating the parking availability with taking into account the traffic condition in neighborhood.

show abstract

6G‐based resource utilization framework in software defined hybrid vehicular networks

Mittal

2022

Int J Communication

View full text Add to dashboard Cite

SummaryAdvanced development in technology raises a huge number of data sources that generate real time data for processing. To process such huge and complex real time data, there is a need for high processing units and more computing resources available at the nearest end, which causes high deployment cost and needs distributed data processing units at different locations. To concern this issue, we proposed a 6G‐based Resource Utilization Framework using SDN in the hybrid vehicular network (HVN). The key objective of this framework is to provide the computing vehicles as resources to complete the task efficiently. Three different technologies are combined into vehicular networks such as Vehicular Fog Computing, Vehicular Edge Computing, and Vehicular Cyber Physical System into integrated HVN. A layered architecture is used to define the data flow and control with the support of SDN and also the fast delivery of data by using 6G cellular communication in the proposed HVN. This scheme uses various algorithms to provide resources through the fast communication between vehicles in Hybrid Vehicular network. An extensive simulation is performed on the proposed scheme using the NS3 simulator. The simulation results outperform the performance of the proposed scheme in terms of various key computing parameters.

show abstract

Fog Following Me: Latency and Quality Balanced Task Allocation in Vehicular Fog Computing

Cited by 80 publications

References 27 publications

Chameleon: Latency and Resolution Aware Task Offloading for Visual-Based Assisted Driving

Chameleon: Latency and Resolution Aware Task Offloading for Visual-Based Assisted Driving

CrowdParking: Crowdsourcing Based Parking Navigation in Autonomous Driving Era

6G‐based resource utilization framework in software defined hybrid vehicular networks

Contact Info

Product

Resources

About