scholarly journals Computation Offloading in a Cognitive Vehicular Networks with Vehicular Cloud Computing and Remote Cloud Computing

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6820
Author(s):  
Shilin Xu ◽  
Caili Guo
Keyword(s):  
Cloud Computing ◽  
Vehicular Networks ◽  
Short Term Memory ◽  
Action Observation ◽  
Resource Discovery ◽  
Computation Offloading ◽  
Vehicular Cloud Computing ◽  
Vehicular Cloud ◽  
Vehicle Communication ◽  
Two Stages

To satisfy the explosive growth of computation-intensive vehicular applications, we investigated the computation offloading problem in a cognitive vehicular networks (CVN). Specifically, in our scheme, the vehicular cloud computing (VCC)- and remote cloud computing (RCC)-enabled computation offloading were jointly considered. So far, extensive research has been conducted on RCC-based computation offloading, while the studies on VCC-based computation offloading are relatively rare. In fact, due to the dynamic and uncertainty of on-board resource, the VCC-based computation offloading is more challenging then the RCC one, especially under the vehicular scenario with expensive inter-vehicle communication or poor communication environment. To solve this problem, we propose to leverage the VCC’s computation resource for computation offloading with a perception-exploitation way, which mainly comprise resource discovery and computation offloading two stages. In resource discovery stage, upon the action-observation history, a Long Short-Term Memory (LSTM) model is proposed to predict the on-board resource utilizing status at next time slot. Thereafter, based on the obtained computation resource distribution, a decentralized multi-agent Deep Reinforcement Learning (DRL) algorithm is proposed to solve the collaborative computation offloading with VCC and RCC. Last but not least, the proposed algorithms’ effectiveness is verified with a host of numerical simulation results from different perspectives.

Vehicular Cloud Computing

2014 ◽  
pp. 262-274 ◽  
Author(s):  
Kayhan Zrar Ghafoor ◽  
Marwan Aziz Mohammed ◽  
Kamalrulnizam Abu Bakar ◽  
Ali Safa Sadiq ◽  
Jaime Lloret
Keyword(s):  
Cloud Computing ◽  
Vehicular Networks ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Vehicular Cloud Computing ◽  
Vehicular Cloud ◽  
Performance Improvements ◽  
Potential Applications ◽  
Research Outcome ◽  
Hoc Networks

Recently, Vehicular Ad Hoc Networks (VANET) have attracted the attention of research communities, leading car manufacturers, and governments due to their potential applications and specific characteristics. Their research outcome was started with awareness between vehicles for collision avoidance and Internet access and then expanded to vehicular multimedia communications. Moreover, vehicles’ high computation, communication, and storage resources set a ground for vehicular networks to deploy these applications in the near future. Nevertheless, on-board resources in vehicles are mostly underutilized. Vehicular Cloud Computing (VCC) is developed to utilize the VANET resources efficiently and provide subscribers safe infotainment services. In this chapter, the authors perform a survey of state-of-the-art vehicular cloud computing as well as the existing techniques that utilize cloud computing for performance improvements in VANET. The authors then classify the VCC based on the applications, service types, and vehicular cloud organization. They present the detail for each VCC application and formation. Lastly, the authors discuss the open issues and research directions related to VANET cloud computing.

Vehicular Cloud Computing

2015 ◽  
pp. 1049-1061 ◽  
Author(s):  
Kayhan Zrar Ghafoor ◽  
Marwan Aziz Mohammed ◽  
Kamalrulnizam Abu Bakar ◽  
Ali Safa Sadiq ◽  
Jaime Lloret
Keyword(s):  
Cloud Computing ◽  
Vehicular Networks ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Vehicular Cloud Computing ◽  
Vehicular Cloud ◽  
Performance Improvements ◽  
Potential Applications ◽  
Research Outcome ◽  
Hoc Networks

Recently, Vehicular Ad Hoc Networks (VANET) have attracted the attention of research communities, leading car manufacturers, and governments due to their potential applications and specific characteristics. Their research outcome was started with awareness between vehicles for collision avoidance and Internet access and then expanded to vehicular multimedia communications. Moreover, vehicles' high computation, communication, and storage resources set a ground for vehicular networks to deploy these applications in the near future. Nevertheless, on-board resources in vehicles are mostly underutilized. Vehicular Cloud Computing (VCC) is developed to utilize the VANET resources efficiently and provide subscribers safe infotainment services. In this chapter, the authors perform a survey of state-of-the-art vehicular cloud computing as well as the existing techniques that utilize cloud computing for performance improvements in VANET. The authors then classify the VCC based on the applications, service types, and vehicular cloud organization. They present the detail for each VCC application and formation. Lastly, the authors discuss the open issues and research directions related to VANET cloud computing.

Reply to comment on ‘SFVCC: Chaotic map-based security framework for vehicular cloud computing’

2020 ◽  
Vol 14 (12) ◽  
pp. 1724-1724
Author(s):  
Dheerendra Mishra ◽  
Vinod Kumar ◽  
Dharminder Dhaminder ◽  
Saurabh Rana
Keyword(s):  
Cloud Computing ◽  
Chaotic Map ◽  
Security Framework ◽  
Vehicular Cloud Computing ◽  
Vehicular Cloud

scholarly journals An energy-efficient failure detector for vehicular cloud computing

PLoS ONE ◽  
2018 ◽  
Vol 13 (1) ◽  
pp. e0191577 ◽  
Author(s):  
Jiaxi Liu ◽  
Zhibo Wu ◽  
Jian Dong ◽  
Jin Wu ◽  
Dongxin Wen
Keyword(s):  
Cloud Computing ◽  
Energy Efficient ◽  
Failure Detector ◽  
Vehicular Cloud Computing ◽  
Vehicular Cloud

Vehicular Clouds

2018 ◽  
pp. 1-29
Author(s):  
Ryan Florin ◽  
Stephan Olariu
Keyword(s):  
Cloud Computing ◽  
Recent Work ◽  
Vehicular Networks ◽  
Research Community ◽  
Active Area ◽  
Vehicular Cloud ◽  
The Future

Vehicular clouds is an active area of research that has emerged at the nexus of conventional cloud computing and vehicular networks. The defining differences between conventional and vehicular clouds include the heterogeneity and volatility of compute resources and the bandwidth-challenged network fabric. A variety of new architectures and services for vehicular clouds have been proposed, mostly as incremental extensions of the VANET platform. As vehicular cloud research continues and expands, a careful eye should be kept on the restrictions that come with the mobility, limited network, and heterogeneity of resources. The first main contribution of this chapter is to survey recent work of VCs with an eye on the realistic and unrealistic. Our second main goal is to realign the VC community with a realistic vision for the future by spelling out a number of challenges faced by the VC research community.

Vehicular Cloud Computing Security: A Survey

2019 ◽  
Vol 45 (4) ◽  
pp. 2473-2499
Author(s):  
Hadjer Goumidi ◽  
Zibouda Aliouat ◽  
Saad Harous
Keyword(s):  
Cloud Computing ◽  
Cloud Computing Security ◽  
Vehicular Cloud Computing ◽  
Vehicular Cloud

scholarly journals A Safety Resource Allocation Mechanism against Connection Fault for Vehicular Cloud Computing

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Tianpeng Ye ◽  
Zhou Su ◽  
Jun Wu ◽  
Longhua Guo ◽  
Jianhua Li ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Real Time ◽  
Traffic Control ◽  
Intelligent Transportation System ◽  
Probability Model ◽  
Allocation Mechanism ◽  
Vehicular Cloud Computing ◽  
Vehicular Cloud ◽  
Resource Allocation Mechanism

The Intelligent Transportation System (ITS) becomes an important component of the smart city toward safer roads, better traffic control, and on-demand service by utilizing and processing the information collected from sensors of vehicles and road side infrastructure. In ITS, Vehicular Cloud Computing (VCC) is a novel technology balancing the requirement of complex services and the limited capability of on-board computers. However, the behaviors of the vehicles in VCC are dynamic, random, and complex. Thus, one of the key safety issues is the frequent disconnections between the vehicle and the Vehicular Cloud (VC) when this vehicle is computing for a service. More important, the connection fault will disturb seriously the normal services of VCC and impact the safety works of the transportation. In this paper, a safety resource allocation mechanism is proposed against connection fault in VCC by using a modified workflow with prediction capability. We firstly propose the probability model for the vehicle movement which satisfies the high dynamics and real-time requirements of VCC. And then we propose a Prediction-based Reliability Maximization Algorithm (PRMA) to realize the safety resource allocation for VCC. The evaluation shows that our mechanism can improve the reliability and guarantee the real-time performance of the VCC.

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