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

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.

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.

A Brief Statement on the Future of Public Scholarship and the Research Methods Landscape

2019 ◽  
pp. 700-707
Author(s):  
Patricia Leavy
Keyword(s):  
Research Methods ◽  
Teaching Practices ◽  
Knowledge Building ◽  
Research Community ◽  
The State ◽  
Public Scholarship ◽  
Institutional Structures ◽  
The Future ◽  
Book Editor

The book editor offers some final comments about the state of the field and promise for the future. Leavy suggests researchers consider using the language of “shapes” to talk about the forms their research takes and to highlight the ongoing role of the research community in shaping knowledge-building practices. She reviews the challenges and rewards of taking your work public. Leavy concludes by noting that institutional structures need to evolve their rewards criteria in order to meet the demands of practicing contemporary research and suggests that professors update their teaching practices to bring the audiences of research into the forefront of discussions of methodology.

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 Industrial policy-making after COVID-19: Manufacturing, innovation and sustainability

2021 ◽  
pp. 103530462110147
Author(s):  
Mark Dean ◽  
Al Rainnie ◽  
Jim Stanford ◽  
Dan Nahum
Keyword(s):  
Recent Work ◽  
Industrial Policy ◽  
Industrial Revolution ◽  
Manufacturing Sector ◽  
The Future ◽  
Australian Economy ◽  
Industry Policy ◽  
Future Work ◽  
Policy Proposals ◽  
Recent Policy

This article critically analyses the opportunities for Australia to revitalise its strategically important manufacturing sector in the wake of the COVID-19 pandemic. It considers Australia’s industry policy options on the basis of both advances in the theory of industrial policy and recent policy proposals in the Australian context. It draws on recent work from The Australia Institute’s Centre for Future Work examining the prospects for Australian manufacturing renewal in a post-COVID-19 economy, together with other recent work in political economy, economic geography and labour process theory critically evaluating the Fourth Industrial Revolution (i4.0) and its implications for the Australian economy. The aim of the article is to contribute to and further develop the debate about the future of government intervention in manufacturing and industry policy in Australia. Crucially, the argument links the future development of Australian manufacturing with a focus on renewable energy. JEL Codes: L50; L52; L78; O10; O13: O25; O44; P18; Q42

scholarly journals On the Design of Efficient Hierarchic Architecture for Software Defined Vehicular Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1400
Author(s):  
Muhammad Adnan ◽  
Jawaid Iqbal ◽  
Abdul Waheed ◽  
Noor Ul Amin ◽  
Mahdi Zareei ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Traffic Management ◽  
Vehicular Networks ◽  
Scheduling Algorithm ◽  
Main Research ◽  
First Come First Serve ◽  
Computing Framework ◽  
Simulation Results

Modern vehicles are equipped with various sensors, onboard units, and devices such as Application Unit (AU) that support routing and communication. In VANETs, traffic management and Quality of Service (QoS) are the main research dimensions to be considered while designing VANETs architectures. To cope with the issues of QoS faced by the VANETs, we design an efficient SDN-based architecture where we focus on the QoS of VANETs. In this paper, QoS is achieved by a priority-based scheduling algorithm in which we prioritize traffic flow messages in the safety queue and non-safety queue. In the safety queue, the messages are prioritized based on deadline and size using the New Deadline and Size of data method (NDS) with constrained location and deadline. In contrast, the non-safety queue is prioritized based on First Come First Serve (FCFS) method. For the simulation of our proposed scheduling algorithm, we use a well-known cloud computing framework CloudSim toolkit. The simulation results of safety messages show better performance than non-safety messages in terms of execution time.

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
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