arrow
Return

An efficient model for vehicular cloud computing with prioritizing computing resources

delete2018-09-11
delete15
PRE
AI
M
Masoud Tahmasebi *
M
Mohammad Reza Khayyambashi
DOI:10.1007/s12083-018-0677-6delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
In recent years, even though there has been a lot of progress in automotive industry and their offered services but not all of their computational capacities have yet been used. The vehicle's onboard computation capacity is underutilized, the power which can be used efficiently and it significantly reduces energy consumption. Considering the novelty of Vehicular Cloud Computing (VCC), the problems like its real cost and different kind of resource allocations in different applications remain an unexplored area. The mentioned problems with the global need for energy management have motivated us to propose an efficient model that considers expenses and response times which also appropriately utilizes onboard computation capacity for VCC. The proposed model is using VCC in a manner that the onboard computational capability is fully used. Since offloading tasks to Vehicular Cloud and remote cloud have additional cost, the goal is to do tasks locally and offload fewer tasks to the vehicular cloud and remote cloud. The model prioritizes computing resources and uses the onboard computing power, which was often ignored in the previous studies. Onboard computing resource provides reasonable response time and makes the model economically beneficial. After the model presentation and structure, simulation of the proposed model with the CloudAnalyst software and the results are presented and compared with appropriate references at the end. The results show that the proposed model can show a view of VCC with its advantages and disadvantages in a practical manner, it also displays the statistical data which compared to other scenarios, shows the superiority of the model.
Keywords:
Vehicular cloud computing
Cloud simulation
VCC simulation
Prioritizing computing resources
CloudAnalyst
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Peer-to-Peer Networking and Applications cover
Peer-to-Peer Networking and Applications
IF:
2.6
Papers:
2.2K
Citations:
2.9K

Organization

U
University of Isfahan
Scholars:
4.5K
Papers: 4.1K
Citations: 5
Cited Papers

Cited Papers

errShare
errSave
Multi-modal intervention to reduce cardiovascular risk among hypertensive older adults: Design of a randomized clinical trial
err2015-07-01
err0
errOAAI
errThomas W. Buford; Stephen D. Anton; Anthony A. Bavry; Christy S. Carter; Michael J. Daniels; Marco Pahor
errShare
errSave
Power Control in D2D-Based Vehicular Communication Networks
err2015-12-01
err129
PREAI
errRen, Yi; Liu, Fuqiang; Liu, Zhi; Wang, Chao; Ji, Yusheng
errShare
errSave
Software Defined Space-Air-Ground Integrated Vehicular Networks: Challenges and Solutions
err2017-01-01
err386
errOAAI
errZhang, Ning; Zhang, Shan; Yang, Peng; Alhussein, Omar; Zhuang, Weihua; Shen, Xuemin (Sherman)
errShare
errSave
Zn,Ni ferrite/NiO nanocomposite powder obtained from acetylacetonato complexes
err2006-09-11
err0
PREAI
errM Vučinić-Vasić; B Antic; A Kremenović; A S Nikolic; M Stoiljkovic; N Bibic; V Spasojevic; Ph Colomban
errShare
errSave
Motion Segmentation Via a Sparsity Constraint
err2017-04-01
err34
PREAI
errLai, Taotao; Wang, Hanzi; Yan, Yan; Chin, Tat-Jun; Zhao, Wan-Lei
errShare
errSave
PROVIDING LOCATION SECURITY IN VEHICULAR AD HOC NETWORKS
err2009-12-01
err60
PREAI
errYan, Gongjun; Olariu, Stephan; Weigle, Michele C.
errShare
errSave
researcher View more