Please use this identifier to cite or link to this item: http://dlib.scu.ac.ir/handle/Hannan/493990
Title: Predictive Offloading in Cloud-Driven Vehicles: Using Mobile-Edge Computing for a Promising Network Paradigm
Authors: Ke Zhang;Yuming Mao;Supeng Leng;Yejun He;Yan ZHANG
Year: 2017
Publisher: IEEE
Abstract: Cloud-based vehicular networks are a promising paradigm to improve vehicular services through distributing computation tasks between remote clouds and local vehicular terminals. To further reduce the latency and the transmission cost of the computation off-loading, we propose a cloud-based mobileedge computing (MEC) off-loading framework in vehicular networks. In this framework, we study the effectiveness of the computation transfer strategies with vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication modes. Considering the time consumption of the computation task execution and the mobility of the vehicles, we present an efficient predictive combination-mode relegation scheme, where the tasks are adaptively off-loaded to the MEC servers through direct uploading or predictive relay transmissions. Illustrative results indicate that our proposed scheme greatly reduces the cost of computation and improves task transmission efficiency.
URI: http://dl.kums.ac.ir/handle/Hannan/493990
volume: 12
issue: 2
More Information: 36,
44
Appears in Collections:2017

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Title: Predictive Offloading in Cloud-Driven Vehicles: Using Mobile-Edge Computing for a Promising Network Paradigm
Authors: Ke Zhang;Yuming Mao;Supeng Leng;Yejun He;Yan ZHANG
Year: 2017
Publisher: IEEE
Abstract: Cloud-based vehicular networks are a promising paradigm to improve vehicular services through distributing computation tasks between remote clouds and local vehicular terminals. To further reduce the latency and the transmission cost of the computation off-loading, we propose a cloud-based mobileedge computing (MEC) off-loading framework in vehicular networks. In this framework, we study the effectiveness of the computation transfer strategies with vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication modes. Considering the time consumption of the computation task execution and the mobility of the vehicles, we present an efficient predictive combination-mode relegation scheme, where the tasks are adaptively off-loaded to the MEC servers through direct uploading or predictive relay transmissions. Illustrative results indicate that our proposed scheme greatly reduces the cost of computation and improves task transmission efficiency.
URI: http://dl.kums.ac.ir/handle/Hannan/493990
volume: 12
issue: 2
More Information: 36,
44
Appears in Collections:2017

Files in This Item:
File Description SizeFormat 
7907225.pdf2 MBAdobe PDFThumbnail
Preview File
Title: Predictive Offloading in Cloud-Driven Vehicles: Using Mobile-Edge Computing for a Promising Network Paradigm
Authors: Ke Zhang;Yuming Mao;Supeng Leng;Yejun He;Yan ZHANG
Year: 2017
Publisher: IEEE
Abstract: Cloud-based vehicular networks are a promising paradigm to improve vehicular services through distributing computation tasks between remote clouds and local vehicular terminals. To further reduce the latency and the transmission cost of the computation off-loading, we propose a cloud-based mobileedge computing (MEC) off-loading framework in vehicular networks. In this framework, we study the effectiveness of the computation transfer strategies with vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication modes. Considering the time consumption of the computation task execution and the mobility of the vehicles, we present an efficient predictive combination-mode relegation scheme, where the tasks are adaptively off-loaded to the MEC servers through direct uploading or predictive relay transmissions. Illustrative results indicate that our proposed scheme greatly reduces the cost of computation and improves task transmission efficiency.
URI: http://dl.kums.ac.ir/handle/Hannan/493990
volume: 12
issue: 2
More Information: 36,
44
Appears in Collections:2017

Files in This Item:
File Description SizeFormat 
7907225.pdf2 MBAdobe PDFThumbnail
Preview File