You are not logged in.
Openly accessible

A cooperative-based model for smart-sensing tasks in fog computing

Li, Ting, Liu, Yuxin, Gao, Longxiang and Liu, Anfeng 2017, A cooperative-based model for smart-sensing tasks in fog computing, IEEE access, vol. 5, pp. 21296-21311, doi: 10.1109/ACCESS.2017.2756826.

Attached Files
Name Description MIMEType Size Downloads
gao-cooperativebased-2017.pdf Published version application/pdf 9.30MB 4

Title A cooperative-based model for smart-sensing tasks in fog computing
Author(s) Li, Ting
Liu, Yuxin
Gao, LongxiangORCID iD for Gao, Longxiang orcid.org/0000-0002-3026-7537
Liu, Anfeng
Journal name IEEE access
Volume number 5
Start page 21296
End page 21311
Total pages 16
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2017-09-23
ISSN 2169-3536
Keyword(s) fog computing
cloud computing
smart-sensing tasks
costs
coverage
Summary OAPA Fog Computing is currently receiving a great deal of focused attention. Fog Computing can be viewed as an extension of cloud computing that services the edges of networks. A cooperative relationship among applications to collect data in a city is a fundamental research topic in Fog Computing (FC). When considering the Green Cloud (GC), people or vehicles with smart-sensor devices can be viewed as users in FC and can forward sensing data to the data center (DC). In a traditional sensing process, rewards are paid according to the distances between the users and the platform, which can be seen as the existing solution. Because users with smart-sensing devices tend to participate in tasks with high rewards, the number of users in suburban regions is smaller, and data collection is sparse and cannot satisfy the demands of the tasks. However, there are many users in urban regions, which makes data collection costly and of low quality. In this paper, a cooperative-based model for smartphone tasks, named a Cooperative-based Model for Smart-Sensing Tasks (CMST), is proposed to promote the quality of data collection in FC networks. In the CMST scheme, we develop an allocation method focused on improving the rewards in suburban regions. The rewards to each user with a smart sensor are distributed according to the region density. Moreover, for each task there is a cooperative relationship among the users; they cooperate with one another to reach the volume of data that the platform requires. Extensive experiments show that our scheme improves the overall data-coverage factor by 14.997% to 31.46%, and the platform cost can be reduced by 35.882%
Language eng
DOI 10.1109/ACCESS.2017.2756826
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2017, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30104100

Document type: Journal Article
Collections: School of Information Technology
Open Access Collection
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 4 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 19 Abstract Views, 3 File Downloads  -  Detailed Statistics
Created: Thu, 02 Nov 2017, 14:07:45 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.