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A Multi-Objective Crowdsourcing Method for Mobile Video Streaming

delete2019-07-01
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PRE
AI
许
许小龙 (Xiaolong Xu) *
S
Shucun Fu
L
Lianyong Qi
X
Xuyun Zhang
W
Wanchun Dou
DOI:10.1109/ICWS.2019.00043delete
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Abstract

Abstract

En 中文
Due to the high demands of mobile video streaming, wireless networks have witnessed great pressure on increasing the transmitting rate. Crowdsourcing sets the trend of ensuring direct communication among the participants, thus expanding the bandwidth of networks, shunting the traffic volume of the core networks and improving the video service quality for mobile users. However, irregularly responding to the requestors poses a threat to the battery life of the mobile devices, and decreasing the service time and incomes of the providers remains challenging. To address this challenge, we propose a multi-objective crowdsourcing method, named MCM, for mobile video streaming. Technically, DBSCAN (Density-based Spatial Clustering for Applications with Noise) and IDP (Improved Dynamic Programming) are utilized to generate the service strategies. Consequently, experimental evaluations are conducted to demonstrate the efficiency of MCM.
Keywords:
crowdsourcing
video
mobile environment
energy
time
AI Summary

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

Journal

I
IEEE International Conference on Web Services
IF:
0
Papers:
9
Citations:
0

Organization

U
University of Auckland
Scholars:
2.3W
Papers: 2.4W
Citations: 3.3W
N
nanjing university
Scholars:
7.8W
Papers: 5.6W
Citations: 87
Q
Qufu Normal University
Scholars:
7.8K
Papers: 5.8K
Citations: 5.4K
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Cited Papers

Cited Papers

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Incentive Mechanism for Mobile Crowdsourcing Using an Optimized Tournament Model
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err2005-07-01
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errLeanne K. Armand; Xavier Crosta; Oscar Romero; Jean-Jacques Pichon
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Challenges in Data Crowdsourcing
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errGarcia-Molina, Hector; Joglekar, Manas; Marcus, Adam; Parameswaran, Aditya; Verroios, Vasilis
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