Enhancing Participant Selection through Caching in Mobile Crowd Sensing

被引：0
|
作者：
Li, Hanshang [1 ]
Li, Ting [1 ]
Li, Fan [2 ,3 ]
Wang, Weichao [4 ]
Wang, Yu [1 ]
机构：
[1] Univ N Carolina, Dept Comp Sci, Charlotte, NC 28223 USA
[2] Beijing Engn Res Ctr, High Volume Language informat Proc & Cloud Comp A, Beijing 100081, Peoples R China
[3] Beijing Inst Technol, Sch Comp Sci, Beijing 100081, Peoples R China
[4] Univ N Carolina, Dept Software & Informat Systerns, Charlotte, NC 28223 USA
来源：
2016 IEEE/ACM 24TH INTERNATIONAL SYMPOSIUM ON QUALITY OF SERVICE (IWQOS) | 2016年
关键词：
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
With the rapid increasing of smart phones and their embedded sensing technologies, mobile crowd sensing (MCS) becomes an emerging sensing paradigm for performing large-scale sensing tasks. One of the key challenges of large-scale mobile crowd sensing systems is how to effectively select the minimum set of participants from the huge user pool to perform the tasks and achieve certain level of coverage. In this paper, we introduce a new MCS architecture which leverages the cached sensing data to fulfill partial sensing tasks in order to reduce the size of selected participant set. We present a newly designed participant selection algorithm with caching and evaluate it via extensive simulations with a real-world mobile dataset.
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页数：10
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