文献类型：期刊文献

英文题名：LCBPA: two-stage task allocation algorithm for high-dimension data collecting in mobile crowd sensing network

作者：Zhou, Ning[1,2];Zhang, Jianhui[1];Wang, Binqiang[1];Xiao, Jia[3]

第一作者：Zhou, Ning;周宁

通讯作者：Zhou, N[1];Zhou, N[2]

机构：[1]Natl Digital Switching Syst Engn & Technol Res Ct, Zhengzhou 450002, Peoples R China;[2]Xinxiang Univ, 191 Jinsui Ave, Xinxiang 453000, Henan, Peoples R China;[3]Beijing Univ Posts & Telecommun, Beijing 100086, Peoples R China

第一机构：Natl Digital Switching Syst Engn & Technol Res Ct, Zhengzhou 450002, Peoples R China

通讯机构：[1]corresponding author), Natl Digital Switching Syst Engn & Technol Res Ct, Zhengzhou 450002, Peoples R China;[2]corresponding author), Xinxiang Univ, 191 Jinsui Ave, Xinxiang 453000, Henan, Peoples R China.|[11071]新乡学院;

年份：2019

卷号：2019

期号：1

外文期刊名：EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING

收录：;EI(收录号：20195307950365);Scopus(收录号：2-s2.0-85077037418);WOS:【SCI-EXPANDED(收录号:WOS:000510409100001)】；

语种：英文

外文关键词：Task allocation; Mobile crowd sensing; High-dimensional data collection

摘要：Mobile crowd sensing (MCS) is a novel emerging paradigm that leverages sensor-equipped smart mobile terminals (e.g., smartphones, tablets, and intelligent wearable devices) to collect information. Compared with traditional data collection methods, such as construct wireless sensor network infrastructures, MCS has advantages of lower data collection costs, easier system maintenance, and better scalability. However, the limited capabilities make a mobile crowd terminal only support limited data types, which may result in a failure of supporting high-dimension data collection tasks. This paper proposed a task allocation algorithm to solve the problem of high-dimensional data collection in mobile crowd sensing network. The low-cost and balance-participating algorithm (LCBPA) aims to reduce the data collection cost and improve the equality of node participation by trading-off between them. The LCBPA performs in two stages: in the first stage, it divides the high-dimensional data into fine-grained and smaller dimensional data, that is, dividing an m-dimension data collection task into k sub-task by K- means, where (k < m). In the second stage, it assigns different nodes with different sensing capability to perform sub-tasks. Simulation results show that the proposed method can improve the task completion ratio, minimizing the cost of data collection.