Compressive sensing based data quality improvement for crowd-sensing applications

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• 作者：Long Cheng^a ; ^{chenglong@buaa.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Jianwei Niu^a ; ^{niujianwei@buaa.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Linghe Kong^b ; ^{linghe.kong@sjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Chengwen Luo^c ; ^{chengwen@szu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Yu Gu^d ; ^{yugu@us.ibm.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Wenbo He^e ; ^{wenbohe@cs.mcgill.ca" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Sajal K. Das^f ; ^{sdas@mst.edu" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：False data detection and correction ; Crowd-sensing ; Spatio-temporal compressive sensing
• 刊名：Journal of Network and Computer Applications
• 出版年：2017
• 出版时间：1 January 2017
• 年：2017
• 卷：77
• 期：Complete
• 页码：123-134
• 全文大小：1356 K

文摘

Crowd-sensing enables to collect a vast amount of data from the crowd by allowing a wide variety of sources to contribute data. However, the openness of crowd-sensing exposes the system to malicious and erroneous participations, inevitably resulting in poor data quality. This brings forth an important issue of false data detection and correction in crowd-sensing. Furthermore, data collected by participants normally include considerable missing values, which poses challenges for accurate false data detection. In this work, we propose class="smallcaps">Deco, a general framework to detect false values for crowd-sensing in the presence of missing data. By applying a tailored spatio-temporal compressive sensing technique, class="smallcaps">Deco is able to accurately detect the false data and estimate both false and missing values for data correction. Through comprehensive performance evaluations, we demonstrate the efficacy of class="smallcaps">Deco in achieving false data detection and correction for crowd-sensing applications with incomplete sensory data.