文章基本信息

标题：MuMoCo : a Framework for Improving Data Quality with Multi-modality Cooperation in Wireless Sensor Networks
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作者：Guo-Ying Wang ; Shen-Ming Qu
期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2012
卷号：9
期号：6
出版社：IJCSI Press
摘要：High quality sensor data stream is crucial to wireless sensor networks applications. However raw data streams in wireless sensor networks tend to be not reliable. Therefore, improving sensor data quality is an important issue for all kinds of wireless sensor networks applications. In this paper, we proposed an integrated framework, MuMoCo, which is based on such a fact: the factors leading to outlier or data missing such as events, insufficient power, or malicious nodes have similar influence on each modality of data created by a node at the same time. Not considering the correlation among different modalities of data may probably lead to a contradictory: different conclusions of data verification according to different modalities of data. Taking advantage of multiple modalities cooperation of sensor data to avoid such contradictory and improve data quality, the MuMoCo framework includes data source quality assessment, data authenticity verification and recovery, and data conversion with quality assurance. Using the MuMoCo framework, we can obtain following benefits: more efficient filtering of data source based on data source quality assessment; more accurate data authenti-city verification and data recover; and more comprehensive utilization of sensor data.
关键词：wireless sensor networks; data quality improvement; multiple modalities cooperation; data clean; outlier detection; data recovery