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Multi-modal Sensor Data Fusion in Internet of Things

The diversification and customization of intelligent sensors, such as wearable devices, smart phones and driving assistants, have accelerated the procedure of social informatization. Whenever and wherever, all sorts of data are collected, transmitted, aggregated and processed for the purpose of decision or even prediction for IoT (Internet of Things) application. Nevertheless, since the precision of analyzing result is always determined by accessible information volume, multifarious data should be assembled from multiple sources.  
Considering the diversity of sensing devices and data granularity, the heterogeneity of information is inevitable with respect to data type, sampling interval, context scenario or privacy level. In order to address the aforementioned problem of data isomerism, appropriate data fusion algorithms are of great significance for intended analyzing. Moreover, oriented to different applications, the requirements of specific systems including mobility, reliability, security and real-time should also be fulfilled which bring about tremendous challenges to it. 
This special issue focuses on sharing recent advances in methods and applications which involve combining multimodal sensory data in IoT. Suggested topics include, but are not limited to, the following. 

Edited by: Shaohua Wan, Zan Gao, Yuan Yuan and Zonghua Gu 

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