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Cyber-physical-social collaborative sensing: from single space to cross-space

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Abstract

The development of wireless sensor networking, social networking, and wearable sensing techniques has advanced the boundaries of research on understanding social dynamics. Collaborative sensing, which utilizes diversity sensing and computing abilities across different entities, has become a popular sensing and computing paradigm. In this paper, we first review the history of research in collaborative sensing, which mainly refers to single space collaborative sensing that consists of physical, cyber, and social collaborative sensing. Afterward, we extend this concept into cross-space collaborative sensing and propose a general reference framework to demonstrate the distinct mechanism of cross-space collaborative sensing. We also review early works in cross-space collaborative sensing, and study the detail mechanism based on one typical research work. Finally, although cross-space collaborative sensing is a promising research area, it is still in its infancy. Thus, we identify some key research challenges with potential technical details at the end of this paper.

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Acknowledgments

This work was supported in part by the National Basic Research Program of China (2015CB352400), and the National Natural Science Foundation of China (Grant Nos. 61373119, 61332005, 61402369).

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Authors and Affiliations

  1. School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072, China

    Fei Yi, Zhiwen Yu, Huihui Chen, He Du & Bin Guo

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  1. Fei Yi
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Corresponding author

Correspondence to Zhiwen Yu.

Additional information

Fei Yi is currently a PhD candidate in computer science at Northwestern Polytechnical University (NPU), China. He received his BE degree in computer science and technology from NPU in 2010. His research interests include ubiquitous computing, social network analysis, and data mining.

Zhiwen Yu is currently a professor with the School of Computer Science, Northwestern Polytechnical University (NPU), China. He received his PhD degree of Engineering in computer science and technology from NPU in 2005. He has been a visiting researcher at the Context-Aware Systems Department, Institute for Infocomm Research (I2R), Singapore from 2004 to 2005, and has been an Alexander von Humboldt Fellow at the Mannheim University, Germany from 2009 to 2010. He is the associate editor or editorial board of IEEE Transactions on Human-Machine Systems, IEEE Communications Magazine, ACM/Springer Personal and Ubiquitous Computing (PUC). He is an awardee of the NSFC Excellent Young Scholars Program in 2015.

Huihui Chen received her BE degree in computer science from Northeast Dianli University, China in 2000, and her ME degree in computer science from Zhengzhou University, China in 2006. She received her PhD degree at Northwestern Polytechnical University, China in 2017. Her research interests include ubiquitous computing and mobile crowd sensing.

He Du received her Bachelor’s degree from Northwestern Polytechnical University (NPU), China. She is currently a PhD student with the School of Computer Science, NPU. Her research interests include ubiquitous computing and mobile sensing.

Bin Guo is currently a professor with Northwestern Polytechnical University, China. He received his PhD degree in computer science from Keio University, Japan in 2009. His current research interests include ubiquitous computing, social and community intelligence, mobile crowd sensing, and human-computer interaction. He has served as an associate editor of IEEE Communications Magazine (ComMag), IEEE Trans. on Human-Machine-Systems (THMS), IEEE IT Professional (ITPro), ACM/Springer Journal of Personal and Ubiquitous Computing (PUC), Springer Frontiers of Computer Science (FCS), the guest editor of ACM Trans. on Intelligent Systems and Technology (TIST), IEEE Internet of Things (IoT), Elsevier Journal of Network and Computer Applications (JNCA), and so on.

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Yi, F., Yu, Z., Chen, H. et al. Cyber-physical-social collaborative sensing: from single space to cross-space. Front. Comput. Sci. 12, 609–622 (2018). https://doi.org/10.1007/s11704-017-6612-9

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