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Enabling entity discovery in indoor commercial environments without pre-deployed infrastructure

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Abstract

Finding entities of interest in indoor commercial places, such as the merchandise in supermarkets and shopping malls, is an essential issue for customers, especially when they are unfamiliar with an ad hoc indoor environment. This type of location-based indoor service requires comprehensive knowledge of indoor entities, including locations as well as their semantic information. However, the existing indoor localization approaches fail to directly localize these general entities without dedicated devices. This paper first focuses on the problem of discovering large-scale general entities of interest in indoor commercial spaces without pre-deployed infrastructure. We present a unique entity localization approach that leverages the localization results from multiple independent users to accurately determine the location of corresponding entities. Our key idea is to exploit the short-distance estimation with dead reckoning to guarantee the accuracy of entity localization. We develop a prototype system based on the crowdsourcing method, iScan, and test it in one of the biggest supermarkets in Changsha, China, to validate the performance of our design. Extensive experimental results show that our approach can achieve meter-level accuracy in a single day with 70 participants. Moreover, in a monthly evaluation with 500 effective participants, iScan discovered more than 200 entities and localized approximately 75% of them within 2 m.

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Acknowledgements

This work was partly supported by the National Natural Science Foundation of China (Grant No. 61422214), National Basic Research Program of China (2014CB347800), the Program for New Century Excellent Talents in University, the Hunan Provincial Natural Science Fund for Distinguished Young Scholars (2016JJ1002), the Research Funding of NUDT (JQ14-05-02 and ZDYYJCYJ20140601).

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Authors and Affiliations

  1. Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, 410073, China

    Bo Yuan, Xiaolei Zhou, Xiaoqiang Teng & Deke Guo

Authors
  1. Bo Yuan
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  2. Xiaolei Zhou
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  3. Xiaoqiang Teng
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  4. Deke Guo
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Corresponding author

Correspondence to Deke Guo.

Additional information

Bo Yuan received the BS degree from the School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, China in 2015. Currently, he is a master student in the College of Information System and Management, National University of Defense Technology, China. His research interests include mobile computing, pervasive computing, and computer vision.

Xiaolei Zhou received the BA degree from Nanjing University, China in 2009, and received MS and PhD degrees in military operational research from National University of Defense Technology (NUDT), China in 2011 and 2016, receptively. He is an assistant professor with the Nanjing Telecommunication Technology Research Institute, NUDT. His research interests include mobile and pervasive computing, mobile crowdsensing, especially wireless indoor positioning and location based services.

Xiaoqiang Teng received the BS degree from the School of Mechanical Engineering, Shenyang University of Technology, China in 2013. Currently, he is a PhD student in the College of Information System and Management, National University of Defense Technology, China. His research interests include mobile computing, pervasive computing, and computer vision.

Deke Guo received the BS degree in industry engineering from Beijing University of Aeronautic and Astronautic, China in 2001, and the PhD degree in management science and engineering from National University of Defense Technology (NUDT), China in 2008. He is an associate professor with the College of Information System and Management, NUDT. His research interests include distributed systems, software-defined networking, and data center networking. He is a member of the ACM and the IEEE.

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Yuan, B., Zhou, X., Teng, X. et al. Enabling entity discovery in indoor commercial environments without pre-deployed infrastructure. Front. Comput. Sci. 13, 618–636 (2019). https://doi.org/10.1007/s11704-017-6601-z

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