Open Access

ARTICLE

Leveraging Logical Anchor into Topology Optimization for Indoor Wireless Fingerprinting

Lin Wang^1,*, Huixiang Liu¹, Wenyuan Liu^1,2, Nan Jing¹, Ahmad Adnan¹, Chenshu Wu³

1 School of Information Science and Engineering, Yanshan University, 066004, China.
2 The Key Laboratory for Software Engineering of Hebei Province, 066004, China.
3 Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, 20742, USA.

* Corresponding Author: Lin Wang. Email: .

Computers, Materials & Continua 2019, 58(2), 437-449. https://doi.org/10.32604/cmc.2019.03814

Abstract

The indoor subarea localization has wide application space in dynamic hot zone identification, indoor layout optimization, store dynamic pricing and crowd flow trend prediction. The ubiquitous mobile devices provide the opportunity for wireless fingerprinting-based indoor localization services. However, there are two short board where the existing methods have been criticized. One is that a tagging approach requires a large number of professional surveys for wireless fingerprint construction, which weakens the scalability of the methods. The other is that the crowdsourcing-based methods encounter the cold boot problem in the system initial stage. To address these issues, the paper proposes a topology optimization approach leveraging the dynamic logical anchor selection into a subarea localization system. First of all, a new annular-based radio map construction strategy with the feedback selection of logic anchor is designed to release the pressure of site survey. The implementation of this strategy harnesses the characteristics of the indoor building structure and inter subarea overlapping recognition, without the topology and distribution of physical anchor (e.g., access points or POIs). Secondly, exploiting the probabilistic support vector machine algorithm, the target is localized in the corresponding subarea in a real-time pattern. Furthermore, the localization error is calibrated with an error recognition algorithm. Finally, massive experiments are implemented on a prototype system. The results show that the proposed method can decrease the overhead of the system initialization and achieve higher localization accuracy compared with the existing approaches.

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Citations