A hybrid outdoor localization scheme with high-position accuracy and low-power consumption

被引:0
|
作者
Hongwei Du
Chen Zhang
Qiang Ye
Wen Xu
Patricia Lilian Kibenge
Kang Yao
机构
[1] Harbin Institute of Technology Shenzhen Graduate School,Department of Computer Science and Technology
[2] Shenzhen Key Laboratory of Internet Information Collaboration,School of of Mathematical and Computational Sciences
[3] University of Prince Edward Island,Department of Mathematics and Computer Science
[4] Texas Woman’s University,undefined
来源
EURASIP Journal on Wireless Communications and Networking | / 2018卷
关键词
Outdoor localization; Positioning accuracy; Power consumption;
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学科分类号
摘要
As mobile devices, such as smart phones, become more and more popular, localization based on mobile devices has gained much attention in both the academia and industry. Although global positioning system (GPS) has been widely deployed in many fields, it has several disadvantages such as consuming too much power and requiring the line of sight to satellites. Cellular network-based localization is much more robust. However, this type of localization scheme cannot achieve enough accuracy for some localization-based services. With the existence of densely deployed WiFi access points, WiFi-based localization seems very promising in terms of positioning precision and power consumption. However, different than GPS and cellular network based localization, most of the existing WiFi-based localization schemes are supposed to be used in indoor environments. Note that indoor WiFi-based localization schemes cannot be simply ported to urban outdoor environments due to several factors such as weaker signal and more serious interference. In this paper, we present a hybrid outdoor localization scheme utilizing crowdsourced WiFi signal data and built-in sensors in smart phones, which can achieve high-positioning accuracy and low power consumption. Our experimental results show that the proposed hybrid localization scheme outperforms the widely-adopted GPS-based method in terms of both positioning accuracy and power consumption. In addition, the idea of crowdsourcing fingerprints is effective.
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