FSELM: fusion semi-supervised extreme learning machine for indoor localization with Wi-Fi and Bluetooth fingerprints

doi:10.1007/s00500-018-3171-4

	FSELM: fusion semi-supervised extreme learning machine for indoor localization with Wi-Fi and Bluetooth fingerprints
	Jiang, Xinlong 1,2,3; Chen, Yiqiang 1,2,3; Liu, Junfa 1,2,3; Gu, Yang 1,2,3; Hu, Lisha 4
	2018-06-01
发表期刊	SOFT COMPUTING
ISSN	1432-7643
卷号	22 期号:11 页码:3621-3635
摘要	Recently, the problem of indoor localization based on WLAN signals is attracting increasing attention due to the development of mobile devices and the widespread construction of networks. However, no definitive solution for achieving a low-cost and accurate positioning system has been found. In most traditional approaches, solving the indoor localization problem requires the availability of a large number of labeled training samples, the collection of which requires considerable manual effort. Previous research has not provided a means of simultaneously reducing human calibration effort and improving location accuracy. This paper introduces fusion semi-supervised extreme learning machine (FSELM), a novel semi-supervised learning algorithm based on the fusion of information from Wi-Fi and Bluetooth Low Energy (BLE) signals. Unlike previous semi-supervised methods, which consider multiple signals individually, FSELM fuses multiple signals into a unified model. When applied to sparsely calibrated localization problems, our proposed method is advantageous in three respects. First, it can dramatically reduce the human calibration effort required when using a semi-supervised learning framework. Second, it utilizes fused Wi-Fi and BLE fingerprints to markedly improve the location accuracy. Third, it inherits the beneficial properties of ELMs with regard to training and testing speeds because the input weights and biases of hidden nodes can be generated randomly. As demonstrated by experimental results obtained on practical indoor localization datasets, FSELM possesses a better semi-supervised manifold learning ability and achieves higher location accuracy than several previous batch supervised learning approaches (ELM, BP and SVM) and semi-supervised learning approaches (SELM, S-RVFL and FS-RVFL). Moreover, FSELM needs less training and testing time, making it easier to apply in practice. We conclude through experiments that FSELM yields good results when applied to a multi-signal-based semi-supervised learning problem. The contributions of this paper can be summarized as follows: First, the findings indicate that effective multi-data fusion can be achieved not only through data-layer fusion, feature-layer fusion and decision-layer fusion but also through the fusion of constraints within a model. Second, for semi-supervised learning problems, it is necessary to combine the advantages of different types of data by optimizing the model's parameters.
关键词	Fusion semi-supervised learning Extreme Learning Machine Indoor localization Wi-Fi and Bluetooth fingerprints
DOI	10.1007/s00500-018-3171-4
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61572471] ; National Natural Science Foundation of China[61472399] ; National Natural Science Foundation of China[61572004] ; Science and Technology Planning Project of Guangdong Province, China[2015B010105001]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS记录号	WOS:000431669200013
出版者	SPRINGER
引用统计	被引频次：22[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/5289
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Chen, Yiqiang
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 2.Beijing Key Lab Mobile Comp & Pervas Device, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Hebei Univ Econ & Business, Inst Informat Technol, Shijiazhuang, Hebei, Peoples R China
推荐引用方式 GB/T 7714	Jiang, Xinlong,Chen, Yiqiang,Liu, Junfa,et al. FSELM: fusion semi-supervised extreme learning machine for indoor localization with Wi-Fi and Bluetooth fingerprints[J]. SOFT COMPUTING,2018,22(11):3621-3635.
APA	Jiang, Xinlong,Chen, Yiqiang,Liu, Junfa,Gu, Yang,&Hu, Lisha.(2018).FSELM: fusion semi-supervised extreme learning machine for indoor localization with Wi-Fi and Bluetooth fingerprints.SOFT COMPUTING,22(11),3621-3635.
MLA	Jiang, Xinlong,et al."FSELM: fusion semi-supervised extreme learning machine for indoor localization with Wi-Fi and Bluetooth fingerprints".SOFT COMPUTING 22.11(2018):3621-3635.