Semi-supervised deep extreme learning machine for Wi-Fi based localization
Introduction
Wireless localization based on Wi-Fi is quite popular, especially in indoor environment [1], [2], [3], for it does not need deploying any extra infrastructure. What is more, the widespread Access Points (APs) and smart mobile devices facilitate the development of Wi-Fi based indoor localization. To implement indoor localization, traditionally, labeled data are required. In Wi-Fi localization field, labeled data means both data and their corresponding locations (coordinates/classes) are known; unlabeled data means only data are available, and whose corresponding locations are unknown. Generally, there are mainly two types of Wi-Fi localization methods: propagation based method and fingerprint based method. The propagation based method takes the advantage of the nonlinear fading characteristics of wireless signal to set up a propagation model [1]. This kind of method can be easily implemented; however, the localization performance is not good enough for it is difficult to set up an accurate propagation model in a complex and dynamic indoor environment. Compared with propagation based method, fingerprint based localization method is widely adopted. “Fingerprints” are features obtained by feature extraction methods, and used to represent the corresponding locations; “localization” means adopting pattern recognition method to estimate the location. Theoretically, the more the labeled data are, the better the localization performance will be. However, the calibration procedure (which is also known as the acquisition of labeled data) is always at the cost of time, man-hour and money, which leads to the limited number of labeled data. According to [4], “calibration procedures are applied in a great number of proposed location techniques and are considered to be not practical or a considerable barrier to wider adoption of such methods”. As reported [5], Ekahau [6], a commercial real-time localization system, spent $10,000 just to collect labeled data in a large office building, which clearly verifies the high cost of obtaining labeled data.
In contrast to the acquisition of labeled data, the collection of unlabeled data can be carried out easily; especially when crowd sourcing is adopted. Crowd Sourcing [7], [8] is a distributed model to solve problems through an open way with different participants. For indoor localization, crowd sourcing (data collection) means gathering data from heterogeneous devices [9], and most of the data are unlabeled. In order to use unlabeled data, works based on manifold learning and semi-supervised learning were proposed. These works indeed improved localization performance with unlabeled data; however, they paid less attention to the problem caused by crowd sourcing.
After data have been collected for localization system, feature extraction becomes an important step. Though Received Signal Strength (RSS) of Wi-Fi signal has real physical meaning and can be directly used as feature; the highly dynamic indoor environment and heterogeneous devices lead to severe fluctuation of wireless signal, which makes this kind of direct feature less representative. Traditional indoor localization methods mainly selected features manually or extracted features by shallow networks, which cannot reflect Wi-Fi characteristics in complex environment well. However, a machine learning method, Deep Learning (DL) has been famous worldwide since 2006 [10]. DL learns high level features and distributed data structure, which can represent original data better than shallow feature. Though DL outperforms traditional neural networks, its training time consumption is high. While, Extreme Learning Machine (ELM) proposed in the same period as DL, is popular for fast learning speed, which can make up the time-consuming shortage of DL.
Considering the analysis above, we put forward a novel localization method, Semi-supervised Deep Extreme Learning Machine (SDELM) to improve the localization performance with unlabeled data. The contribution of this work is:
- 1)
Utilize unlabeled data to get discriminative features and better classification ability;
- 2)
Propose semi-supervised embedding for deep leaning network;
- 3)
Adopt modified ELM to improve the learning speed of SDELM.
The rest of this paper is organized as follows: Section II shows the related works in Wi-Fi localization field. Section III introduces the proposed SDELM in detail. Section IV evaluates the performance of SDELM in real wireless indoor environments. And Section V concludes the work.
Section snippets
Related works
The mainstream approach of Wi-Fi based indoor localization, fingerprint based method [2], [3], consists of two phases: off-line training phase and online locating phase. At training phase, features are extracted according to a certain rule, so that the relationship between features and their corresponding locations can be established, which forms a “radio map”; at online locating phase, features of unknown location are extracted with the same rule, and pattern recognition methods are used to
Methodology--Semi-supervised deep extreme learning machine
Graph based semi-supervised learning is embedded to deep learning structure to get discriminative feature and better classification boundary. To guarantee the learning speed, semi-supervised learning is evolved from ELM. Hence, in this section, we firstly briefly introduce graph based semi-supervised learning, deep leaning, and ELM; then elaborate the proposed SDELM in detail.
Performance Evaluation
In this section, performance of proposed SDELM will be validated from four aspects: feature representation, localization accuracy, time consumption and man-hour cost in real indoor environments.
Conclusion
Wireless localization system based on Wi-Fi faces a practical problem that the number of labeled data is limited. In order to fully utilize a large number of unlabeled data, SDELM method is proposed, which uses unlabeled data to improve localization performance from feature extraction to pattern recognition (classifier). Semi-supervised learning embedded deep learning can not only extract features with high discriminability but also improve the classification ability with the help of unlabeled
Acknowledgement
This work is supported by Natural Science Foundation of China (No.61173066, No.61070110), and Beijing Natural Science Foundation (No.4144085).
Yang Gu received the B.A.Sc. from Beijing University of Posts and Telecommunications, China in 2010. Currently, she is a Ph.D student in the Institute of Computing Technology, Chinese Academy of Sciences (ICT, CAS). Her current research interests include machine learning, indoor localization and pervasive computing.
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Yang Gu received the B.A.Sc. from Beijing University of Posts and Telecommunications, China in 2010. Currently, she is a Ph.D student in the Institute of Computing Technology, Chinese Academy of Sciences (ICT, CAS). Her current research interests include machine learning, indoor localization and pervasive computing.
Yiqiang Chen received Ph.D. degree from the Institute of Computing Technology, Chinese Academy of Sciences (ICT, CAS), Beijing, China, in 2002. In 2004, he was a Visiting Scholar Researcher at the Department of Computer Science, Hong Kong University of Science and Technology (HKUST), Hong Kong. Currently, he is a Professor and Director of the pervasive computing research center at ICT, CAS. His research interests include artificial intelligence, pervasive computing, and human computer interface. He is a member of IEEE.
Junfa Liu received the Ph.D. degree in the Institute of Computing Technology, Chinese Academy of Sciences (ICT, CAS) in 2009. Currently, he is a vice Professor in ICT, CAS. His current research interests include machine learning, computational intelligence, human computer interaction and pervasive computing. He is a member of IEEE.
Xinlong Jiang, received the B.A.Sc. from Beijing University of Posts and Telecommunications, China in 2011. Currently, he is a Ph.D student in the Institute of Computing Technology, Chinese Academy of Sciences (ICT, CAS). His current research interests include indoor localization and pervasive computing.