FSELM: fusion semi-supervised extreme learning machine for indoor localization with Wi-Fi and Bluetooth fingerprints
- 171 Downloads
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.
KeywordsFusion semi-supervised learning Extreme Learning Machine Indoor localization Wi-Fi and Bluetooth fingerprints
This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 61572471, 61472399 and 61572004 and by the Science and Technology Planning Project of Guangdong Province, China, under Grant No. 2015B010105001.
Compliance with ethical standards
Conflict of interest
The authors declare that there are no conflicts of interest regarding the publication of this paper.
This article does not report on any studies with human or animal participants performed by any of the authors.
- Aparicio S, Pérez J, Bernardos AM, Casar JR (2008) A fusion method based on bluetooth and wlan technologies for indoor location. In: Multisensor fusion and integration for intelligent systems, 2008. MFI 2008. IEEE international conference on. IEEE, pp 487–491Google Scholar
- Aparicio S, Pérez J, Tarrío P, Bernardos A, Casar J (2009) An indoor location method based on a fusion map using Bluetooth and WLAN technologies. In: International symposium on distributed computing and artificial intelligence 2008 (DCAI 2008). Springer, pp 702–710Google Scholar
- Bahl P, Padmanabhan VN (2000) Radar: an in-building RF-based user location and tracking system. In: INFOCOM 2000. Nineteenth annual joint conference of the IEEE computer and communications societies. Proceedings. IEEE, vol 2, pp 775–784Google Scholar
- Chai X, Yang Q (2005) Reducing the calibration effort for location estimation using unlabeled samples. In: Pervasive computing and communications, 2005. PerCom 2005. Third IEEE international conference on. IEEE, pp 95–104Google Scholar
- Chen Y-C, Chiang J-R, Chu H, Huang P, Tsui AW (2005) Sensor-assisted Wi-Fi indoor location system for adapting to environmental dynamics. In: Proceedings of the 8th ACM international symposium on modeling, analysis and simulation of wireless and mobile systems. ACM, pp 118–125Google Scholar
- Chen Z, Chen Y, Gao X, Wang S, Hu L, Yan CC, Lane ND, Miao C (2015) Unobtrusive sensing incremental social contexts using fuzzy class incremental learning. In: Data mining (ICDM), 2015 IEEE international conference on. IEEE, pp 71–80Google Scholar
- Gao X, Hoi SCH, Zhang Y, Wan J, Li J (2014) Soml: sparse online metric learning with application to image retrieval. In: AAAI, pp 1206–1212Google Scholar
- González E, Prados L, Rubio A, Segura J, de la Torre Á, Moya J, Rodríguez P, Martín J (2009) Atlintida: a robust indoor ultrasound location system: design and evaluation. In: 3rd symposium of ubiquitous computing and ambient intelligence 2008. Springer, pp 180–190Google Scholar
- Gu B, Sheng VS, Li S (2015) Bi-parameter space partition for cost-sensitive svm. In: IJCAI, pp 3532–3539Google Scholar
- Haeberlen A, Flannery E, Ladd AM, Rudys A, Wallach DS, Kavraki LE (2004) Practical robust localization over large-scale 802.11 wireless networks. In: Proceedings of the 10th annual international conference on mobile computing and networking. ACM, pp 70–84Google Scholar
- Ham J, Lee DD, Saul LK (2005) Semisupervised alignment of manifolds. In: AISTATS, pp 120–127Google Scholar
- Hossain AKMM, Van HN, Jin Y, Soh W-S (2007) Indoor localization using multiple wireless technologies. In: Mobile adhoc and sensor systems, 2007. MASS 2007. IEEE international conference on. IEEE, pp 1–8Google Scholar
- Letchner J, Fox D, LaMarca A (2005) Large-scale localization from wireless signal strength. In: AAAI, pp 15–20Google Scholar
- Pan JJ, Yang Q, Chang H, Yeung D-Y (2006) A manifold regularization approach to calibration reduction for sensor-network based tracking. In: AAAI, pp 988–993Google Scholar
- Pan JJ, Yang Q, Pan SJ (2007) Online co-localization in indoor wireless networks by dimension reduction. In: Proceedings of the national conference on artificial intelligence. Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999, vol 22, p 1102Google Scholar
- Pandya D, Jain R, Lupu E (2003) Indoor location estimation using multiple wireless technologies. In: Personal, indoor and mobile radio communications, 2003. PIMRC 2003. 14th IEEE proceedings on. IEEE, vol 3, pp 2208–2212Google Scholar
- Rodrigues ML, Vieira LFM, Campos MFM (2012) Mobile robot localization in indoor environments using multiple wireless technologies. In: Robotics symposium and Latin American robotics symposium (SBR-LARS), 2012 Brazilian. IEEE, pp 79–84Google Scholar
- Schmidt WF, Kraaijveld MA, Duin RPW (1992) Feedforward neural networks with random weights. In: Pattern recognition, 1992. Conference B: pattern recognition methodology and systems, proceedings, 11th IAPR international conference on. IEEE, vol II, pp 1–4Google Scholar
- Serre D (2002) Matrices: theory and applications. In: Graduate texts in mathematics. Springer, New YorkGoogle Scholar
- Torres-Solis J, Falk TH, Chau T (2010) A review of indoor localization technologies: towards navigational assistance for topographical disorientation. INTECH Open Access PublisherGoogle Scholar
- Xiang L, Wang D, Wei Y, Zhou Y (2015) Location-fingerprint based indoor localization via scalable semi-supervised learning. Int Inf Inst (Tokyo) Inf 18(2):641Google Scholar
- Yang Z, Wu C, Liu Y (2012) Locating in fingerprint space: wireless indoor localization with little human intervention. In: Proceedings of the 18th annual international conference on mobile computing and networking. ACM, pp 269–280Google Scholar
- Zhang L, Deng P (2017) Abnormal odor detection in electronic nose via self-expression inspired extreme learning machine. IEEE Trans Syst Man Cybern Syst PP(99):1–11Google Scholar
- Zhang Y, Zhi X (2010) Indoor positioning algorithm based on semi-supervised learning. Comput Eng 36(17):277–279Google Scholar