Abstract
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.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
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 487–491
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 702–710
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 775–784
Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7:2399–2434
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 95–104
Chai X, Yang Q (2007) Reducing the calibration effort for probabilistic indoor location estimation. IEEE Trans Mob Comput 6(6):649–662
Chen J, Wang C, Sun Y, Shen XS (2011) Semi-supervised laplacian regularized least squares algorithm for localization in wireless sensor networks. Comput Netw 55(10):2481–2491
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 118–125
Chen Y, Yang Q, Yin J, Chai X (2006) Power-efficient access-point selection for indoor location estimation. IEEE Trans Knowl Data Eng 18(7):877–888
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 71–80
Chung FRK (1997) Spectral graph theory, vol 92. American Mathematical Society, Providence
Cooper M, Biehl J, Filby G, Kratz S (2016) Loco: boosting for indoor location classification combining Wi-Fi and BLE. Pers Ubiquitous Comput 20(1):83–96
Ding S, Zhang N, Zhang J, Xu X, Shi Zhongzhi (2017) Unsupervised extreme learning machine with representational features. Int J Mach Learn Cybern 8(2):587–595
Galván-Tejada CE, Carrasco-Jiménez JC, Brena RF (2013) Bluetooth–WiFi based combined positioning algorithm, implementation and experimental evaluation. Procedia Technol 7:37–45
Galvan-Tejada I, Sandoval EI, Brena R et al (2012) Wifi bluetooth based combined positioning algorithm. Procedia Eng 35:101–108
Gao X, Hoi SCH, Zhang Y, Wan J, Li J (2014) Soml: sparse online metric learning with application to image retrieval. In: AAAI, pp 1206–1212
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 180–190
Gu B, Sheng VS (2017) A robust regularization path algorithm for v-support vector classification. IEEE Trans Neural Netw Learn Syst 28(5):1241–1248
Gu B, Sheng VS, Li S (2015) Bi-parameter space partition for cost-sensitive svm. In: IJCAI, pp 3532–3539
Gu B, Sheng VS, Tay KY, Romano W, Li Shuo (2015) Incremental support vector learning for ordinal regression. IEEE Trans Neural Netw Learn Syst 26(7):1403–1416
Gu B, Sheng VS, Wang Z, Ho D, Osman S, Li S (2015) Incremental learning for \(\nu \)-support vector regression. Neural Netw 67:140–150
Gu Y, Chen Y, Liu J, Jiang X (2015) Semi-supervised deep extreme learning machine for Wi-Fi based localization. Neurocomputing 166:282–293
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 70–84
Ham J, Lee DD, Saul LK (2005) Semisupervised alignment of manifolds. In: AISTATS, pp 120–127
Han D, Hu Y, Ai S, Wang G (2015) Uncertain graph classification based on extreme learning machine. Cogn Comput 7(3):346–358
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 1–8
Huang G-B, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B (Cybern) 42(2):513–529
Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501
Lee S, Ha KN, Lee KC (2006) A pyroelectric infrared sensor-based indoor location-aware system for the smart home. IEEE Trans Consum Electron 52(4):1311–1317
Letchner J, Fox D, LaMarca A (2005) Large-scale localization from wireless signal strength. In: AAAI, pp 15–20
Lin Q, Zhao F, Luo H, Kang Y (2011) A wireless localization algorithm based on spectral decomposition of the graph laplacian. Acta Autom Sin 37(3):316–321
Liu H, Darabi H, Banerjee P, Liu J (2007) Survey of wireless indoor positioning techniques and systems. IEEE Trans Syst Man Cybern Part C (Appl Rev) 37(6):1067–1080
Liu J, Chen Y, Liu M, Zhao Z (2011) Selm: semi-supervised elm with application in sparse calibrated location estimation. Neurocomputing 74(16):2566–2572
Liu M, Liu B, Zhang C, Wang W, Sun W (2017) Semi-supervised low rank kernel learning algorithm via extreme learning machine. Int J Mach Learn Cybern 8(3):1039–1052
Liu Y, Zhang L, Deng P, He Z (2017) Common subspace learning via cross-domain extreme learning machine. Cogn Comput 9(4):555–563
Lowe D (1988) Multi-variable functional interpolation and adaptive networks. Complex Syst 2:321–355
Mao W, Wang J, Xue Z (2017) An elm-based model with sparse-weighting strategy for sequential data imbalance problem. Int J Mach Learn Cybern 8(4):1333–1345
Nguyen X, Jordan MI, Sinopoli B (2005) A kernel-based learning approach to ad hoc sensor network localization. ACM Trans Sens Netw (TOSN) 1(1):134–152
Ouyang RW, Wong AK-S, Lea C-T, Chiang M (2012) Indoor location estimation with reduced calibration exploiting unlabeled data via hybrid generative/discriminative learning. IEEE Trans Mob Comput 11(11):1613–1626
Pan JJ, Pan SJ, Yin J, Ni LM, Yang Qiang (2012) Tracking mobile users in wireless networks via semi-supervised colocalization. IEEE Trans Pattern Anal Mach Intell 34(3):587–600
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 988–993
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 1102
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 2208–2212
Pao Y-H, Park G-H, Sobajic DJ (1994) Learning and generalization characteristics of the random vector functional-link net. Neurocomputing 6(2):163–180
Rao CR, Mitra SK (1972) Generalized inverse of matrices and its applications. Wiley, New York
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 79–84
Scardapane S, Comminiello D, Scarpiniti M, Uncini A (2016) A semi-supervised random vector functional-link network based on the transductive framework. Inf Sci 364:156–166
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 1–4
Serre D (2002) Matrices: theory and applications. In: Graduate texts in mathematics. Springer, New York
Torres-Solis J, Falk TH, Chau T (2010) A review of indoor localization technologies: towards navigational assistance for topographical disorientation. INTECH Open Access Publisher
Vapnik V (2013) The nature of statistical learning theory. Springer science & business media, Berlin
Wen X, Shao L, Xue Y, Fang W (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295:395–406
Wong PK, Gao XH, Wong KI, Vong CM (2016) An analytical study on reasoning of extreme learning machine for classification from its inductive bias. Cogn Comput 8(4):746–756
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):641
Xu L, Ding S, Xu X, Zhang N (2016) Self-adaptive extreme learning machine optimized by rough set theory and affinity propagation clustering. Cogn Comput 8(4):720–728
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 269–280
Zhai J, Zhang S, Wang C (2017) The classification of imbalanced large data sets based on mapreduce and ensemble of elm classifiers. Int J Mach Learn Cybern 8(3):1009–1017
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):1–11
Zhang L, He Z, Liu Y (2017) Deep object recognition across domains based on adaptive extreme learning machine. Neurocomputing 239:194–203
Zhang L, Zhang D (2015) Domain adaptation extreme learning machines for drift compensation in e-nose systems. IEEE Trans Instrum Meas 64(7):1790–1801
Zhang L, Zhang D (2016) Robust visual knowledge transfer via extreme learning machine-based domain adaptation. IEEE Trans Image Process 25(10):4959–4973
Zhang L, Zhang D (2017) Evolutionary cost-sensitive extreme learning machine. IEEE Trans Neural Netw Learn Syst 28(12):3045–3060
Zhang Y, Zhi X (2010) Indoor positioning algorithm based on semi-supervised learning. Comput Eng 36(17):277–279
Zhou M, Tian Z, Xu K, Yu X, Hong X, Wu H (2014) Scanme: location tracking system in large-scale campus Wi-Fi environment using unlabeled mobility map. Expert Syst Appl 41(7):3429–3443
Acknowledgements
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.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that there are no conflicts of interest regarding the publication of this paper.
Ethical approval
This article does not report on any studies with human or animal participants performed by any of the authors.
Additional information
Communicated by X. Wang, M. Pelillo, A. K. Sangaiah.
Rights and permissions
About this article
Cite this article
Jiang, X., Chen, Y., Liu, J. et al. FSELM: fusion semi-supervised extreme learning machine for indoor localization with Wi-Fi and Bluetooth fingerprints. Soft Comput 22, 3621–3635 (2018). https://doi.org/10.1007/s00500-018-3171-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-018-3171-4