Feature Adaptive Online Sequential Extreme Learning Machine for lifelong indoor localization

Abstract Wi-Fi-based indoor localization with high capability and feasibility needs to implement lifelong online learning mechanism. However, the characteristic of Wi-Fi is wide variability, which lies in not only the fluctuation of signal strength value, but also the increase or decrease in the number of access points (APs). The traditional algorithms are effective for signal fluctuation, but cannot handle the dimension-changing problem of features caused by increase and decrease in APs’ number. To solve this problem, we propose a Feature Adaptive Online Sequential Extreme Learning Machine (FA-OSELM) algorithm. It can transfer the original model to a new one with a small number of data with new features, so as to make the new model suitable for the new feature dimension. The experiments show that the FA-OSELM can get higher accuracy with a small amount of new data, and it is an effective method to make lifelong indoor localization practical.

[1]  Dianhui Wang,et al.  Advances in extreme learning machines (ELM2014) , 2011, Neurocomputing.

[2]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[3]  S. J. Lee,et al.  Implementation results and service examples of GPS-Tag for indoor LBS and message service , 2013, 2013 15th International Conference on Advanced Communications Technology (ICACT).

[4]  C. R. Rao,et al.  Generalized Inverse of Matrices and its Applications , 1972 .

[5]  K. S. Banerjee.  Generalized Inverse of Matrices and Its Applications , 1973 .

[6]  Liu Jun.  Incremental Localization in WLAN Environment with Timeliness Management , 2013 .

[7]  Yiqiang Chen,et al.  SELM: Semi-supervised ELM with application in sparse calibrated location estimation , 2011, Neurocomputing.

[8]  C. Siew,et al.  Extreme Learning Machine with Randomly Assigned RBF Kernels , 2005 .

[9]  Wang Xi-zhao,et al.  Architecture selection for networks trained with extreme learning machine using localized generalization error model , 2013 .

[10]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[11]  Mikkel Baun Kjærgaard,et al.  A Taxonomy for Radio Location Fingerprinting , 2007, LoCA.

[12]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[13]  Andrés Bueno-Crespo,et al.  Neural architecture design based on extreme learning machine , 2013, Neural Networks.

[14]  Shuangquan Wang,et al.  ContextSense: unobtrusive discovery of incremental social context using dynamic bluetooth data , 2014, UbiComp Adjunct.

[15]  Paramvir Bahl,et al.  A case for adapting channel width in wireless networks , 2008, SIGCOMM '08.

[16]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[17]  Sungyoung Lee,et al.  In-building Localization using Neural Networks , 2006, 2006 IEEE International Conference on Engineering of Intelligent Systems.

[18]  Yiqiang Chen,et al.  RECOGNIZING TRANSPORTATION MODE ON MOBILE PHONE USING PROBABILITY FUSION OF EXTREME LEARNING MACHINES , 2013 .

[19]  Narasimhan Sundararajan,et al.  A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks , 2006, IEEE Transactions on Neural Networks.

[20]  Mauro Brunato,et al.  Statistical learning theory for location fingerprinting in wireless LANs , 2005, Comput. Networks.

[21]  Chee Kheong Siew,et al.  Extreme learning machine: RBF network case , 2004, ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004..

[22]  Jaegeol Yim,et al.  Introducing a decision tree-based indoor positioning technique , 2008, Expert Syst. Appl..

[23]  Shuangquan Wang,et al.  Online sequential ELM based transfer learning for transportation mode recognition , 2013, 2013 IEEE Conference on Cybernetics and Intelligent Systems (CIS).

[24]  Lei Chen,et al.  Enhanced random search based incremental extreme learning machine , 2008, Neurocomputing.

[25]  Guang-Bin Huang,et al.  Convex incremental extreme learning machine , 2007, Neurocomputing.

[26]  D. Serre.  Matrices: Theory and Applications , 2002 .

[27]  Xizhao Wang,et al.  Architecture selection for networks trained with extreme learning machine using localized generalization error model , 2013, Neurocomputing.

[28]  Nobuo Kawaguchi,et al.  Bayesian based location estimation system using wireless LAN , 2005, Third IEEE International Conference on Pervasive Computing and Communications Workshops.

[29]  Yan Li,et al.  FUSION OF EXTREME LEARNING MACHINE WITH FUZZY INTEGRAL , 2013 .

[30]  Yongsheng Ding,et al.  Robust state estimation for discrete-time stochastic genetic regulatory networks with probabilistic measurement delays , 2013, Neurocomputing.

[31]  Peidong Liu,et al.  Extreme learning machine for wireless indoor localization , 2012, IPSN.

[32]  Xizhao Wang,et al.  Dynamic ensemble extreme learning machine based on sample entropy , 2012, Soft Comput..

[33]  Amaury Lendasse,et al.  TROP-ELM: A double-regularized ELM using LARS and Tikhonov regularization , 2011, Neurocomputing.

[34]  Yang Gu,et al.  Incremental Localization in WLAN Environment with Timeliness Management: Incremental Localization in WLAN Environment with Timeliness Management , 2014 .

[35]  Henry Tirri,et al.  A Probabilistic Approach to WLAN User Location Estimation , 2002, Int. J. Wirel. Inf. Networks.

[36]  Jing Liu,et al.  Survey of Wireless Indoor Positioning Techniques and Systems , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[37]  Shuangquan Wang,et al.  A Class Incremental Extreme Learning Machine for Activity Recognition , 2014, Cognitive Computation.

[38]  Peidong Liu,et al.  Poster abstract: Extreme learning machine for wireless indoor localization , 2012, 2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN).

[39]  Shuangquan Wang,et al.  Extreme learning machine-based device displacement free activity recognition model , 2012, Soft Comput..

[40]  Yiqiang Chen,et al.  Power-efficient access-point selection for indoor location estimation , 2006, IEEE Transactions on Knowledge and Data Engineering.

[41]  Feng-Li Lian,et al.  WLAN location determination in e-home via support vector classification , 2004, IEEE International Conference on Networking, Sensing and Control, 2004.