Detecting and correcting WiFi positioning errors

Recent advances in GPS and WiFi-based positioning technologies for mobile phones have triggered many location-based services. However, GPS positioning quickly drains a phone's battery and cannot be used indoors. On the other hand, WiFi positioning provides energy-efficient indoor and outdoor positioning with reasonable accuracy. However, WiFi positioning sometimes makes large errors caused by various reasons, e.g., the movement of reference WiFi access points. In this paper we attempt to detect and correct such errors automatically by performing outlier detection in time series. So, we solve this problem by comparing a user's current measurement at time T with her coordinate point at time T predicted from her past coordinate history, and judging whether the current measurement is correct or not by computing the distance between the measurement location and the predicted location. However, it is difficult to predict the user's coordinates accurately with a single prediction method (predictor) because the user's context (e.g., migration speed and sparseness of past coordinates) greatly affects predictor performance. We thus design a context-aware error detection method by employing an ensemble of predictors that have different strengths and weaknesses.

[1]  Bill N. Schilit,et al.  Place Lab: Device Positioning Using Radio Beacons in the Wild , 2005, Pervasive.

[2]  Paul A. Zandbergen,et al.  Accuracy of iPhone Locations: A Comparison of Assisted GPS, WiFi and Cellular Positioning , 2009 .

[3]  Ian Witten,et al.  Data Mining , 2000 .

[4]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[5]  Polly Huang,et al.  Sensor-assisted wi-fi indoor location system for adapting to environmental dynamics , 2005, MSWiM '05.

[6]  Wade Trappe,et al.  Exploiting Environmental Properties for Wireless Localization and Location Aware Applications , 2008, 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom).

[7]  Maximilian Schirmer,et al.  Energy efficient continuous location determination for pedestrian information systems , 2012, MobiDE '12.

[8]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[9]  Mikkel Baun Kjærgaard,et al.  EnTracked: energy-efficient robust position tracking for mobile devices , 2009, MobiSys '09.

[10]  Edward Y. Chang,et al.  Adaptive stream resource management using Kalman Filters , 2004, SIGMOD '04.

[11]  Prasant Mohapatra,et al.  Improving energy efficiency of Wi-Fi sensing on smartphones , 2011, 2011 Proceedings IEEE INFOCOM.

[12]  Victoria J. Hodge,et al.  A Survey of Outlier Detection Methodologies , 2004, Artificial Intelligence Review.

[13]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[14]  S. C. Johnson.  Hierarchical clustering schemes , 1967, Psychometrika.

[15]  Henry Tirri,et al.  Topics in probabilistic location estimation in wireless networks , 2004, 2004 IEEE 15th International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE Cat. No.04TH8754).

[16]  Jatinder Pal Singh,et al.  Improving energy efficiency of location sensing on smartphones , 2010, MobiSys '10.

[17]  P. S. Maybeck,et al.  The Kalman Filter: An Introduction to Concepts , 1990, Autonomous Robot Vehicles.

[18]  Feng Zhao,et al.  Energy-accuracy trade-off for continuous mobile device location , 2010, MobiSys '10.

[19]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[20]  Wei Jiang,et al.  On-line outlier detection and data cleaning , 2004, Comput. Chem. Eng..

[21]  M. Csíkszentmihályi,et al.  The Experience Sampling Method , 2014 .

[22]  S. Sathiya Keerthi,et al.  Improvements to the SMO algorithm for SVM regression , 2000, IEEE Trans. Neural Networks Learn. Syst..

[23]  Rui José,et al.  Delay-Tolerant Positioning for Location-Based Logging with Mobile Devices , 2012, UCAmI.

[24]  Gérard G. Medioni,et al.  Continuous tracking within and across camera streams , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..