Smartphone-based Wi-Fi Pedestrian-Tracking System Tolerating the RSS Variance Problem Yungeun Kim, Hyojeong Shin, and Hojung Cha Yonsei University Bing Han

Outline Introduction Analysis of RSS variance problem Indoor Pedestrian-Tracking System with Peak-based Wifi Fingerprinting Evaluation Conclusion 2 / 23

Introduction Wi-Fi fingerprinting (WF) technique Localization techniques in indoor environments where GPS signals can’t penetrate WF-based localization requires two phases of operations: In training phase, Vectors of RSS samples from AP are collected and stored with location coordinates in the fingerprint server In localization phase, an RSS vector denoting user’s current positions is relayed to the server in real-time Server then finds the most similar RSS vector from database and estimate the current position 3 / 23

Wi-Fi fingerprinting (WF) technique Cons Using Wi-Fi without GPS signals Improving the positional accuracy Pros Individual privacy security RSS variance problem Accuracy is degraded when the RSS vectors observed in localization phase are different the ones collected from training phase 4 / 23

Analysis of the RSS variance problem RSS variance problem in smartphone-based Wi-Fi Fingerprinting Systems Performance Degradation due to the RSS variance problem Effect on Indoor Pedestrian-tracking Services 5 / 23

RSS variance problem in WF systems 4 categories: device type, device placement, user direction, environmental Detailed setup for RSS sampling data Test environment 740 RSS vectors 37 different locations With 4m spacing based on time-space Characteristics of different smartphones 6 / 23

RSS difference analysis The largest difference is 18dB in mixed case RSS differences with the refrence data Not only consider device- diversity problem, but also the differences in 4 categroies 7 / 23

Performance Degradation Two representative types of location-based services The Point Of Interest(POI) detection services To decide if a user is at a particular POI in different rooms The indoor pedestrian-tracking service To provide high positional accuracy while a user is moving For the localization experiment Location estimation with K-Nearest Neighbors(KNN) Tanimoto coefficient is used to compare Wi-Fi fingerprints 5 samples for POI detection and 2 samples for indoor pedestrian tracking 8 / 23

Effect on POI Detection Services Performance of POI detection The worst case is 0.81 when changes were made over 4 categories Performance improvied with number of APs increasing 9 / 23

Effect on Indoor Pedestrian-tracking Performance of indoor pedestrian tracking Mixed data is the worst result Distance error reduced with number of APs increasing 10 / 23

Conclusion for Performance Degradation POI detection is more robust to the RSS variance problem Indoor pedestrian-tracking suffers significant performance degradation The average error distance reaches up to 3.9m even though using 16APs 11 / 23

Indoor Pedestrian-Tracking System with Peak- based Wi-Fi Fingerprinting Rationale for using the RSS peak System Overview Peak-based Wi-Fi Fingerprinting Particle Filter with Accelerometer and Digital Compass 12 / 23

Rationale for Using the RSS Peak RSS comparisions of 4 different configurations A linear shift in the RSS values for all cases linear-transformation function: The maximum RSS from AP j is always preserved around the same location: Estimating the current position accurately in training data when detect a peak RSS signal from AP combining the peak-based location estimation with Pedestrian Dead Reckoning(PDR) 13 / 23

System Overview Pedestrian Dead Reckoning(PDR) which provides relative location information Built into smartphones. Wi-Fi Fingerprinting(WF) KNN-based + Peak-based Wi-Fi Fingerprinting (PWF) KNN is available but the accuracy is not reliable PWF provides highly accurate location estimation but is available only when peak is detected System prioritizes PWF 14 / 23

Peak-based Wi-Fi Fingerprinting Training phase Build a radio map in a similar way to the traditional fingerprinting system To divide the radio map into a number of Sections, represents the section where only use KNN-based All of including location index and RSS value will recorded Localization phase Next slide 15 / 23

Peak-based Wi-Fi Fingerprinting (cond’) Localization phase First, estimate the current section s with KNN algorithm using Tanimoto coefficient. Peak-based method is suspended when section s is classified as Second, check the peak among n recently RSS vectors. The set of RSS for each i-th AP is defined as, the peak detection function is Third, the location is estimated from the AP recorded in the radio map. The set of fingerprints of section is denoted as = the current location is estimated as depending on moving direction 16 / 23

Particle Filter 17 / 23

Evaluation Experimental Setup 18 / 23

RSS difference and Preservation of Peak Location RSS difference with reference data (data 0) Peak location error according to AP and war-walking 19 / 23

Performance of Peak-based Wi-Fi Fingerprinting Performance of Wi-Fi fingerprinting algorithms 20 / 23

Performance of the Proposed Pedestrian-Tracking System Accuracy of different pedestrian-tracking systems Performance vs. the number of APs 21 / 23

Performance in the Shopping Mall The test environment in a shopping mall Performance in the shopping mall 22 / 23

Conclusion To investigated the RSS variance problem in smartphone-based WF system To evaluated the effect of RSS variance problem between the training phase and localization phase The proposed tracking system overcomes the RSS variance problem by using the location of signal peak for the localization 23 / 23