Transfer Learning for WiFi-based Indoor Localization Sinno Jialin Pan Department of Computer Science and Engineering The Hong Kong University of Science and Technology www.cse.ust.hk/~sinnopan Joint Work with: Vincent Zheng, Prof. Qiang Yang and Derek Hu

Indoor WiFi-based Localization Received-Signal-Strength (RSS) based localization in an indoor WiFi environment. Access point 2 Mobile device Access point 1 Access point 3 -30dBm -70dBm -40dBm (location_x, location_y) Where is the mobile device?

Machine-learning-based Methods Two phases: offline training & online localization Offline phase: Input: Collected samples at variant locations Output: A mapping function F from signal space S to location space L Online phase: Given a new signal s, estimate the most likely location l by F. s=[-60,-50,-40]dBm, compute F(s) as the estimated location. Location (AP1, AP2, AP3) (1,0) (-60, -50, -40) dBm (2,0) (-62, -48, -35) dBm ….. ( … , … , … ) dBm (9,5) (-50, -35, -42) dBm Training… Mapping function F

Assumption behind Previous Learning-based Localization Models In an offline phase, a lot of labeled data are needed to be given to train a localization model. In an online phase, the learnt localization model can be used directly to estimate locations of mobile devices.

Assumption behind Previous Learning-based Localization Models In an offline phase, a lot of labeled data are needed to be given to train a localization model. Traditional supervised learning based localization systems Semi-supervised learning based localization systems Bahl et al. 2000 Letchner et al. 2005 Pan et al. 2006

Assumption behind Previous Learning-based Localization Models In an online phase, the learnt localization model can be used directly to estimate locations of mobile devices. Time Time Period 1 Time Period 2 Device Device A Device B

Transfer-Learning-based Localization Models Transferring localization models over time. Transferring localization models across space. Transferring localization models across devices.

Transferring Localization Models Over Time I (Pan et al. 2007) Motivation: The physical location space does not change over time, different signal spaces have a common underlying low dimensional (2D/3D) manifold structure. Location Space Signal Space in Online Time Period Signal Space in Offline Time Period Reference points are placed at A, B, C, which bridge a connection between old signal space and new signal space. Assumption: There exists a common low-dimensional manifold structure over time.

Transferring Localization Models Over Time II (Zheng et al. 2008) Motivation: Using trace information to adapt a HMM-based localization model for use in a new time period. Reference points Using EM algorithm to adapt the parameters of a HMM learnt previously to new time period. Mapping function Transition matrix of user moves Prior knowledge on the likelihood of where the user is Assumption: long-term behavior of users does not change a lot over time.

Transferring Localization Models Across Space (Pan et al. 2008) B A Access Point Extracted domain knowledge Encode learnt knowledge to construct a localization model for the whole area Assumption: There exist some common constraints across space.

Transferring Localization Models Across Devices (Zheng et al. 2008) Motivation: Extend regularization-based multi-task learning method onto latent space to transfer localization models across devices. where Assumption: Models of related tasks should share some common parameters.

Experimental Results Test Bed: Results: ICDM 2007 Data Mining Contest Dataset, task 2: Indoor WiFi-based localization over time. The WiFi data are collected in an academic building in the Hong Kong University of Science and Technology over time periods, which consists of an area of 145.5m × 37.5m. We further collect WiFi data in the same indoor building at HKUST to evaluate our proposed solution for transferring localization models across space and devices. Results:

Conclusion & Future Work We propose four specific transfer-learning-based solutions to transfer localization models over time, across space and across devices. In the future, we plan to develop more general transfer learning methods to transfer localization models over time, across space and across devices at the same time. In the future, we also plan to exploit transfer learning into other pervasive computing applications, such as activity recognition from low-level sensory data.

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