eTrack: Target Localization System in Surveillance Sensor Networks

Graduate Research SymposiumMay 04, 2012

Cuong (Charlie) Pham

Challenges• Accuracy• Time• Cost• Noisy environment• Anchor locations need to be known in advance

Main Contributions:- Low cost- Working with noisy environment

- Anchor locations unknown

Can we do better than this?Of course, we do (APIT, Spotlight, Diffusion, etc.)

Equipment

Arduino Uno SMD - ATmega328 microcontroller- 32k Flash Memory - 16Mhz Clock Speed

Xbee Series 1 (802.15.4)- 250kbps Max data rate- 300ft (100m) range

Base Station- Network sink

The Machine Learning Approach

• Do classification to get locationo Define classeso Get training datao Build modelo Predict location

Training

DataModel

Learning

Data

Class

Classes + Training Data

1

Class 1Class 2

2 3 4 5 6

Location EstimationXi Xi+1

Yj

Yj+1The sensor is• In class Xi+1 but not in Xi• In class Yj+1 but not in Yj

Binary SearchXh 6

Accuracy6

Demo Video• http://www.youtube.com/watch?v=BA6hUwSmWQ8

References[1] XUANLONG NGUYEN, MICHAEL I. JORDAN, and BRUNO SINOPOLI. A Kernel-Based Learning Approach to Ad Hoc Sensor Network Localization

[2] Duc A. Tran and Thinh Nguyen. Localization in Wireless Sensor Networks based on Support Vector Machines. IEEE Transactions on Parallel and Distributed Systems (TDPS), 19(7): 981-994, July 2008.

[3] Wang, J., Ghosh, R., and Das, S. A survey on sensor localization. Journal of Control Theory andApplications 8, 1 (2010), 2-11.

[4] Lingxuan Hu and David Evans. Localization for Mobile Sensor Networks. In Tenth Annual International Conference on Mobile Computing and Networking (MobiCom 2004). Philadelphia, 26 September - 1 October 2004

[5] Tian He, Chengdu Huang, Brian M. Blum, John A. Stankovic, Tarek Abdelzaher. Range-Free Localization Schemes for Large Scale Sensor Networks.

Come visit NISLab (S-3-124B)