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.
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