1. Distributed and Efficient Classifiers for Wireless Audio-Sensor Networks Baljeet Malhotra Ioanis Nikolaidis Mario A. Nascimento University of Alberta Canada To be presented at: INSS, June 17-19, 2008, Kanazawa, Japan Supported By:

2. Outline Of The Talk Introduction Motivation Motivation Classification of Acoustic Targets –Classification Framework –Classification Methods: KNN & ML Features Extraction –Independent Features Selection –Global Features Selection Simulation Study –Dataset and Setup –Methodology –Results and Discussions Conclusion and Future Directions 2 min 5 min 6 min 2 min 5 min

3. Introduction Vehicle classification is an important problem in WSN Vehicle classification is an important problem in WSN –Tracking –Localization Tracking can be facilitated by: Tracking can be facilitated by: – Video/Image based sensors – RFID tags – Limitations:  Video/Image requires higher processing capabilities  RFID tags may not be feasible Acoustic target tracking Acoustic target tracking – Lesser processing requirements

4. Vehicle Classification Vehicle classification is crucial to tracking Vehicle classification is crucial to tracking Only vehicles of interest are reported Only vehicles of interest are reported Problem becomes much challenging if there are more vehicles of the same class Problem becomes much challenging if there are more vehicles of the same class – Identification problem This paper deals with the problem of vehicle classification only and NOT identification This paper deals with the problem of vehicle classification only and NOT identification Disclaimer: Images used above are collected through Google’s search engine Class AClass BClass A

5. A Framework for Classification Nodes organize themselves into neighborhoods “clusters” as a vehicles crosses through an area monitored by sensors Nodes organize themselves into neighborhoods “clusters” as a vehicles crosses through an area monitored by sensors A master node is selected based on the signal strength. A master node is selected based on the signal strength. A cluster can perform classification independently. A cluster can perform classification independently. Multiple clusters may be formed and collaborate for: Multiple clusters may be formed and collaborate for: –Better accuracy –Sharing the costs –But not attempted in this paper (future work) Sensor deployment along a straight path Formation of a cluster

6. Classification Techniques k-NN is one of the simplest, yet accurate methods. –Given a set of samples known samples, U –Fetch k (≥ 1) closest known samples from U –Classifies the unknown sample as the majority class of the drawn k samples. Maximum Likelihood (ML) Real time computation is proportional to: –d × l × c (for KNN) –d 2 (for ML) –d : size of feature vectors, l : class size, c : number of classes Conclusion: Features vector size is important

7. Feature Extraction Hundreds of features to choose from acoustic signatures Two demands that compete with each other –Low dimensional features that are yet effective Acoustic features –Power spectral density Power is concentrated in the lower range of frequencies Assault Amphibian Vehicles Dragon Wagon

8. Feature Extraction Schemes Pruning Step 1: Select the frequencies that have the maximum power as reported by training samples: – where Pruning Step 2: Ranking and selecting only a % of them: – (< ) Independent Feature Selection –a Global Feature Selection –s

9. Experimental Study DAPRA/IXO SenseIT dataset –Two types of vehicles (AAV and DW) –Total 389 samples (180 AAV, and 209 DW) Simulated a network of (3 ~ 40) sensors –In order to create a local copy of unknown (testing) sample for a sensor, a signal is attenuated based on its distance from the moving vehicle, and white noise is added Performance Metrics –Classification accuracy –Communication (energy) expenditure

10. Evaluation Methodology Classification accuracy: –Based on leave-one-out policy Energy expenditure model: –E r = 50nJ/bit and E s = 50+.1×R 3 nJ/bit, where E r is the energy required to receive one bit and E s is the energy required to send one bit at R distance. L1 Distance Metric

11. Size of IFS and GFS Feature Vectors Evaluation of Results IFSGFS Size does not go beyond 20 and 15 in IFS and GFS respectively

12. Classification Accuracy Evaluation of Results KNNML ML outperforms KNN

13. Communication Costs Evaluation of Results KNNML DEF is less expensive than DAF

14. Comparison with other studies Evaluation of Results

15. Conclusion and Future Direction Classifying ground vehicles is an important problem in wireless sensor networks. We have two main contributions in this work: –Distributed data/decision fusion framework for classification –New feature extraction schemes that can produce low dimensional yet effective features We conducted a simulation study using real acoustic signals of military vehicles, and our proposed features achieved better classification accuracy In the future: –Improve the efficiency of our proposed schemes. –Consider more than two classes of ground vehicles

16. Thank You !