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Distributed and Efficient Classifiers for Wireless Audio-Sensor Networks

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

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Distributed and Efficient Classifiers for Wireless Audio-Sensor Networks

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

  2. Outline Of The Talk • Introduction • 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 5 min 6 min 2 min

  3. Introduction • Vehicle classification is an important problem in WSN • Tracking • Localization • 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 • Lesser processing requirements

  4. Vehicle Classification • Vehicle classification is crucial to tracking • Only vehicles of interest are reported • 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 Class A Class B Class A Disclaimer: Images used above are collected through Google’s search engine

  5. A Framework for Classification • 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 cluster can perform classification independently. • Multiple clusters may be formed and collaborate for: • Better accuracy • Sharing the costs • But not attempted in this paper (future work) Formation of a cluster Sensor deployment along a straight path

  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) • d2 (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 Dragon Wagon Assault Amphibian Vehicles

  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: • Er = 50nJ/bit and Es = 50+.1×R3 nJ/bit, where Er is the energy required to receive one bit and Es is the energy required to send one bit at R distance. • L1 Distance Metric

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

  12. Evaluation of Results Classification Accuracy KNN ML • ML outperforms KNN

  13. Evaluation of Results Communication Costs KNN ML • DEF is less expensive than DAF

  14. Evaluation of Results Comparison with other studies

  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 !

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