Distributed and Efficient Classifiers for Distributed and Efficient Classifiers for Wireless Audio-Sensor NetworksWireless Audio-Sensor Networks

Baljeet MalhotraBaljeet MalhotraIoanis NikolaidisIoanis Nikolaidis

Mario A. NascimentoMario A. Nascimento

University of AlbertaUniversity of AlbertaCanadaCanada

To be presented at:INSS, June 17-19, 2008, Kanazawa, Japan

Supported By:

Outline Of The TalkOutline Of The Talk

Introduction

MotivationMotivation

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 DiscussionsResults and Discussions

Conclusion and Future Directions

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IntroductionIntroduction

Vehicle classification is an important problem in Vehicle classification is an important problem in WSNWSN

– TrackingTracking– LocalizationLocalization

Tracking can be facilitated by:Tracking can be facilitated by:– Video/Image based sensors Video/Image based sensors – RFID tagsRFID tags– Limitations:Limitations:

Video/Image requires higher processing capabilitiesVideo/Image requires higher processing capabilities RFID tags may not be feasibleRFID tags may not be feasible

Acoustic target tracking Acoustic target tracking – Lesser processing requirementsLesser processing requirements

Vehicle ClassificationVehicle Classification

Vehicle classification is crucial to trackingVehicle classification is crucial to tracking

Only vehicles of interest are reportedOnly vehicles of interest are reported

Problem becomes much challenging if there are Problem becomes much challenging if there are more vehicles of the same class more vehicles of the same class – Identification problem Identification problem

This paper deals with the problem of vehicle This paper deals with the problem of vehicle classification only and NOT identificationclassification only and NOT identification

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Class A Class B Class A

A Framework for ClassificationA Framework for Classification

Nodes organize themselves into neighborhoods Nodes organize themselves into neighborhoods “clusters” as a vehicles crosses through an area “clusters” as a vehicles crosses through an area monitored by sensorsmonitored by sensors

A master node is selected based on the signal A master node is selected based on the signal strength. 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 accuracyBetter accuracy– Sharing the costsSharing the costs– But not attempted in this paper (future work)But not attempted in this paper (future work)

Sensor deployment along a straight path Formation of a cluster

Classification TechniquesClassification 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

Feature ExtractionFeature 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 densityPower spectral density

Power is concentrated in the lower range of frequencies

Assault Amphibian Vehicles

Dragon Wagon

Feature Extraction SchemesFeature 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

Experimental StudyExperimental 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

Evaluation MethodologyEvaluation 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

Size of IFS and GFS Feature Vectors

Evaluation of Results

IFS GFS

• Size does not go beyond 20 and 15 in IFS and GFS respectively

Classification Accuracy

Evaluation of Results

KNN ML

• ML outperforms KNN

Communication Costs

Evaluation of Results

KNN ML

• DEF is less expensive than DAF

Comparison with other studies

Evaluation of Results

Conclusion and Future DirectionConclusion 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

Thank You !Thank You !