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Introduction Fully Distributed Schemes Decentralised Schemes Current Work Conclusion Acknowledgements
Distributed Pattern Recognition andClassification in Wireless Sensor Networks
Alexander SeniorSupervisors: Y. Ahmet Sekercioglu Asad Khan
Department of Electrical and Computer Systems EngineeringFaculty of Engineering
Monash University
May 28, 2012
Alexander Senior ESCE Monash University
Distributed Pattern Recognition and Classification in Wireless Sensor Networks
Introduction Fully Distributed Schemes Decentralised Schemes Current Work Conclusion Acknowledgements
Introduction
WSNs:small amount of memory‘weak’ processorsenergy poor – communication is THE biggest drain on battery
Want to focus on distributed schemes — computation andstorage are shared throughout the networkDistinction between the degrees of ‘distributed-ness’:
Fully distributed: absolutely no reliance on a base station (e.g.computer) or a root node that has authority over the entirenetworkDecentralised: requires a base station or network leader,though work is shared throughout the network
Focus on schemes that are (at least) adapted for WSNs, orhave had real-world evaluation
Alexander Senior ESCE Monash University
Distributed Pattern Recognition and Classification in Wireless Sensor Networks
Introduction Fully Distributed Schemes Decentralised Schemes Current Work Conclusion Acknowledgements
1 Introduction
2 Fully Distributed SchemesKohonen Self-Organising MapSupport Vector Machines
3 Decentralised SchemesGraph NeuronAdaptive Resonance TheoryOther decentralised schemes
4 Current Work
5 Conclusion
6 Acknowledgements
Alexander Senior ESCE Monash University
Distributed Pattern Recognition and Classification in Wireless Sensor Networks
Introduction Fully Distributed Schemes Decentralised Schemes Current Work Conclusion Acknowledgements
Kohonen Self-Organising Map
Kohonen Self-Organising Map (SOM)
Winner-takes all neural network — units/neurons compete towin the data
Each unit contains a prototype vector which is initiallyrandom, evolves as data is input into the map
Units win data that their prototype vectors are closest to
As units win data, all units update their prototype vectoraccording to:
current datadistance between their vector and winning vector — amount isaffected by neighbourhood functionlearning rate
Alexander Senior ESCE Monash University
Distributed Pattern Recognition and Classification in Wireless Sensor Networks
Introduction Fully Distributed Schemes Decentralised Schemes Current Work Conclusion Acknowledgements
Kohonen Self-Organising Map
Self-Organisation in Ad Hoc Networks: An Empirical StudyCatterall, van Laerhovena and Strohbach, 2003
Implemented SOM on Smart-It platform
One unit assigned to each nodeNodes broadcast their sensor readings to each other so thatwinning unit can be foundNodes can be added/removed in an ad-hoc fashion
Collected data on Smart-Its, and executed algorithm onsimilar hardware
Totally distributed scheme, no leader necessary
Method of choosing winner not discussed; broadcasts will notbe feasible in large networks; no evaluation of effectiveness
Alexander Senior ESCE Monash University
Distributed Pattern Recognition and Classification in Wireless Sensor Networks
Introduction Fully Distributed Schemes Decentralised Schemes Current Work Conclusion Acknowledgements
Support Vector Machines
Support Vector Machines
Primarily used for classifying data points as either ‘positive’ or‘negative’
Linear classifiers operate on data directly to produce theirclassification
Non-linear classifiers employ the ‘kernel trick’ — data istransformed into higher-dimensional space, and the performtransformation in this space
Transformation can be found using optimisation techniqueswith training data
Alexander Senior ESCE Monash University
Distributed Pattern Recognition and Classification in Wireless Sensor Networks
Introduction Fully Distributed Schemes Decentralised Schemes Current Work Conclusion Acknowledgements
Support Vector Machines
Support Vector Machine for Distributed Classification: aDynamic Consensus ApproachWang, Li and Zhou, 2009
Altered SVM algorithm for totally distributed evaluation
Nodes run their own SVM on their local data, and exchangedata locally to improve their estimate of the classification
Simulation verification with 36 nodes
Performance nearly as good as centralised method
Needs training
No mention of difficult of evaluating algorithm on nodes
Alexander Senior ESCE Monash University
Distributed Pattern Recognition and Classification in Wireless Sensor Networks
Introduction Fully Distributed Schemes Decentralised Schemes Current Work Conclusion Acknowledgements
Graph Neuron
Graph Neuron (GN)
Take a pattern and separate it into (position, value) pairs, e.g.’WSN’={(1,W ), (2,S), (3,N)}Every possible (position,value) pair is represented by a neuron
Neurons only activate if their pair is present in the pattern
Also have Stimulator and Interpreter (S&I)
Alexander Senior ESCE Monash University
Distributed Pattern Recognition and Classification in Wireless Sensor Networks
Introduction Fully Distributed Schemes Decentralised Schemes Current Work Conclusion Acknowledgements
Graph Neuron
GN continued
Active neurons broadcast their value toadjacent pairs — other active neurons usethese broadcasts to form their bias array:map from active neighbours (on left andright) to an index
Neurons determine if they haveencountered (sub-)patterns before byconsulting their bias array
S&I stores indices emitted by neurons
Memorising sub-patterns enables compactstorage
Alexander Senior ESCE Monash University
Distributed Pattern Recognition and Classification in Wireless Sensor Networks
Introduction Fully Distributed Schemes Decentralised Schemes Current Work Conclusion Acknowledgements
Graph Neuron
X(1)
O(2)
X(3)
X(1)
O(2)
O(3)
P1 P2
O(4)X(4)
X(2)
X(3)
X(4)
X(1)
O(2)
O(3)
O(4)
O(1)Port sequence:6 (bias RED)6 (bias BLUE)
2 (bias GREEN)
Port sequence:6,4 (bias RED)
2,8 (bias BLACK)
N1
N2
N3
N4
N5
N6
N7
N8
Port sequence:3 (bias RED)
Port sequence:1,3 (bias RED)1,7 (bias BLUE)
Port sequence:6,8 (bias BLUE)
2,8 (bias GREEN)
Port sequence:7 (bias BLUE)
3 (bias BLACK)7 (bias GREEN)
P1, P2
P1,P2
P1
P1
P2
P2
O(1)
X(2)
X(3)
P3
O(4)
Port sequence:2 (bias BLACK)
Port sequence:5,3 (bias BLACK)1,7 (bias GREEN)
X(1)
X(2)
O(3)
O(4)
P4
Alexander Senior ESCE Monash University
Distributed Pattern Recognition and Classification in Wireless Sensor Networks
Introduction Fully Distributed Schemes Decentralised Schemes Current Work Conclusion Acknowledgements
Graph Neuron
Hierarchical Graph Neuron
Problem with base GNalgorithm: crosstalk
Compare stored patterns‘abcdf’ and ‘fbcde’ with newpattern ‘abcde’: havecommon sub-patterns, falserecall
Use a hierarchy of arrays tosolve this problem:Hierarchical Graph Neuron(HGN)
Alexander Senior ESCE Monash University
Distributed Pattern Recognition and Classification in Wireless Sensor Networks
Introduction Fully Distributed Schemes Decentralised Schemes Current Work Conclusion Acknowledgements
Graph Neuron
HGN continued
Indices of lower arrays are fed asinput to higher arrays: upper levelbias arrays contain bias indices ofleft and right neurons and index ofnode directly below it — this solvescrosstalk problem
Hierarchy allows S&I to match withnoisy patterns: if a match cannotbe obtained from top-most level,can get consensus from lower levels
Have many more neurons
Alexander Senior ESCE Monash University
Distributed Pattern Recognition and Classification in Wireless Sensor Networks
Introduction Fully Distributed Schemes Decentralised Schemes Current Work Conclusion Acknowledgements
Graph Neuron
Cellular Microscopic Pattern Recogniser
Variant of GN
Neurons are arrangedlogically in a series ofconcentric circles (tracks)
Neurons function similarly asbefore, but also report toneurons in inner track
Inner neurons have moreauthority over network, aswith higher layers in HGN
Alexander Senior ESCE Monash University
Distributed Pattern Recognition and Classification in Wireless Sensor Networks
Introduction Fully Distributed Schemes Decentralised Schemes Current Work Conclusion Acknowledgements
Graph Neuron
Comparisons, advantages and disadvantages of GN
No complex computations needed — simple integer arithmetic
Storage needed proportional to unique sub-patternsencountered, not patterns
Simulations have shown good performance against otherrecognisers/classifiers such as SOM, SVM
Not fully distributed — need S&I to function
Will need to be adapted to cope with complex and changingtopologies of WSNs
No implementation in physical WSNs
Alexander Senior ESCE Monash University
Distributed Pattern Recognition and Classification in Wireless Sensor Networks
Introduction Fully Distributed Schemes Decentralised Schemes Current Work Conclusion Acknowledgements
Adaptive Resonance Theory
Adaptive Resonance Theory (ART)
Neural network like SOM, however:
have long and short term memoryadditional pattern classificationscan be learned by the systemwithout user intervention
Typical ART neural networkcomposed of input layer (L0),comparison layer (L1) and categorylayer (L2); also have a sensitivity(vigilance) threshold
Taken from Kulakov and Davcev, “IntelligentWireless Sensor Networks Using FuzzyARTNeural-Networks”, 2007.
Alexander Senior ESCE Monash University
Distributed Pattern Recognition and Classification in Wireless Sensor Networks
Introduction Fully Distributed Schemes Decentralised Schemes Current Work Conclusion Acknowledgements
Adaptive Resonance Theory
ART continued
Category nodes contain prototypevectors — pattern ‘captured’ by nodesthat have the highest bottom-upactivation, and also have a highenough similarity
Prototype vector of activated categoryneuron updated, amount set bylearning rate
If no category neuron activates, inputforms a new category node
ART1 classifies binary data,FuzzyART classifies analog data
Taken from Kulakov and Davcev,“Intelligent Wireless Sensor NetworksUsing FuzzyART Neural-Networks”,2007.
Alexander Senior ESCE Monash University
Distributed Pattern Recognition and Classification in Wireless Sensor Networks
Introduction Fully Distributed Schemes Decentralised Schemes Current Work Conclusion Acknowledgements
Adaptive Resonance Theory
Intelligent Wireless Sensor Networks Using FuzzyARTNeural-NetworksKulakov and Davcev, 2007
Used ‘clustered approach’:
cluster members all run a FuzzyART neural network, produceinteger category labelscluster leader runs an ART1 neural network to classify labels
Ran in small network with MicaZ motes
Sensitivity threshold is dynamically monitored so node’smemory is not overwhelmed
No evaluation of effectiveness of classification
Alexander Senior ESCE Monash University
Distributed Pattern Recognition and Classification in Wireless Sensor Networks
Introduction Fully Distributed Schemes Decentralised Schemes Current Work Conclusion Acknowledgements
Adaptive Resonance Theory
Intruder Detection using a wireless sensor network with anintelligent mobile robot responseLi and Parker, 2008
Builds on work of Kulakov and Davcev, focus is on intruderdetection
Adds a Markov model after ART1 network in cluster leader todetermine if state transitions are abnormal
Scheme adapted to cope with missing data
Good improvement over previous scheme
Alexander Senior ESCE Monash University
Distributed Pattern Recognition and Classification in Wireless Sensor Networks
Introduction Fully Distributed Schemes Decentralised Schemes Current Work Conclusion Acknowledgements
Adaptive Resonance Theory
Building Intrusion Detection with a Wireless SensorNetworkWalchli and Braun, 2010
Also uses FuzzyART — detection of abnormal activity inoffice environment
For cluster members, communication scheme is even simplerthan before: if a new category node has to be created, ‘1’ issent to cluster leader, otherwise ‘0’ implicitly assumed
Real-world implementation and testing with Embedded SensorBoards (MSP430 microcontroller)
Performed better than threshold technique, but no othercomparison made
Alexander Senior ESCE Monash University
Distributed Pattern Recognition and Classification in Wireless Sensor Networks
Introduction Fully Distributed Schemes Decentralised Schemes Current Work Conclusion Acknowledgements
Other decentralised schemes
Prototype modelling
Similar to neural network schemes in that data is classified viaprototype vectors
Prototype vectors formed by manipulating training data
Data is classified by determining which prototype vector it isclosest to
Alexander Senior ESCE Monash University
Distributed Pattern Recognition and Classification in Wireless Sensor Networks
Introduction Fully Distributed Schemes Decentralised Schemes Current Work Conclusion Acknowledgements
Other decentralised schemes
A System for Distributed Event Detection in WirelessSensor NetworksWittenburg et al., 2010
Focus is event detection - wireless alarm system forconstruction site
Real-life testing with Scatterweb nodes
Training data collected in network, then full readings sent tobase station
Prototype vector consider readings from several adjacentnodes — nodes broadcast to neighbours (GN)
Alexander Senior ESCE Monash University
Distributed Pattern Recognition and Classification in Wireless Sensor Networks
Introduction Fully Distributed Schemes Decentralised Schemes Current Work Conclusion Acknowledgements
Current Work
What if we had nodes with very limited abilities but in largeamounts and with large numbers of connections available?
Work is VERY preliminary
Alexander Senior ESCE Monash University
Distributed Pattern Recognition and Classification in Wireless Sensor Networks
Introduction Fully Distributed Schemes Decentralised Schemes Current Work Conclusion Acknowledgements
Conclusion
Distributed approaches:
SOM lacks validation and ability to scaleSVM is promising but requires testing in real-world networks
Decentralised approaches:
GN: light-weight, but needs implementation in real-worldnetworksART: most promising candidate yet, and have already hadimplementations; might have scaling issuesPrototype modelling: requires off-line training at base station,but have implementation
Alexander Senior ESCE Monash University
Distributed Pattern Recognition and Classification in Wireless Sensor Networks
Introduction Fully Distributed Schemes Decentralised Schemes Current Work Conclusion Acknowledgements
Acknowledgements
My thanks go to:
Supervisors Ahmet Sekercioglu and Asad Khan
STINT program
Sven Molin and Kim Ng
Fredrik Sandin and Blerim Emruli
Alexander Senior ESCE Monash University
Distributed Pattern Recognition and Classification in Wireless Sensor Networks
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