Self-Organisation in Self-Organisation in SECOAS Sensor NetworkSECOAS Sensor Network

UCL SECOAS team: Dr. Lionel Sacks, Dr. Matt BrittonUCL SECOAS team: Dr. Lionel Sacks, Dr. Matt BrittonToks Adebutu, Aghileh Marbini, Venus Shum, Ibiso WokomaToks Adebutu, Aghileh Marbini, Venus Shum, Ibiso Wokoma

Presented by Venus ShumPresented by Venus Shum

Advance Communications and Systems Engineering groupAdvance Communications and Systems Engineering group

University College LondonUniversity College London

Supervisor: Dr. Lionel SacksSupervisor: Dr. Lionel Sacks

ContentContent

The SECOAS sensor networkThe SECOAS sensor networkSECOAS architectureSECOAS architectureDistributed Algorithms OverviewDistributed Algorithms OverviewData Handling in SECOASData Handling in SECOAS

The SECOAS The SECOAS Sensor NetworkSensor Network

SECOAS projectSECOAS project SECOAS – Self-Organised Collegiated Sensor SECOAS – Self-Organised Collegiated Sensor

Network ProjectNetwork Project Aim: To collect oceanographic data with good Aim: To collect oceanographic data with good

temporal and spatial resolutiontemporal and spatial resolution Why SECOAS?Why SECOAS?

Traditionally done by 1 (or a few) expensive high-Traditionally done by 1 (or a few) expensive high-precision sensor nodesprecision sensor nodes

Lack of spatial resolutionLack of spatial resolution Data obtained upon recovery of sensor nodesData obtained upon recovery of sensor nodes Data gathered in burst – may miss important features.Data gathered in burst – may miss important features.

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SolutionSolution Use of sensor ad-hoc networkUse of sensor ad-hoc network

large number of Lower-cost, disposable sensors (tens to large number of Lower-cost, disposable sensors (tens to thousands, maybe more).thousands, maybe more).

provide temporal as well as spatial resolutionprovide temporal as well as spatial resolutionwireless communication - data are dispatched to the wireless communication - data are dispatched to the

base station to the users in regular intervalsbase station to the users in regular intervalsad-hoc nature – easily adopt to addition and removal of ad-hoc nature – easily adopt to addition and removal of

nodes nodes

Other Characteristics: Other Characteristics: distributed distributed low processing power low processing power stringent battery requirementstringent battery requirementcommunication constraint communication constraint

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SpecialtiesSpecialties Distributed system and Distributed system and

distributed algorithms.distributed algorithms. Use of complex system Use of complex system

concept when designing concept when designing algorithms – simple rules algorithms – simple rules lead to desirable global lead to desirable global behaviourbehaviour

Biologically-inspired Biologically-inspired algorithmsalgorithms

A custom-made kind-of OS A custom-made kind-of OS (kOS) tailor for (kOS) tailor for implementation of Distributed implementation of Distributed algorithms algorithms

Base Station

WiredNetwork

SeaLand

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SECOAS ArchitectureSECOAS Architecture

Physical Structure of a sensor nodePhysical Structure of a sensor node

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Functional PlanesFunctional Planes

Node Level

Functional Planes

Sampled data Battery level Cost matrix etc

Location Cluster group Neighbours ID etc

Spatial Coordination of nodes formingSpatial Coordination of nodes forming Location planeLocation plane Clustering planeClustering plane

Data Fusion planeData Fusion plane Adaptive sampling planeAdaptive sampling plane

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Distributed Algorithms Distributed Algorithms OverviewOverview

Characteristics of DAsCharacteristics of DAs

Easy addition, alteration and removal of Easy addition, alteration and removal of functionality (just plug them together!)functionality (just plug them together!)

Self-organising, self-managing and self-Self-organising, self-managing and self-optimisingoptimising

No knowledge of a global stateNo knowledge of a global stateA stateless machine is good for easy A stateless machine is good for easy

implementationimplementationRequired Required interfaces interfaces for algorithms to talk for algorithms to talk

to each otherto each other

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kOS – the supporting platformkOS – the supporting platform Kind-of operating systemKind-of operating system Individual algorithms responsible for scheduling Individual algorithms responsible for scheduling

their actionstheir actions Virtualisation of algorithms – Virtualisation of algorithms –

software can use kOS functions disregarding their software can use kOS functions disregarding their physical locationphysical location

Interfaces to other physical boards are providedInterfaces to other physical boards are provided Easy exchange of parameters between algorithmsEasy exchange of parameters between algorithms

Adaptive scheduling to distribute resources Adaptive scheduling to distribute resources according to environmentaccording to environment

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Interaction of algorithmsInteraction of algorithms

IntelligentradioGossip

Adaptivesensing

CRmanage-

ment agent

QSClustering

Auto-location

Datacompres

sion /fusion

Adaptiveschedu-

ling

CRmanage-

ment agent

Adaptivesensing

Gossip Intelligentradio

Radio Module

kOS

Sensor Module

Alg

Alg

Virtualizedalgorithm

Physicalalgorithm

C.PControl

Parameters

posPosition

Information

IntelligentradioGossip

Adaptivesensing

CRmanage-

ment agent

QSClustering

Auto-location

Datacompres

sion /fusion

Adaptiveschedu-

ling

CRmanage-

ment agent

Adaptivesensing

Gossip Intelligentradio

Radio Module

kOS

Sensor Module

Alg

Alg

Virtualizedalgorithm

Physicalalgorithm

C.PControl

Parameters

posPosition

Information

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Parameter sharing among neighboursParameter sharing among neighbours

Enable exchange of information between Enable exchange of information between nodesnodes

An interesting facts of UCL SECOAS team:An interesting facts of UCL SECOAS team:Consist of 4 (pretty) women and 1 guyConsist of 4 (pretty) women and 1 guy

=> gossip!=> gossip!2 characteristics of gossiping2 characteristics of gossiping

Selective/random targetsSelective/random targetsDon’t always pass information that is exactly the Don’t always pass information that is exactly the

same! (Add salt and vinegar)same! (Add salt and vinegar)

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Gossiping protocol in SECOASGossiping protocol in SECOAS Type 1: Passing the Type 1: Passing the

exact parameters to exact parameters to randomly selected nodesrandomly selected nodes

Type 2: Passing altered Type 2: Passing altered parameters to all parameters to all neighbour nodesneighbour nodes

Efficient protocol and Efficient protocol and avoid floodingavoid flooding

Low latency requirement Low latency requirement and network has weak and network has weak consistency consistency

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Data Handling in SECOASData Handling in SECOAS

Before data handling, there isBefore data handling, there is

Data analysis first Data analysis first To get a first hand knowledge of the data dealt withTo get a first hand knowledge of the data dealt with important on engineering solutionimportant on engineering solution

Trend, periods, correlation, self-similarity, heavy Trend, periods, correlation, self-similarity, heavy tail, etc. tail, etc.

=> modelling=> modelling Test data from Wavenet project. Test data from Wavenet project.

Consists of 3 months burst data from April-June 03Consists of 3 months burst data from April-June 03 Temperature, pressure, conductivity and sedimentTemperature, pressure, conductivity and sediment

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Basic AnalysisBasic Analysis

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Temperature

Con

duct

ivity

Mean of conductivity vs mean of temperature

y = 1.141*x - 32.38

10-3

10-2

10-1

100

10-7

10-6

10-5

10-4

10-3

10-2

10-1 (a) Power spectrum of Autocorrelation of Pressure

frequency (Hz)

f = 0.0939 (Hz), Period = 10.65 s

1024 data points sampled at 1Hz

10-3

10-2

10-1

100

10-6

10-5

10-4

10-3

10-2

10-1

(b) Power spectrum of Autocorrelation of Detrended Temperature

frequency (1/hr)

f = 0.0780 (1/hr), Period = 12.82 hr

f = 0.0411 (1/hr), Period = 24.35 hr

971 Data points from obtaining mean of each hour

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Extraction of anomalies using Extraction of anomalies using waveletwavelet

100 200 300 400 500 600 700 800 900 10000

100

200Wavelet Decomposition for Sediment (FTU) in Hour 1 using Haar Wavelet

Sig

na

l

100 200 300 400 500 600 700 800 900 1000100

200

300

Sca

l. C

oe

f.

100 200 300 400 500 600 700 800 900 1000-50

0

50

16

100 200 300 400 500 600 700 800 900 1000-50

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50

8

100 200 300 400 500 600 700 800 900 1000-50

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100 200 300 400 500 600 700 800 900 1000-50

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50

2

100 200 300 400 500 600 700 800 900 1000-100

0

100

1

Time (s)

j

t (

s)

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Data Handling processData Handling processSampling &Data pre-

processing

Node-levelcompression

Extract PPI

Route tobase station

ClusteringSensingStrategy

Spatialdata fusion

“raw data”Compressed

data

PPIClusters

info

Fuseddata

Temporal extract interesting features for Temporal extract interesting features for clusteringclustering

Temporal compressionTemporal compression Clustering for spatial data fusion and sensing Clustering for spatial data fusion and sensing

strategystrategy

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Spatial StrategiesSpatial StrategiesDivide the monitored area into regions of Divide the monitored area into regions of

interest based on a Physical Phenomenon interest based on a Physical Phenomenon of Interest (PPI) parameter. of Interest (PPI) parameter.

PPI is used to form clustersPPI is used to form clustersThe division is used as basis for spatial The division is used as basis for spatial

sampling and data fusion strategysampling and data fusion strategy

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Clustering AlgorithmClustering Algorithm An algorithm inspired by Quorum sensing carried out by An algorithm inspired by Quorum sensing carried out by

bacteria cells to determine when there is minimum bacteria cells to determine when there is minimum concentration of a particular substance to carry out concentration of a particular substance to carry out processes such as processes such as bioluminescencebioluminescence..

AnalogyAnalogy Concentration of substance => PPIConcentration of substance => PPI Bacteria cell => sensor nodesBacteria cell => sensor nodes Process group => clustersProcess group => clusters

Self-organisation – The network is Self-organisation – The network is divided into regions of interest divided into regions of interest without knowledge of the global without knowledge of the global states of nodes.states of nodes.

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SummarySummarySECOAS aims to provide temporal and SECOAS aims to provide temporal and

spatial oceanography data with an ad-hoc spatial oceanography data with an ad-hoc distributed networkdistributed network

Complex system concept and biologically Complex system concept and biologically inspired algorithms are used to achieve inspired algorithms are used to achieve self-organisation in the networkself-organisation in the network

Demonstrate the basic architecture of data Demonstrate the basic architecture of data handlinghandling

Future direction: WORK HARD!!Future direction: WORK HARD!!Continue data analysis and modelingContinue data analysis and modelingDevelop spatial sampling and fusion strategyDevelop spatial sampling and fusion strategy

Thanks for the attention!Thanks for the attention!

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