A Cluster-based Approach for A Cluster-based Approach for Data Handling in Self-Data Handling in Self-

organising Sensor Networks organising Sensor Networks

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 networkObjectives and approaches of data Objectives and approaches of data

handlinghandling

Spatial algorithmsSpatial algorithmsSupporting platform and message Supporting platform and message

exchangeexchange

The SECOAS The SECOAS Sensor NetworkSensor Network

SECOAS projectSECOAS project SECOAS – Self-Organised Collegiate Sensor SECOAS – Self-Organised Collegiate 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 Equipment needs to be recovered at the end of the Equipment needs to be recovered at the end of the

data gathering exercisedata gathering exercise Burst data - May miss interesting featuresBurst data - May miss interesting features

1 2 3 4

The sensor network approachThe sensor network approach

A distributed system/ networkA distributed system/ network

Characteristics:Characteristics:

Large number Large number

Low costLow cost

Low processing power Low processing power

AdvantagesAdvantages

Provide temporal and spatial resolutionProvide temporal and spatial resolution

Data dispatched to the scientist in regular intervalData dispatched to the scientist in regular interval

Wireless ad hoc network Wireless ad hoc network

Stringent battery requirementStringent battery requirement

communication constraintcommunication constraint

1 2 3 4

SECOAS SpecialtiesSECOAS Specialties

Distributed Algorithms Distributed Algorithms

A A clusteringclustering approach for data handling approach for data handling

Biologically-inspired algorithmsBiologically-inspired algorithms

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

1 2 3 4

Node architectureNode architecture

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Network scenarioNetwork scenario

Land Station

WiredNetwork

SeaLand

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Node Level

Functional Planes

Sampled data Battery level Cost matrix etc

Location Cluster group Neighbours ID etc

Network scenarioNetwork scenario

1 2 3 4

Objectives and approaches Objectives and approaches of data handlingof data handling

A simplified scenarioA simplified scenario All nodes sampleAll nodes sample

1.1. SamplingSampling

2.2. Temporal compressionTemporal compression

3.3. Data route back to base Data route back to base stationstation

4.4. Spatial compression when Spatial compression when possiblepossible

Not optimal becauseNot optimal because Data Redundancy Data Redundancy Power usage for sampling Power usage for sampling

and comm.and comm.

21 3 4

A clustering approachA clustering approach A clustering approach for spatial data handlingA clustering approach for spatial data handling the monitored area is partitioned into interesting the monitored area is partitioned into interesting

groups groups strategies are carried out based on the cluster strategies are carried out based on the cluster

formations. formations. Clustering Requirements specific to SECOASClustering Requirements specific to SECOAS

ScalableScalable Dynamic and adaptiveDynamic and adaptive SimpleSimple Distributed, not rely on underlying network Distributed, not rely on underlying network

architecturearchitecture RobustRobust

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Resources analysisResources analysis

Scientist

algorithms

Battery power

Data Resolutiontunes

Bandwidth

Memory

provides

controls

controls

controls

Scientist

algorithms

Battery power

Data Resolutiontunes

Bandwidth

Memory

provides

controls

controls

controls

ResourcesResources Battery powerBattery power

+ Processing + Processing powerpower

BandwidthBandwidth MemoryMemory

Data resolution Data resolution is a goal is a goal Abstract Abstract

conceptconcept set by userset by user Related to the Related to the

environmentenvironment

21 3 4

Processing of algorithms

Battery power

Sensing Battery power

Memory

Communication Battery power (++)

Bandwidth

A resource scenarioA resource scenario

Data fusion save Data fusion save power, memory and power, memory and bandwidthbandwidth Radio: processing = Radio: processing =

20:1 in the first trial20:1 in the first trial

Increase sampling Increase sampling nodes = increase nodes = increase resolutionresolution

Final results Final results feedback to feedback to algorithmsalgorithms

No. of sampling nodes

Data fusion

increase

+ data resolution- power (sampling + radio)- bandwidth- memory

- data resolution+ power (radio - processing)+ bandwidth+ memory

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Parameter spaceParameter space The parameters set The parameters set

(physical phenomena of (physical phenomena of interest PPI) used for interest PPI) used for clusteringclustering

Need to find out what Need to find out what characterise the characterise the measurement – data analysismeasurement – data analysis Pressure, salinity, temperature, Pressure, salinity, temperature,

sediment, tilts sediment, tilts

TheThe Mean Mean, does not mean a , does not mean a lot in most caseslot in most cases

0 50 100 150 200 250 300-1

-0.5

0

0.5

1(a) sine wave

0 50 100 150 200 250 300-4

-2

0

2

4(b) Guassian(0,1)

0 50 100 150 200 250 300-1

-0.5

0

0.5(c) False Guassian Noise (0,1) with H = 0.8

21 3 4

Spatial AlgorithmsSpatial Algorithms

Information FlowInformation Flow

Oceanfeatures

ADC

Auto-location

Temporal data proc.

Radio Rx

Analoguesensor

voltages

Discrete sensorreadings

Discrete radio signal strength

Temp. compressed data

PPI

Location andrange

Cluster info

Quantization andsampling error

Info loss due to lossycompression

Algo.Precision error

Algo.Precision error

Quantization errorprecision error

Compression lossAlgo. Precision error

Sensing algo. &data fusion

Quorum Sensing

31 2 4

Auto-location algorithmAuto-location algorithm

Iterative averagingIterative averaging Position aware nodes (PA) Position aware nodes (PA)

and position determining and position determining nodes (PD)nodes (PD)

Position propagates from Position propagates from PAs to PDs. PDs use PAs to PDs. PDs use averaging to estimate averaging to estimate position iteratively.position iteratively.

Simple, distributed and self-Simple, distributed and self-organised organised

Coordinates ActionsOriginate node (coordinate) destination nodes

A B C D E

0 - - - 8 A(0) BE(8) D

0 0 - 8 8 B(0) C & AD(8) C & E

0 0 4 8 8 C(4) B & D

0 2 4 6 8 B(2) A& CD(4) C & E

31 2 4

Results - Auto-locationResults - Auto-location

0 50 100 150 200 250 300 350 4000

50

100

150

200

250

300

350

400

Dis

tan

ce/m

Distance/m

0 50 100 150 200 250 300 350 4000

50

100

150

200

250

300

350

400

Dis

tan

ce/m

Distance/m

0 10 20 30 40 50 600

1

2

3

4

5

6

7

8

9

10

Error (m)N

um

be

r o

f No

de

s

31 2 4

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

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

AnalogyAnalogy Concentration of substance => PPIConcentration of substance => PPI Bacteria cell => sensor nodesBacteria cell => sensor nodes Processed group => clustersProcessed group => clusters

The range of the grouping is The range of the grouping is determined by LALI used by determined by LALI used by e.g. ant e.g. ant cemetery construction

LALI (local activation LALI (local activation lateral inhibition)lateral inhibition)

31 2 4

Results - clusteringResults - clustering

Only local/ Only local/ neighbour neighbour information is information is required for required for forming clusters.forming clusters.

Independent of Independent of topologytopology

Dynamic and Dynamic and scalablescalable

31 2 4

Supporting platform and Supporting platform and message exchangemessage exchange

kOS – kind-of operating systemkOS – kind-of operating system Full support of distributed algorithmsFull support of distributed algorithms 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 environmental conditionaccording to environmental condition

41 2 3

Interaction of algorithms within a nodeInteraction of algorithms within a node

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

41 2 3

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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Gossip protocol in SECOASGossip protocol in SECOAS Type 1: Passing the exact Type 1: Passing the exact

parameters to randomly parameters to randomly selected nodes (multi-hop)selected nodes (multi-hop)

Type 2: Passing altered Type 2: Passing altered parameters to all neighbour parameters to all neighbour nodes (also, one hop only)nodes (also, one hop only)

Efficient protocol and avoid Efficient protocol and avoid floodingflooding

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

41 2 3

Finally…Finally…

Conclusion and Future workConclusion and Future workSECOAS data handling uses cluster-based SECOAS data handling uses cluster-based

approachapproachNext step:Next step:

Find the suitable parameters (PPI) from data Find the suitable parameters (PPI) from data analysisanalysis

Investigate how they work with the clustering Investigate how they work with the clustering algorithmalgorithm

Auto-location optimises using number of Auto-location optimises using number of position aware nodes, signal strength, etc. position aware nodes, signal strength, etc.

Investigate temporal compression and spatial Investigate temporal compression and spatial fusion strategyfusion strategy

Thanks for the attention!Thanks for the attention!

Now Q&ANow Q&A