a cluster-based approach for data handling in self- organising sensor networks ucl secoas team: dr....
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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
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 Level
Functional Planes
Sampled data Battery level Cost matrix etc
Location Cluster group Neighbours ID etc
Network scenarioNetwork scenario
1 2 3 4
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
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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
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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
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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)
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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
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
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