SelfOrganisation in SECOAS Sensor Network

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Self-Organisation in SECOAS Sensor Network

• UCL SECOAS team Dr. Lionel Sacks, Dr. Matt
  BrittonToks Adebutu, Aghileh Marbini, Venus
  Shum, Ibiso Wokoma
• Presented by Venus Shum
• Advance Communications and Systems Engineering
  group
• University College London
• Supervisor Dr. Lionel Sacks

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Content

• The SECOAS sensor network
• SECOAS architecture
• Distributed Algorithms Overview
• Data Handling in SECOAS

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The SECOAS Sensor Network
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SECOAS project

• SECOAS Self-Organised Collegiated Sensor
  Network Project
• Aim To collect oceanographic data with good
  temporal and spatial resolution
• Why SECOAS?
• Traditionally done by 1 (or a few) expensive
  high-precision sensor nodes
• Lack of spatial resolution
• Data obtained upon recovery of sensor nodes
• Data gathered in burst may miss important
  features.

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Solution

• Use of sensor ad-hoc network
• large number of Lower-cost, disposable sensors
  (tens to thousands, maybe more).
• provide temporal as well as spatial resolution
• wireless communication - data are dispatched to
  the base station to the users in regular
  intervals
• ad-hoc nature easily adopt to addition and
  removal of nodes
• Other Characteristics
• distributed
• low processing power
• stringent battery requirement
• communication constraint

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Specialties

• Distributed system and distributed algorithms.
• Use of complex system concept when designing
  algorithms simple rules lead to desirable
  global behaviour
• Biologically-inspired algorithms
• A custom-made kind-of OS (kOS) tailor for
  implementation of Distributed algorithms

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SECOAS Architecture
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Physical Structure of a sensor node
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Functional Planes

• Spatial Coordination of nodes forming
• Location plane
• Clustering plane

• Data Fusion plane
• Adaptive sampling plane

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Distributed Algorithms Overview
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Characteristics of DAs

• Easy addition, alteration and removal of
  functionality (just plug them together!)
• Self-organising, self-managing and
  self-optimising
• No knowledge of a global state
• A stateless machine is good for easy
  implementation
• Required interfaces for algorithms to talk to
  each other

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kOS the supporting platform

• Kind-of operating system
• Individual algorithms responsible for scheduling
  their actions
• Virtualisation of algorithms
• software can use kOS functions disregarding their
  physical location
• Interfaces to other physical boards are provided
• Easy exchange of parameters between algorithms
• Adaptive scheduling to distribute resources
  according to environment

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Interaction of algorithms
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Parameter sharing among neighbours

• Enable exchange of information between nodes
• An interesting facts of UCL SECOAS team
• Consist of 4 (pretty) women and 1 guy
• gt gossip!
• 2 characteristics of gossiping
• Selective/random targets
• Dont always pass information that is exactly the
  same! (Add salt and vinegar)

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Gossiping protocol in SECOAS

• Type 1 Passing the exact parameters to randomly
  selected nodes
• Type 2 Passing altered parameters to all
  neighbour nodes
• Efficient protocol and avoid flooding
• Low latency requirement and network has weak
  consistency

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Data Handling in SECOAS
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Before data handling, there is

• Data analysis first
• To get a first hand knowledge of the data dealt
  with
• important on engineering solution
• Trend, periods, correlation, self-similarity,
  heavy tail, etc.
• gt modelling
• Test data from Wavenet project.
• Consists of 3 months burst data from April-June
  03
• Temperature, pressure, conductivity and sediment

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Basic Analysis
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Extraction of anomalies using wavelet
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Data Handling process

• Temporal extract interesting features for
  clustering
• Temporal compression
• Clustering for spatial data fusion and sensing
  strategy

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Spatial Strategies

• Divide the monitored area into regions of
  interest based on a Physical Phenomenon of
  Interest (PPI) parameter.
• PPI is used to form clusters
• The division is used as basis for spatial
  sampling and data fusion strategy

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Clustering Algorithm

• An algorithm inspired by Quorum sensing carried
  out by bacteria cells to determine when there is
  minimum concentration of a particular substance
  to carry out processes such as bioluminescence.
• Analogy
• Concentration of substance gt PPI
• Bacteria cell gt sensor nodes
• Process group gt clusters
• Self-organisation The network is divided into
  regions of interest without knowledge of the
  global states of nodes.

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Summary

• SECOAS aims to provide temporal and spatial
  oceanography data with an ad-hoc distributed
  network
• Complex system concept and biologically inspired
  algorithms are used to achieve self-organisation
  in the network
• Demonstrate the basic architecture of data
  handling
• Future direction WORK HARD!!
• Continue data analysis and modeling
• Develop spatial sampling and fusion strategy

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Thanks for the attention!

• Now QA