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Self-Organisation in SECOAS Sensor Network. UCL SECOAS team: Dr. Lionel Sacks, Dr. Matt Britton Toks Adebutu, Aghileh Marbini, Venus Shum, Ibiso Wokoma Presented by Venus Shum Advance Communications and Systems Engineering group University College London Supervisor: Dr. Lionel Sacks. Content.

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self organisation in secoas sensor network

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

content
Content
  • The SECOAS sensor network
  • SECOAS architecture
  • Distributed Algorithms Overview
  • Data Handling in SECOAS
secoas project
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
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
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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functional planes
Functional Planes
  • Spatial Coordination of nodes forming
    • Location plane
    • Clustering plane
  • Data Fusion plane
  • Adaptive sampling plane

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characteristics of das
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
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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parameter sharing among neighbours
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

=> gossip!

  • 2 characteristics of gossiping
    • Selective/random targets
    • Don’t always pass information that is exactly the same! (Add salt and vinegar)

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gossiping protocol in secoas
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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before data handling there is
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.

=> 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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data handling process
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
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
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 => PPI
    • Bacteria cell => sensor nodes
    • Process group => clusters
  • Self-organisation – The network is divided into regions of interest without knowledge of the global states of nodes.

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summary
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