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Self-Organisation in SECOAS Sensor Network

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

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  1. 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

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

  3. The SECOAS Sensor Network

  4. 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. 1 2 3 4

  5. 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 1 2 3 4

  6. 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 1 2 3 4

  7. SECOAS Architecture

  8. Physical Structure of a sensor node 1 2 3 4

  9. Functional Planes • Spatial Coordination of nodes forming • Location plane • Clustering plane • Data Fusion plane • Adaptive sampling plane 1 2 3 4

  10. Distributed Algorithms Overview

  11. 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 1 2 3 4

  12. 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 1 2 3 4

  13. Interaction of algorithms 1 2 3 4

  14. 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) 1 2 3 4

  15. 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 1 2 3 4

  16. Data Handling in SECOAS

  17. 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 1 2 3 4

  18. Basic Analysis 1 2 3 4

  19. Extraction of anomalies using wavelet 1 2 3 4

  20. Data Handling process • Temporal extract interesting features for clustering • Temporal compression • Clustering for spatial data fusion and sensing strategy 1 2 3 4

  21. 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 1 2 3 4

  22. 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. 1 2 3 4

  23. 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

  24. Thanks for the attention! Now Q&A

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