1. 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

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

3. The SECOAS Sensor Network

4. SECOAS project SECOAS – Self-Organised Collegiated Sensor Network Project SECOAS – Self-Organised Collegiated Sensor Network Project Aim: To collect oceanographic data with good temporal and spatial resolution Aim: To collect oceanographic data with good temporal and spatial resolution Why SECOAS? Why SECOAS? Traditionally done by 1 (or a few) expensive high- precision sensor nodes Traditionally done by 1 (or a few) expensive high- precision sensor nodes Lack of spatial resolution Lack of spatial resolution Data obtained upon recovery of sensor nodes Data obtained upon recovery of sensor nodes Data gathered in burst – may miss important features. Data gathered in burst – may miss important features. 1234

5. Solution Use of sensor ad-hoc network Use of sensor ad-hoc network large number of Lower-cost, disposable sensors (tens to thousands, maybe more). large number of Lower-cost, disposable sensors (tens to thousands, maybe more). provide temporal as well as spatial resolution provide temporal as well as spatial resolution wireless communication - data are dispatched to the base station to the users in regular intervals 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 ad-hoc nature – easily adopt to addition and removal of nodes Other Characteristics: Other Characteristics: distributed distributed low processing power low processing power stringent battery requirement stringent battery requirement communication constraint communication constraint 1234

6. Specialties Distributed system and distributed algorithms. Distributed system and distributed algorithms. Use of complex system concept when designing algorithms – simple rules lead to desirable global behaviour Use of complex system concept when designing algorithms – simple rules lead to desirable global behaviour Biologically-inspired algorithms Biologically-inspired algorithms A custom-made kind-of OS (kOS) tailor for implementation of Distributed algorithms A custom-made kind-of OS (kOS) tailor for implementation of Distributed algorithms 1234

7. SECOAS Architecture

8. Physical Structure of a sensor node 2134

9. Functional Planes Spatial Coordination of nodes forming Spatial Coordination of nodes forming Location plane Clustering plane Data Fusion plane Data Fusion plane Adaptive sampling plane Adaptive sampling plane 2134

10. Distributed Algorithms Overview

11. Characteristics of DAs Easy addition, alteration and removal of functionality (just plug them together!) Easy addition, alteration and removal of functionality (just plug them together!) Self-organising, self-managing and self- optimising Self-organising, self-managing and self- optimising No knowledge of a global state No knowledge of a global state A stateless machine is good for easy implementation A stateless machine is good for easy implementation Required interfaces for algorithms to talk to each other Required interfaces for algorithms to talk to each other 3124

12. kOS – the supporting platform Kind-of operating system Kind-of operating system Individual algorithms responsible for scheduling their actions Individual algorithms responsible for scheduling their actions Virtualisation of algorithms – Virtualisation of algorithms – software can use kOS functions disregarding their physical location software can use kOS functions disregarding their physical location Interfaces to other physical boards are provided Interfaces to other physical boards are provided Easy exchange of parameters between algorithms Easy exchange of parameters between algorithms Adaptive scheduling to distribute resources according to environment Adaptive scheduling to distribute resources according to environment 3124

13. Interaction of algorithms 3124

14. Parameter sharing among neighbours Enable exchange of information between nodes Enable exchange of information between nodes An interesting facts of UCL SECOAS team: An interesting facts of UCL SECOAS team: Consist of 4 (pretty) women and 1 guy Consist of 4 (pretty) women and 1 guy => gossip! 2 characteristics of gossiping 2 characteristics of gossiping Selective/random targets Selective/random targets Don’t always pass information that is exactly the same! (Add salt and vinegar) Don’t always pass information that is exactly the same! (Add salt and vinegar) 3124

15. Gossiping protocol in SECOAS Type 1: Passing the exact parameters to randomly selected nodes Type 1: Passing the exact parameters to randomly selected nodes Type 2: Passing altered parameters to all neighbour nodes Type 2: Passing altered parameters to all neighbour nodes Efficient protocol and avoid flooding Efficient protocol and avoid flooding Low latency requirement and network has weak consistency Low latency requirement and network has weak consistency 3124

16. Data Handling in SECOAS

17. Before data handling, there is Data analysis first Data analysis first To get a first hand knowledge of the data dealt with To get a first hand knowledge of the data dealt with important on engineering solution important on engineering solution Trend, periods, correlation, self-similarity, heavy tail, etc. Trend, periods, correlation, self-similarity, heavy tail, etc. => modelling Test data from Wavenet project. Test data from Wavenet project. Consists of 3 months burst data from April-June 03 Consists of 3 months burst data from April-June 03 Temperature, pressure, conductivity and sediment Temperature, pressure, conductivity and sediment 4123

18. Basic Analysis 4123

19. Extraction of anomalies using wavelet 4123

20. Data Handling process Temporal extract interesting features for clustering Temporal extract interesting features for clustering Temporal compression Temporal compression Clustering for spatial data fusion and sensing strategy Clustering for spatial data fusion and sensing strategy 4123

21. Spatial Strategies Divide the monitored area into regions of interest based on a Physical Phenomenon of Interest (PPI) parameter. Divide the monitored area into regions of interest based on a Physical Phenomenon of Interest (PPI) parameter. PPI is used to form clusters PPI is used to form clusters The division is used as basis for spatial sampling and data fusion strategy The division is used as basis for spatial sampling and data fusion strategy 4123

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. 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 Analogy Concentration of substance => PPI Concentration of substance => PPI Bacteria cell => sensor nodes Bacteria cell => sensor nodes Process group => clusters Process group => clusters Self-organisation – The network is divided into regions of interest without knowledge of the global states of nodes. Self-organisation – The network is divided into regions of interest without knowledge of the global states of nodes. 4123

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

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