A Cluster-based Approach for Data Handling in Self-organising Sensor Networks

1. A Cluster-based Approach for Data Handling in Self-organising Sensor Networks 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 • Objectives and approaches of data handling • Spatial algorithms • Supporting platform and message exchange

3. The SECOAS Sensor Network

4. SECOAS project • SECOAS – Self-Organised Collegiate 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 • Equipment needs to be recovered at the end of the data gathering exercise • Burst data - May miss interesting features 1 2 3 4

5. The sensor network approach • A distributed system/ network • Characteristics: • Large number • Low cost • Low processing power • Advantages • Provide temporal and spatial resolution • Data dispatched to the scientist in regular interval • Wireless ad hoc network • Stringent battery requirement • communication constraint 1 2 3 4

6. SECOAS Specialties • Distributed Algorithms • A clustering approach for data handling • Biologically-inspired algorithms • A custom-made kind-of OS (kOS) tailor for implementation of Distributed algorithms 1 2 3 4

7. Node architecture 1 2 3 4

8. Network scenario 1 2 3 4

9. Network scenario 1 2 3 4

10. Objectives and approaches of data handling

11. A simplified scenario • All nodes sample • Sampling • Temporal compression • Data route back to base station • Spatial compression when possible • Not optimal because • Data Redundancy • Power usage for sampling and comm. 1 2 3 4

12. A clustering approach • A clustering approach for spatial data handling • the monitored area is partitioned into interesting groups • strategies are carried out based on the cluster formations. • Clustering Requirements specific to SECOAS • Scalable • Dynamic and adaptive • Simple • Distributed, not rely on underlying network architecture • Robust 1 2 3 4

13. Resources analysis • Resources • Battery power + Processing power • Bandwidth • Memory • Data resolution is a goal • Abstract concept • set by user • Related to the environment 1 2 3 4

14. A resource scenario • Data fusion save power, memory and bandwidth • Radio: processing = 20:1 in the first trial • Increase sampling nodes = increase resolution • Final results feedback to algorithms 1 2 3 4

15. Parameter space • The parameters set  (physical phenomena of interest PPI) used for clustering • Need to find out what characterise the measurement – data analysis • Pressure, salinity, temperature, sediment, tilts • The Mean, does not mean a lot in most cases These all have the same mean! 1 2 3 4

16. Spatial Algorithms

17. Information Flow 1 2 3 4

18. Auto-location algorithm • Iterative averaging • Position aware nodes (PA) and position determining nodes (PD) • Position propagates from PAs to PDs. PDs use averaging to estimate position iteratively. • Simple, distributed and self-organised 1 2 3 4

19. Results - Auto-location 1 2 3 4

20. 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 • Processed group => clusters • The range of the grouping is determined by LALI used by e.g. ant cemetery construction • LALI (local activation lateral inhibition) 1 2 3 4

21. Results - clustering • Only local/ neighbour information is required for forming clusters. • Independent of topology • Dynamic and scalable 1 2 3 4

22. Supporting platform and message exchange

23. kOS – kind-of operating system • Full support of distributed algorithms • 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 environmental condition 1 2 3 4

24. Interaction of algorithms within a node 1 2 3 4

25. 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

26. Gossip protocol in SECOAS • Type 1: Passing the exact parameters to randomly selected nodes (multi-hop) • Type 2: Passing altered parameters to all neighbour nodes (also, one hop only) • Efficient protocol and avoid flooding • Low latency requirement and network has weak consistency 1 2 3 4

27. Finally…

28. Conclusion and Future work • SECOAS data handling uses cluster-based approach • Next step: • Find the suitable parameters (PPI) from data analysis • Investigate how they work with the clustering algorithm • Auto-location optimises using number of position aware nodes, signal strength, etc. • Investigate temporal compression and spatial fusion strategy

29. Thanks for the attention! Now Q&A