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

3. The SECOAS Sensor Network

4. SECOAS project SECOAS – Self-Organised Collegiate Sensor Network Project SECOAS – Self-Organised Collegiate 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 Equipment needs to be recovered at the end of the data gathering exercise Equipment needs to be recovered at the end of the data gathering exercise Burst data - May miss interesting features Burst data - May miss interesting features 1234

5. The sensor network approach A distributed system/ network A distributed system/ network Characteristics: Characteristics: Large number Large number Low cost Low cost Low processing power Low processing power Advantages Advantages Provide temporal and spatial resolution Provide temporal and spatial resolution Data dispatched to the scientist in regular interval Data dispatched to the scientist in regular interval Wireless ad hoc network Wireless ad hoc network Stringent battery requirement Stringent battery requirement communication constraint communication constraint 1234

6. SECOAS Specialties Distributed Algorithms Distributed Algorithms A clustering approach for data handling A clustering approach for data handling 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. Node architecture 1234

8. Network scenario 1234

9. 1234

10. Objectives and approaches of data handling

11. A simplified scenario All nodes sample All nodes sample 1. Sampling 2. Temporal compression 3. Data route back to base station 4. Spatial compression when possible Not optimal because Not optimal because Data Redundancy Power usage for sampling and comm. 2134

12. A clustering approach A clustering approach for spatial data handling A clustering approach for spatial data handling the monitored area is partitioned into interesting groups the monitored area is partitioned into interesting groups strategies are carried out based on the cluster formations. strategies are carried out based on the cluster formations. Clustering Requirements specific to SECOAS Clustering Requirements specific to SECOAS Scalable Scalable Dynamic and adaptive Dynamic and adaptive Simple Simple Distributed, not rely on underlying network architecture Distributed, not rely on underlying network architecture Robust Robust 2134

13. Resources analysis Resources Resources Battery power + Processing power Bandwidth Memory Data resolution is a goal Data resolution is a goal Abstract concept set by user Related to the environment 2134

14. A resource scenario Data fusion save power, memory and bandwidth Data fusion save power, memory and bandwidth Radio: processing = 20:1 in the first trial Increase sampling nodes = increase resolution Increase sampling nodes = increase resolution Final results feedback to algorithms Final results feedback to algorithms 2134

15. Parameter space The parameters set  (physical phenomena of interest PPI) used for clustering The parameters set  (physical phenomena of interest PPI) used for clustering Need to find out what characterise the measurement – data analysis Need to find out what characterise the measurement – data analysis Pressure, salinity, temperature, sediment, tilts Pressure, salinity, temperature, sediment, tilts The Mean, does not mean a lot in most cases The Mean, does not mean a lot in most cases 2134

16. Spatial Algorithms

17. Information Flow 3124

18. Auto-location algorithm Iterative averaging Iterative averaging Position aware nodes (PA) and position determining nodes (PD) Position aware nodes (PA) and position determining nodes (PD) Position propagates from PAs to PDs. PDs use averaging to estimate position iteratively. Position propagates from PAs to PDs. PDs use averaging to estimate position iteratively. Simple, distributed and self- organised Simple, distributed and self- organised CoordinatesActions Originate node (coordinate)  destination nodes ABCDE 0---8 A(0)  B E(8)  D 00-88 B(0)  C & A D(8)  C & E 00488 C(4)  B & D 02468 B(2)  A& C D(4)  C & E 3124

19. Results - Auto-location 3124

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. 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 Processed group => clusters Processed group => clusters The range of the grouping is determined by LALI used by e.g. ant The range of the grouping is determined by LALI used by e.g. ant cemetery construction LALI (local activation lateral inhibition) LALI (local activation lateral inhibition) 3124

21. Results - clustering Only local/ neighbour information is required for forming clusters. Only local/ neighbour information is required for forming clusters. Independent of topology Independent of topology Dynamic and scalable Dynamic and scalable 3124

22. Supporting platform and message exchange

23. kOS – kind-of operating system Full support of distributed algorithms Full support of distributed algorithms 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 environmental condition Adaptive scheduling to distribute resources according to environmental condition 4123

24. Interaction of algorithms within a node 4123

25. 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) 4123

26. Gossip protocol in SECOAS Type 1: Passing the exact parameters to randomly selected nodes (multi-hop) 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) Type 2: Passing altered parameters to all neighbour nodes (also, one hop only) 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 4123

27. Finally…

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

29. Thanks for the attention! Now Q&A