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Computational informatics

Thank you. CSIRO Computational Informatics Prof. Paulo de Souza OCE Science Leader t +61 3 6232 5578 e paulo.desouza@csiro.au w www.csiro.au / ict. Computational informatics. Swarm Sensing. Paulo de Souza | OCE Science Leader – CSIRO Computational Informatics. 26 November 2013.

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Computational informatics

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  1. Thank you CSIRO Computational Informatics Prof. Paulo de SouzaOCE Science Leader t +61 3 6232 5578 epaulo.desouza@csiro.au wwww.csiro.au/ict Computational informatics

  2. Swarm Sensing Paulo de Souza | OCE Science Leader – CSIRO Computational Informatics 26 November 2013 CSIRO Computational informatics

  3. Swarm Sensing | Prof. Paulo de Souza

  4. MotivationThe real need Swarm Sensing | Prof. Paulo de Souza

  5. Technology Roadmap Silverster(2011) Swarm Sensing | Prof. Paulo de Souza

  6. Technology Roadmap: Where are we? [Silverster11] Our Target by 2016 … Swarm Sensing | Prof. Paulo de Souza

  7. Technology Roadmap Swarm Sensing | Prof. Paulo de Souza

  8. Technology Roadmap Disruptive Applications Better IP Space Populated IP Space Too Blue Sky cm mm nm mm Mature Immature Technology Readiness Level Swarm Sensing | Prof. Paulo de Souza

  9. Our Target • To develop a 100 mm sensor platform that is able to: • Harvest and store energy • Process data and store it • Make environmental measurements • Communicate • Perform environmental monitoring and insect monitoring • Considering: • Cost ($0.30/unit) • Theoretical formalism to interpret data from these sensors Swarm Sensing | Prof. Paulo de Souza

  10. Swarm Sensing: Challenges we are facing Where we are focusing now, next and later? Swarm Sensing | Prof. Paulo de Souza

  11. Research Challenges Energy Harvesting (from insect movement) Storage (3D batteries) Integration Design, optimisation, prototyping, manufacturing, testing Communications Increasing distance Tracking insects Analytics Interpreting data coming from thousands of sensors in real-time Modelling insect behaviour Swarm Sensing | Prof. Paulo de Souza

  12. Swarm Sensing: Functions of micro-devices What can we do with it? Swarm Sensing | Prof. Paulo de Souza

  13. Functions of Micro-Devices Tagging Challenge: Distance achieved with wireless communication Tracking Challenge: landscape, size of supporting structure, energy, antenna Sensing Challenges: Communication, energy harvesting and storage Micro-devices | Page 13

  14. Swarm Sensing: What are we doing? Swarm Sensing | Prof. Paulo de Souza

  15. What are we doing? • Tagging 5,000 honey bees • 2.5 x 2.5 x 0.4 mm RFID manufactured by Hitachi Japan • Four identical hives • Feeder stations with different nutritional contents • Pollen excluders • Pesticides on pollen • Aiming at gathering information on: • Bee behaviour x environmental changes • Pre-swarming management • Pollination under stress • Real impact of pesticides • Insight to bee collapse • Interactions between individuals

  16. What are we doing?

  17. What are we doing?

  18. Swarm Sensing | Prof. Paulo de Souza

  19. What are we doing? • Theoretical Formalism • Statistical Mechanics • Thermodynamic-equivalent • States • Constants • Modelling/Simulation • How to integrate large data sets • What can we learn from data time latitude longitude

  20. Energy Harvesting Swarm Sensing | Prof. Paulo de Souza

  21. Energy Harvesting Swarm Sensing | Prof. Paulo de Souza

  22. 3D Microbatteries Maximised Energy on a Small Footprint Area Thin Film Battery 3D Microbattery ~15 µm ~500 µm Higher electrode surface area = Increased Energy per Footprint Area 3D Microbattery Project | Page 22

  23. Integration of Micro-Devices Antenna Energy Harvesting Micro-Devices 3-D Micro-Battery Micro-Sensors Micro-Electronics & RF Module Energy Harvesting Micro-Devices 3-D Micro-Battery Micro-Sensors Micro-Electronics & RF Module

  24. Swarm Sensing: What’s next? What are we doing ? Swarm Sensing | Prof. Paulo de Souza

  25. What’s next? • Tracking • Antenna • Harmonic Radar • Aiming • Migration • Dispersal of invasive species • Disease vector, pest and beneficial insect movement • Mining operations x insect behaviour

  26. Swarm Sensing: What’s later? What we dream of achieving? Swarm Sensing | Prof. Paulo de Souza

  27. Swarm Sensing | Prof. Paulo de Souza

  28. Swarm Sensing | Prof. Paulo de Souza

  29. Insects as Sensors 5 4 : 3 2 1 0 2 4 Female pheromone gland extract : : 5 9 2 1 4 2 2 5 3 : 9 5 : 1 : 4 4 6 2 : 2 2 2 2 FID EAD antenna reaction Fact 1: Insects are very sensitive to chemicals (reported 10-19) Fact 2: Exposure to some chemicals create a specific pattern through the insect nerve system Swarm Sensing | Prof. Paulo de Souza

  30. Insects as sensors? Swarm Sensing | Prof. Paulo de Souza

  31. Insects as Sensor Swarm Sensing | Prof. Paulo de Souza

  32. Swarm Sensing | Prof. Paulo de Souza

  33. Swarm Sensing: Partnerships We can’t make it happen alone! Swarm Sensing | Prof. Paulo de Souza

  34. Current Capability SSN-TCP (CCI, CPSE, CET) & CES UTAS Desert Research Institute University of Michigan Output Biosecurity Flagship Vale Institute of Technology Tasmanian Beekeepers Association Fruit Growers Tasmania Seed Producers Quarantine Tasmania EPA - TasGov Partnerships Swarm Sensing | Prof. Paulo de Souza

  35. Swarm Sensing: Conclusions What we have seen today? Swarm Sensing | Prof. Paulo de Souza

  36. To reflect • Technology Development Requires: • Discipline and strategic thinking; • A relevant application; • Capability in R&D (people, infrastructure and relationships); • Scientific relevance; • Resources. • CSIRO is the place to make it happen. • This is a team effort.

  37. Thank you CSIRO Computational Informatics Prof. Paulo de SouzaOCE Science Leader t +61 3 6232 5578 epaulo.desouza@csiro.au wwww.csiro.au/ict Computational informatics

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