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COMPUTATIONAL INFORMATICS Thank you CSIRO Computational Informatics Prof. Paulo de Souza OCE Science Leader t+61 3 6232 5578 wwww.csiro.au/ict.

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Presentation on theme: "COMPUTATIONAL INFORMATICS Thank you CSIRO Computational Informatics Prof. Paulo de Souza OCE Science Leader t+61 3 6232 5578 wwww.csiro.au/ict."— Presentation transcript:

1 COMPUTATIONAL INFORMATICS Thank you CSIRO Computational Informatics Prof. Paulo de Souza OCE Science Leader t wwww.csiro.au/ict

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

3 Swarm Sensing | Prof. Paulo de Souza

4 Motivation The real need

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

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

7 Technology Roadmap Swarm Sensing | Prof. Paulo de Souza

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

9 Our Target To develop a 100  m 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

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?

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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 longitude latitude time

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

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

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

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29 Insects as Sensors Fact 1: Insects are very sensitive to chemicals (reported ) Fact 2: Exposure to some chemicals create a specific pattern through the insect nerve system 1 9 : : : : : : : 4 9 EAD FID antenna reaction Female pheromone gland extract

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

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

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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 COMPUTATIONAL INFORMATICS Thank you CSIRO Computational Informatics Prof. Paulo de Souza OCE Science Leader t wwww.csiro.au/ict


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