1. Sensor Networks for Planktonic Ecosystem Research: Goals, Approaches and Challenges Gaurav Sukhatme 1 & David Caron 2 1 Department of Computer Science and 2 Department of Biological Sciences University of Southern California

2. Outline Overarching questions in plankton research What can we do now (sensing/sampling)? Small scale Large scale New approaches a la CENS Sensor coordination Mobile nodes (sensors and boats) Modes of navigation Sensor-based actuation Coordination between static & mobile nodes Some results (model system in the lab) Future directions

3. Fearless Leader, aka Ari Requicha (a major node in the USC underwater sensor network program) Acknowledgements Deborah Estrin (UCLA) Chongwu Zhou (USC) Carl Oberg Beth Stauffer Amit Dhariwal Eric Shieh Bin Zhang

4. Fundamental biological questions (driving forces behind the work) What are the spatial/temporal distributions in nature of microorganisms of interest to ecosystem or human health? What factors (environmental or otherwise) explain the distributions of these microorganisms? What factors (chemical, physical, biological) lead to the exclusion of some microbial species and the success of others in natural assemblages? How can we most effectively and efficiently address these issues? Not simply a question of ‘more data’ (although that also helps). CENS approach will provide the ability to characterize the chemical, physical, biological community on temporal and spatial scales that are pertinent to the organisms.

5. 3 meter discus buoy (Autonomous Aerosol Sampler; Sholkovitz et al., WHOI Typical Scales of Ocean Sensing/Sampling CTD rosette (Ross Sea, 1999)

6. What are the problems with these approaches? Scale: Six orders of magnitude between scales of observation and scales of interaction (meters - micrometers) Spatiotemporal Coverage: “One ocean, one instrument” approach (workable sea state, instrument cost) Instrument Capability (biosensor limitation): Chlorophyll (phytoplankton only; not specific)

7. Depth (m) 15 10 5 0 12:00 00:00 12:00 00:00 12:00 Relative Scattering Strength Improvements in temporal/spatial characterization (Small Scale; an example using acoustics) 19941996 1998 From: Critical Scales and Thin Layers Website (http://www.gso.uri.edu/criticalscales/index.html)

8. East Sound, WA (Donaghay) (http://www.gso.uri.edu/criticalscales/about/index.html) Small and microscale distributions of aquatic microorganisms. What factor act to establish these patterns? What do the patterns mean?

9. How to characterize and sample features of biological interest in aquatic ecosystems? Improvements in temporal/spatial characterization Large (ish) Scale

10. Domoic acid concentrations (ng/liter) in the LA harbor and San Pedro Channel in May, 2002 Without networked sensors, the result is often lot’s of sampling, very little information. Amnesic shellfish poisoning in Southern California

11. What we do now (at best). Sampling at a subset of fixed nodes based on sensing-based decision

12. What we want to do. Aggregation of sampling nodes at feature(s) of interest based on sensing- directed movement

13. Marine Microorganism Monitoring Subsystems

14. Research Implications High spatial density (cm-mm), small sensors (cm-nm) of small size and limited capability Sensor-coordinated actuation and mobility, e.g., to deploy sensors and sample collectors where and when they are needed –Data processing inside the network, e.g., to trigger sensing and actuation Rapid microorganism identification in an aquatic environment  New sensing techniques

15. Adaptive Sampling How to aggregate sensors where they are needed Within CENS there are several parallel strategies being pursued: –Statistically rigorous adaptive sampling (NIMS) –Event-aware (triggered) sampling (NIMS) –Bacterial motion-inspired swarming

16. Bacterial Motion Bacteria move by interspersing propulsion with random turns (tumbles) Taxis is achieved by varying the duration of propulsion If during propulsion, a positive gradient is sensed then the propulsion event lasts a little longer before the next inevitable tumble This is in effect, a biased random walk

17. Controlling Robots

18. Results

19. The Experimental Setup A tethered system of small robots with radios (limited range) A small number of mobile underwater robots Initially focus on temperature measurements Collect water samples for offline analysis

20. Initial Experiment Create a thermocline in the tank Place sensors so as to find the thermocline accurately and with a small part of the total network Acquire water samples at strategically-located points Determine microorganism content (offline) Analyze the data to correlate algal behavior with thermocline.

21. Distributed Binary Search The search space is 1D, and is divided into regions Each node explores one region with binary search Each node tries to persuade others that the thermocline is within its search region A process of data aggregation is enacted on the route from each node to the edge node (the one at the top of the tank)

22. Binary Search At initialization, each node n i collects following data –P t : upper-most point of its search space –p b: lower-most point of its search space –t t : the temperature reading at p t –t b : the temperature reading at p b At each step – n i moves to depth p = (p t +p b )/2, and gets new temperature reading t –If |t t - t| > |t b - t|, replace t b, p b with t, p –If |t t - t| < |t b - t|, replace t t, p t with t, p This process is repeated until |t t - t| > |t b - t| or preset resolution is achieved

23. Data Aggregation Stage1: Build Routing Tree This process is initialized by the initialization message from the user Any node receiving the initialization message is the root of the routing tree Root broadcasts message BUILD-ROUTING- TREE Once receiving the message –node B checks its distance from the sender A –If A is within reliable communication range and B does not have a parent, B sets the sender as its parent –B broadcasts the same message BUILD-ROUTING- TREE to build the sub tree rooted at B

24. Data Aggregation Stage 2: Combine Estimates On receiving a query, a node –executes one step of binary search –forwards the query to its children –(if no children) sends its estimate to its parent On receiving message from children, each node –Discards all below threshold estimates –Sends others to its own parent Nodes whose estimates are discarded become inactive, and they ignore queries from parent Users receive one estimate with each query Every successive estimate has better resolution than the previous one

25. Simulated Thermocline Localization Left: Simulated temperature profile Right: Error histogram

26. Thermocline Localization

27. Improving Energy Efficiency with a Data Mule Motivation: –After first several steps, most nodes become inactive –However, many of them have to be awake to forward the messages from the active nodes to root Solution: –Create shortcuts from active nodes to root with a robotic submarine Assumption: –Submarine can be recharged

28. Energy Saving With Data Mule No data mule usedOne data mule used N send N received N send N received Node1989.338 Node248811.67 Node314233.678.33 Node428261816.67 Data MuleNA 811.33 Base station1012911.33 AllN/A12NA12.33 Number of messages exchanged in experiments

29. Biosensors: Correlatiing small- scale spatial distributions with chemical and physical structure Sensor network: Defining small-scale physical structure Pattern-Triggered Data Collection

30. T=22 day T=21 day T=18 day T=15 day T=13 day T=1 day T=3 day T=7 day T=27 day Addition of BT grazer, Pedinella thermocline Temperature profile and growth of Brown Tide alga with depth in column Brown Tide Cells/ml Temperature (˚C) Depth (cm) 0 50 100 150 200 0 5 10 15 20 25 30 0 2x10 6 4x10 6 6x10 6 T=29 day

31. Summary/Conclusions Lab-based experimental application of sensor- actuated microbial sampling. Movement from lab-based ground-truthing into the real world. –Small-scale coordinated sensor nodes (mobile & stationary working together) –Multiple mobility modes ‘True’ biosensor development