Accuracy-Aware Aquatic Diffusion Process Profiling Using Robotic Sensor Networks

1. Accuracy-Aware Aquatic Diffusion Process Profiling Using Robotic Sensor Networks Yu Wang, Rui Tan, Guoliang Xing, Jianxun Wang, Xiaobo Tan Michigan State University

2. Harmful Diffusion Processes Unocal oil spill Santa Barbara, CA, 1969 http://en.wikipedia.org BP oil spill, Gulf of Mexico, 2010 http://en.wikipedia.org Chemicals/Waste Water Pollution UK, 2009, Reuters • Diffusion profiling • source location, concentration, diffusion speed • high accuracy, short delay • Physical uncertainties • temporal evolution, sensor biases, environmental noises 04/19/2012 IPSN'12, Beijing, China 2

3. Traditional Approaches Manual sampling labor intensive coarse spatiotemporal granularity Fixed buoyed sensors expensive, limited coverage, poor adaptability Mobile sensing via AUVs and sea gliders expensive (>$50K), bulky, heavy 04/19/2012 IPSN'12, Beijing, China 3

4. Aquatic Sensing via Robotic Fish On-board sensing, control, and wireless comm. Low manufacturing cost: ~$200-$500 Limited power supply and sensing capability Smart Microsystems Lab, MSU 04/19/2012 IPSN'12, Beijing, China 4

5. Problem Statement diffusion source robotic sensors • Maximize profiling accuracy w/ limited power supply • Collaborative sensing: source location, concentration, speed • Scheduling sensor movement to increase profiling accuracy 04/19/2012 IPSN'12, Beijing, China 5

6. Roadmap Motivation Background Profiling and Accuracy Modeling Movement Scheduling Trace Collection & Evaluation 04/19/2012 IPSN'12, Beijing, China 6

7. Diffusion Process Model Concentration at position (x,y,z) and time instance t Diffusion and water speed Diffusion profile (source loc, α, β) 04/19/2012 IPSN'12, Beijing, China 7

8. Sensor Measurement Model Sensor measurement Actual concentration distance to diffusion source elapsed time Sensor bias Random noise, 04/19/2012 IPSN'12, Beijing, China 8

9. Collaborative Diffusion Profiling Each sensor samples periodically Samples from different sensors are fused via Maximum Likelihood Estimation (MLE) How to model the accuracy of profiling? How does the accuracy metric guide the movement of sensors? 04/19/2012 IPSN'12, Beijing, China 9

10. Cramér-Rao Bound (CRB) Lower bound of estimate variance Highly non-linear expression e.g. row vectors of all sensor coordinates 04/19/2012 IPSN'12, Beijing, China 10

11. A New Accuracy Metric Sum of contributions of individual sensors fixed in each profiling iteration node i's contribution to overall profiling accuracy diffusion parameter distance b/w source and sensor i min distance to source 04/19/2012 IPSN'12, Beijing, China 11

12. Sensor Movement Scheduling Objective: find movement schedule for each sensor, s.t. profiling accuracy ω is maximized Constraint: Movement Schedule: {orientation, # of steps} number of steps for sensor i 04/19/2012 IPSN'12, Beijing, China 12

13. Assign orientation Find di* that maximizes If di > di*,toward estimated source, otherwise away from Allocate moving steps Maximize Σω(Δi), Δi – # of steps of sensor i Decomposition → dynamic programming Radial Scheduling Algorithm di* 04/19/2012 IPSN'12, Beijing, China 13

14. Putting All Together 1 • Collaborative profiling • Sampling • TX samples to node 2 • Profiling via MLE estimation •  Estimated source location • Movement scheduling • Orientation determination • DP-based step allocation 2 3 diffusion source robotic sensors

15. Evaluation Methodology Trace collection Rhodamine-B diffusion model On-water Zigbee communication GPS localization, robotic fish movement Trace-driven simulation Profiling accuracy, scalability etc. Implementation on TelosB motes Computation complexity 04/19/2012 IPSN'12, Beijing, China 15

16. Rhodamine-B Diffusion grayscale model verification • discharge Rhodamine-B in saline water • periodically capture diffusion with a camera • expansion of contour → diffusion evolution 04/19/2012 IPSN'12, Beijing, China 16

17. On-water ZigBee Communication • PRR measurement using ZigBee radios on Lake Lansing • 50% drop of comm. range compared to on land 04/19/2012 IPSN'12, Beijing, China 17

18. GPS and Movement Errors GPS localization errors groundtruth vs. GPS measurement average error is 2.29 m Robotic fish movement 3m×1m water tank tail beating frequency: 0.9 Hz, amplitude: 23o expected speed: 2.5 m/min Linx GPS module 04/19/2012 IPSN'12, Beijing, China 18

19. Trace-driven Simulations Profiling accuracy vs. elapsed time < SNR-based scheduling > orientation: gradient-ascent of SNR # of steps: proportion to SNR profiling accuracy improves as time elapses 04/19/2012 IPSN'12, Beijing, China 19

20. Time Complexity Implemented MLE estimation and scheduling algorithm on TeobsB motes 04/19/2012 IPSN'12, Beijing, China 20

21. Conclusions Collaborative diffusion profiling using robotic fish New accuracy profiling metric Movement scheduling algorithm Evaluation in trace-driven simulation & real implementation High accuracy & low overhead 04/19/2012 IPSN'12, Beijing, China 21

22. Trace-driven Simulations Profiling accuracy vs. number of sensors profiling accuracy improves as more sensors are deployed 04/19/2012 IPSN'12, Beijing, China 23