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

Diffusion profiling source location, concentration, diffusion speed high accuracy, short delay Physical uncertainties –temporal evolution, sensor biases, environmental noises 04/19/2012IPSN'12, Beijing, China2 Harmful Diffusion Processes Unocal oil spill Santa Barbara, CA, BP oil spill, Gulf of Mexico, Chemicals/Waste Water Pollution UK, 2009, Reuters

04/19/2012 IPSN'12, Beijing, China3 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

04/19/2012IPSN'12, Beijing, China4 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

04/19/2012IPSN'12, Beijing, China5 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

04/19/2012IPSN'12, Beijing, China6 Roadmap Motivation Background Profiling and Accuracy Modeling Movement Scheduling Trace Collection & Evaluation

04/19/2012IPSN'12, Beijing, China7 Diffusion Process Model Concentration at position (x,y,z) and time instance t Diffusion and water speed Diffusion profile (source loc, α, β)

04/19/2012IPSN'12, Beijing, China8 Sensor Measurement Model Sensor measurement Actual concentration –distance to diffusion source –elapsed time Sensor bias Random noise,

04/19/2012IPSN'12, Beijing, China9 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?

04/19/2012IPSN'12, Beijing, China10 Cramér-Rao Bound (CRB) Lower bound of estimate variance Highly non-linear expression e.g. row vectors of all sensor coordinates

04/19/2012IPSN'12, Beijing, China11 A New Accuracy Metric Sum of contributions of individual sensors fixed in each profiling iteration node i 's contribution to overall profiling accuracy distance b/w source and sensor i min distance to source diffusion parameter

04/19/2012IPSN'12, Beijing, China12 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

Assign orientation –Find d i * that maximizes –If d i > d i *, toward estimated source, otherwise away from Allocate moving steps –Maximize Σ ω(Δ i ), Δ i – # of steps of sensor i –Decomposition → dynamic programming 04/19/2012IPSN'12, Beijing, China13 Radial Scheduling Algorithm di*di*

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

04/19/2012IPSN'12, Beijing, China15 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

04/19/2012IPSN'12, Beijing, China16 Rhodamine-B Diffusion  discharge Rhodamine-B in saline water  periodically capture diffusion with a camera  expansion of contour → diffusion evolution grayscale model verification

04/19/2012IPSN'12, Beijing, China17 On-water ZigBee Communication PRR measurement using ZigBee radios on Lake Lansing 50% drop of comm. range compared to on land

04/19/2012IPSN'12, Beijing, China18 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: 23 o expected speed: 2.5 m/min Linx GPS module

04/19/2012IPSN'12, Beijing, China19 Trace-driven Simulations Profiling accuracy vs. elapsed time profiling accuracy improves as time elapses orientation: gradient-ascent of SNR # of steps: proportion to SNR

04/19/2012IPSN'12, Beijing, China20 Time Complexity Implemented MLE estimation and scheduling algorithm on TeobsB motes

04/19/2012IPSN'12, Beijing, China21 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

04/19/2012IPSN'12, Beijing, China22 Approach Overview H H diffusion source robotic sensors cluster head concentration measurements MLE-based diffusion profiling Movement scheduling to decrease CRB dmdm

04/19/2012IPSN'12, Beijing, China23 Trace-driven Simulations Profiling accuracy vs. number of sensors profiling accuracy improves as more sensors are deployed