accuracy-aware aquatic diffusion process profiling using robotic sensor networks yu wang, rui tan,...
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Accuracy-Aware Aquatic Accuracy-Aware Aquatic Diffusion Process Diffusion Process
Profiling Using Robotic Profiling Using Robotic Sensor NetworksSensor 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
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Harmful Diffusion ProcessesHarmful Diffusion Processes
Unocal oil spillSanta Barbara, CA, 1969http://en.wikipedia.org
BP oil spill,Gulf of Mexico, 2010
http://en.wikipedia.org
Chemicals/Waste Water PollutionUK, 2009, Reuters
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Traditional ApproachesTraditional 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
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Aquatic Sensing via Robotic Aquatic Sensing via Robotic FishFish
• On-board sensing, control, and wireless comm.
• Low manufacturing cost: ~$200-$500
• Limited power supply and sensing capability
Smart Microsystems Lab, MSU
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Problem StatementProblem 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
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RoadmapRoadmap
• Motivation
• Background
• Profiling and Accuracy Modeling
• Movement Scheduling
• Trace Collection & Evaluation
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Diffusion Process ModelDiffusion Process Model
• Concentration at position (x,y,z) and time instance t
• Diffusion and water speed• Diffusion profile (source loc, α, β)
)exp(),( 2dtdc
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Sensor Measurement ModelSensor Measurement Model
• Sensor measurement• Actual concentration
– distance to diffusion source– elapsed time
• Sensor bias• Random noise,
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Collaborative Diffusion Collaborative Diffusion Profiling 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?
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Cramér-Rao Bound (CRB)Cramér-Rao Bound (CRB)
• Lower bound of estimate variance• Highly non-linear expression
e.g.
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row vectors of all sensor coordinates
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A New Accuracy MetricA New Accuracy Metric
• Sum of contributions of individual sensors
fixed in each profiling iteration node i's contribution tooverall profiling accuracy
),,( minddf ii distance b/w source
and sensor i min distance
to source
diffusion parameter
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Sensor Movement SchedulingSensor Movement Scheduling
Objective: find movement schedule for each sensor, s.t. profiling accuracy ω is maximized
Constraint:
• Movement Schedule: {orientation, # of steps}
MmN
ii
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number of steps for sensor i
• 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
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Radial Scheduling AlgorithmRadial Scheduling Algorithm
di*
),,( minddf ii
diffusion source
robotic sensors
Putting All TogetherPutting All Together
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• Collaborative profiling• Sampling• TX samples to node 2• Profiling via MLE estimation Estimated source location
• Movement scheduling• Orientation determination • DP-based step allocation
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Evaluation MethodologyEvaluation 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
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Rhodamine-B DiffusionRhodamine-B Diffusion
discharge Rhodamine-B in saline water periodically capture diffusion with a camera expansion of contour → diffusion evolution
grayscale
model verification
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On-water ZigBee On-water ZigBee CommunicationCommunication
• PRR measurement using ZigBee radios on Lake Lansing
• 50% drop of comm. range compared to on land
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GPS and Movement ErrorsGPS 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
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Trace-driven SimulationsTrace-driven Simulations
• Profiling accuracy vs. elapsed time
profiling accuracy improves as time elapses
< SNR-based scheduling >orientation: gradient-ascent of
SNR# of steps: proportion to SNR
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Time ComplexityTime Complexity
• Implemented MLE estimation and scheduling algorithm on TeobsB motes
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ConclusionsConclusions
• Collaborative diffusion profiling using robotic fish– New accuracy profiling metric– Movement scheduling algorithm
• Evaluation in trace-driven simulation & real implementation
– High accuracy & low overhead
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Trace-driven SimulationsTrace-driven Simulations
• Profiling accuracy vs. number of sensors
profiling accuracy improves as more sensors are deployed