Transcript Communication Systems and Networks

Plume Source Position
Estimation Using
Sensor Networks
Michalis P. Michaelides &
Christos G. Panayiotou
Dept. of Electrical and Computer Engineering
University of Cyprus
E-NEXT WG1 Meeting
September 29, 2005
Motivation
Motivation
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Diversity and quantity of chemicals released into
the environment has risen dramatically in recent
years.
Legacy of land and groundwater contaminated
by human activities affects the ecosystem,
human health and quality of life.
Need for reliable, cost-effective monitoring of
contaminating compounds in water, soil and
sediments…
Motivation cont.
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One of the most dangerous terrorist attacks is
the release of toxic plumes.
Internationally this is a hot research topic
following the September 11, 2001 in New York
terrorist attacks.
A contaminant source can also occur as a result
of an accident at a ship or factory.
In both cases people and the proper authorities
need the necessary information within minutes
after the event to deal with the crisis…
Presentation Overview
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Introduction
Related work
Simulation model
Simulation results
Conclusion
Future Work
Plume propagation
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Once released at its source odor is carried by the wind to form a
plume.
As the plume travels further away it becomes on average more
dilute due to molecular diffusion.
Dominant cause of diffusion is turbulence.
Characteristics of odor plume depend on physical environment.
Output of odor plume
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Graphical interpretation of the output of an odor plume
with moderate turbulence.
Sensor was stationary at 10 cm downstream of the odor
source and at the geometric center of the plume.
Time averaged vs. Instantaneous
Plume
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Smooth
Time-invariant
Gaussian plume model
with peak near the
source.
Odor concentration
gradient along wind
direction is negligibly
small.
Time averaged gradient
points towards the source
only close to the source.
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Discontinuous
Time-varying
Detected a lot more often
than Gaussian model
predicts.
Instantaneous
concentrations available
at significant distances
from source.
Direction of
instantaneous gradient
does not always point to
odor source.
Related work in plume tracking using
unmanned vehicles
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Bio-mimetic robotic plume-tracing algorithms based on
olfactory sensing (Homing, Foraging, Mate seeking)
Basic steps in robotic plume-tracing
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Sensing the chemical and sensing or estimating fluid velocity.
Generating sequence of searcher speed and heading
commands such that the motion of the vehicle is likely to locate
the odor source.
J. Farrell et al. uses an autonomous vehicle operating in
the fluid flow capable of detecting above threshold
chemical concentration and sensing fluid flow velocity.
Chemical Plume Tracing by J. Farrell
Vehicles
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vs. Sensor Networks
Plume finding: Has to spend a
good amount of time searching for
the plume in reachable areas.
Plume maintaining: Has the
problem of maintaining contact
with the plume once found and
reacquiring contact in case it is
lost.
Can move closer to source for
better estimation until it finds
source location.
All necessary computation can be
done on-board.
Once source location is identified
it returns to base to report.
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Plume finding: If the plume is
within sensing radius of any of the
sensors it is immediately
discovered. (among people,
around buildings, obstacles…)
Plume maintaining: Contact with
the plume is maintained
throughout the sensor field- no
reacquisition necessary. (timeaveraging is possible)
Assuming static sensors the
position of the source needs to be
remotely estimated using fusion
techniques.
Energy constraints
Need efficient routing techniques
to relay the information hop by
hop to the sink.
Related work in sensor networks
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By summer 2005 Syracuse University researchers will have installed
a dozen robotic sensors to form the largest underwater monitoring
system in USA.
In Europe SENSPOL Thematic Network focused European
expertise on the problems associated with monitoring environmental
pollutants in water, soil and sediments.
Oak Ridge National Laboratory in USA are working to develop a
SensorNet that will serve as a national system for comprehensive
incident management that will rapidly respond to a chemical,
biological or radiological event.
Los Alamos National Laboratory are working in developing a DSN
(Distributed Sensor Network) that will detect a motor vehicle carrying
a RDD (Radiological Dispersion Device)
CSIP (Collaborative Signal Information Processing) deals with the
energy constrained dynamic sensor collaboration.
Sensor Network Plume Tracking
Contaminant
Source
Sensor nodes
Simulation model
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Measurement of sensor i at time t
Concentration at source
c
zi ,t    wi ,t
ri
ri 
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 xs  xi    ys  yi 
wi ,t  N  0, 
2
2
2
 i, t
Gaussian white noise
Radial distance of sensor i from the source
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N sensor nodes stationary,
randomly placed in a
rectangular field R with
locations known ( xi , yi ).
Contaminant source ( xs , ys ) is
somewhere inside R (1km x
1km).
Propagation of contaminant
transport is uniform in all
directions.
We assume additive Gaussian
white noise.
α=2, c=106 or simulation
results.
Least squares estimation
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N
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J  
i 1 
 x x
 s i
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2

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c
1
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 zi  , zi 

M
2
2 2
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 y s  yi 
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

 

M
z
t 1
it
Sensor nodes calculate the mean of M measurements and then
send the computed mean to the sink.
After the sink receives the information from all sensor nodes it
employs the nonlinear least squares method to compute an estimate
of the source location by minimizing function J.
Least squares start position
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LS max start – start the minimization in the
neighbourhood of the sensor node with the
highest measurement.
LS random start – randomly pick 10 start
positions in the sensor field.
LS combo – choose the method that minimizes
the squared 2-norm of the residual.
CPA – Closest point approach
 The
source position is the location of the sensor that
measured the highest concentration.
Simulation results
K
1
Error  
K k 1
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x
s ,k
 xs ,k
 y
2
s ,k
 ys ,k

2
MATLAB simulation package
100 randomly placed sources for each experiment
(K=100)
Effect of varying number of sensors, noise variance and
number of measurement samples.
Conclusions
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Our proposed sensor network estimates the plume
source location in a constrained sensor field assuming a
uniform propagation of the plume.
The proposed Nonlinear Least Squares optimization
achieves better estimation results than the CPA( Closest
Point Approach ).
Our results indicate that in situations of high noise
variance it is necessary to increase the number of
sensors or the number of measurements to achieve
satisfactory results.
Sensor Network Plume Tracking with
wind
Wind Direction
Contaminant
Source
Sensor nodes
New model
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Only a few of the sensors are able to detect the plume
based on wind direction and spread of the plume.
When a sensor node is triggered by the presence of the
plume it wakes-up, it takes a number of discrete
measurements and calculates the mean.
If the mean exceeds a predefined threshold T it
communicates this value to the sink and continues
measuring otherwise it goes back to sleep.
At the sink as before the nonlinear least squares
optimization is used to find the source position using all
available measurements.
1000
SN original field
SN selected for estimation
Plume source
900
800
700
Active Area
600
Threshold
500
400
w
s
300
200
100
0
Wind
0
100
200
300
400
500
600
700
800
900
1000
New estimator
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LSp Least squares estimator with initial
concentration known.
LSc Least squares estimator with initial
concentration unknown.
 Use
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separable least squares techniques
Further improvements:
 LSu
Unconstrained optimization
 LSw Constrained search based on wind direction
Threshold considerations…
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Determines the number of sensors involved in
the estimation.
Needs to be large enough to minimize
probability of false alarms.
Needs to be small enough to ensure maximum
detection probability.
Needs to be appropriately chosen to minimize
energy consumption while not compromising
estimation accuracy.
Future Work
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Propagation model
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Noise
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Other models, e.g., lognormal or Chi-Square
Estimation techniques
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Gaussian model, wind, turbulence
Maximum Likelihood, Bayesian estimators
Data fusion, aggregation
Multiple sources
Real-time implementation
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Berkeley modes test-bed