DTIC AD1014864: High-Density Distributed Sensing for Chemical and Biological Defense

2.6 MB·English

by  Defense Technical Information Center

High-Density Distributed
Sensing for Chemical and
Biological Defense
Adam Norige, Jason Thornton, Curran Schiefelbein, and Christina Rudzinski
A new sensing architecture is being developed » As the nation faces a growing concern
regarding asymmetrical threats, such as
for plume-based chemical and biological (CB)
terrorist attacks using chemical and bio-
threat detection, mapping, and prediction. This
logical (CB) weapons, the diversity of envi-
effort seeks to provide a novel, affordable, and ronments requiring protection is on the rise. CB sensor
systems, once reserved for military battlefield deploy-
effective solution to spatially distributed CB
ments, are now appearing in civilian structures such as
detection by working in four key areas. These
public transportation systems and office buildings [1].
four areas include the analysis of performance This expanding range of sensing environments and oper-
gains offered by distributed sensing; the ating conditions is forcing the requirements for protec-
tive sensor systems to evolve. Not only do future detection
development of detection algorithms; the
systems have to satisfy traditional requirements such as
construction of an inexpensive sensor prototype;
sensitivity, response time, probability of detection, and
and experimentation with inexpensive, low-power false-alarm rates, but they must also satisfy other con-
straining factors such as cost, power consumption, and
deployment infrastructures. This multipronged
maintainability [2]. Ultimately, operators seek low-cost
approach to inexpensive spatially distributed
detection systems with flexible deployment capabilities
sensing has led to the creation of a robust CB that do not sacrifice overall detection performance.
sensing architecture that offers many significant To address these evolving CB detection requirements,
Lincoln Laboratory is developing a CB sensing frame-
advantages over traditional approaches.
work, consisting of high-density networks of inexpen-
sive point sensors. Not only does this sensing framework
address the evolving detection requirements, but it also
has the potential to provide many advanced capabilities,
such as threat tracking, mapping, and prediction, which
are not readily feasible with many traditional frameworks.
Throughout our development efforts, we viewed high-
density sensor networks as an interdependent system of
sensors, network detection algorithms, and deployment
infrastructure technologies. Viewing this new frame-
work as a system of technologies is critical because the
key technical challenges exist within the system’s inter-
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high-deNSiTy diSTRibuTed SeNSiNg foR ChemiCAl ANd biologiCAl defeNSe
CB point
release In alarm
High-performance
Wind sensor
Higher
concentration
CB line In alarm
release
High-performance
sensor
Low
concentration
Low-cost sensor
Low-cost sensor
figuRe 1. Line releases usually consist of low concen- figuRe 2. In a point-release scenario, high-density
trations of material distributed across a wide area, thus deployments of low-cost sensors have a higher probability
making sparse deployments of high-fidelity sensors bet- of encountering the chemical-biological (CB) plume than
ter suited for detection. the single high-fidelity sensor.
dependencies. For example, as more sensors are added concentration of threat material either as a burst or as a
to the network, the need arises to establish agile deploy- continuous release. Point releases generally target spe-
ment mechanisms such as wireless communication links. cific areas, but can affect large areas, depending on the
Dealing with wireless communications, especially in the atmospheric conditions. Unlike with line releases, sparse
low-power domain, introduces bandwidth constraints on sensor deployments are not well suited for detecting CB
data transfers within the network, thereby restricting the threats from point releases because individual CB threat
amount of data available to the network detection algo- plumes from point sources consist of distinct unmixed
rithms and ultimately influencing the overall detection regions of both high and low concentrations [3]. Indi-
performance. To maintain a systems-based approach, vidual plumes do not have Gaussian concentration dis-
our development efforts focused on four key areas: the tributions or smooth gradients between regions of high
performance analysis of high-density sensor networks, and low concentrations, which are seen in the average of
the development of inexpensive sensor technologies, the many plumes or in plume ensembles. As shown in Fig-
development of network detection algorithms, and the ure 3, such plumes can meander and exhibit unpredict-
use of agile deployment technologies. able concentration distributions, making single-sensor
placement for threat detection a difficult task. Even an
Chemical and biological Threats exquisitely sensitive sensor will never detect threats that
Fundamentally, the majority of large-scale outdoor CB do not interact with it.
attacks fall within two categories, line and point. These On the basis of the understanding of the behavior of
two categories refer to the style of release. Line releases, individual plumes, high-density sensor networks are an
as shown in Figure 1, consist of a moving release point ideal choice for detecting CB threats from point releases.
that disseminates a low concentration of threat material High-density sensor networks work well because the
over a wide area. Sensing CB threats from line releases increased sensor density offers increased sensor coverage
requires a few high-fidelity sensors capable of detecting within the spatial domain, thus increasing the probability
very small quantities of threat material. Sparse deploy- that the plume will interact with at least a few sensors.
ments of high-fidelity sensors are suitable for detecting Because, in most cases, predicting whether an adver-
line releases because low concentrations of threat mate- sary may use a point or line style of attack is impossible,
rial permeate a wide area and have a high probability of it may make sense to always use high-density networks of
encountering a sensor. high-fidelity sensors. In theory, this approach is ideal for
Conversely, a point release, shown in Figure 2, con- addressing the entire threat space, but is greatly hindered
sists of a stationary release point that disseminates a high by practical considerations. Currently, the high-fidelity
26 LINCOLN LABORATORY JOURNAL n VOLUME 18, NUMBER 1, 2009
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figuRe 3. A photograph comparison reveals the difference between an individual plume (top) and an
averaged set of plumes (bottom) [3]. Individual plumes do not have Gaussian concentration distribu-
tions or smooth gradients between regions of high and low concentrations, which are seen in the aver-
age of many plumes or in plume ensembles.
sensors that are capable of detecting ultralow concentra- from individual sensors. In essence, spatial networks of
tions of threat material can cost upwards of $50K per sen- low-performance, inexpensive sensors can perform just
sor. Building high-density networks of hundreds of sensors as well as, if not better, than a single high-performance,
is immediately prohibited by the involved sensor costs. expensive sensor by leveraging both the proximity of sen-
In order for high-density networks to serve as a sors to the threat source and the spatial correlations of
practical solution, they must utilize inexpensive sensors. sensor readings. Furthermore, sensor networks can be
With CB sensors, there is a very strong inverse correla- designed to be more tolerant of single-node failures and
tion between sensor cost and sensor performance. In gen- more forgiving of exact sensor placement. Much of the
eral, as the sensor costs decline, the sensor’s sensitivity work discussed in this report demonstrates these con-
diminishes and its false-alarm rate increases. Much of the cepts under realistic conditions.
recent sensor development from the CB community has
focused on the development of sensors with ultralow sen- modeling and Analysis
sitivities and false-alarm rates, but at an ever-increasing When assessing the performance of CB sensing systems,
sensor cost. many researchers traditionally use modeling and simula-
It is our belief that when used intelligently in net- tion tools, such as the Defense Threat Reduction Agency’s
works, inexpensive lower-performing sensors will serve (DTRA) Hazard Prediction and Assessment Capability
as an effective solution for detecting threats from point tool or the Navy’s Vapor, Liquid, and Solids Tracking tool
releases. Through our research, we have determined this [4, 5]. However, these modeling tools rely upon the Sec-
to be true for two fundamental reasons. First, for releases ond-order Closure Integrated Puff (SCIPUFF) model to
within or near the sensor network, some of the sensors simulate the atmospheric dispersion of vapor and aerosol
are bound to be located near the threat source, which materials. SCIPUFF is a Lagrangian puff dispersion model
is typically a region of high concentration of material — that represents material dispersions as distributions of the
thereby reducing the sensor’s performance requirements. probability of encountering a specific concentration at a
Second, by searching for spatially correlated sensor alarm specific time and location. In essence, these probability
activity within the spatial network, the network false- distributions represent an ensemble of plumes rather
alarm rate is reduced by rejecting uncorrelated alarms than an individual plume. The difference between plume
VOLUME 18, NUMBER 1, 2009 n LINCOLN LABORATORY JOURNAL 27
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Release point
figuRe 4. This visualization depicts the 3D virtual urban area within the computational fluid dynamics (CFD) model and the
sarin release points. The urban area is derived from geographical information system data on Oklahoma City’s central business
district. We released 0.18, 1.8, and 18 kg each at ten different locations within the simulated environment to constitute the thirty
different plumes. The staggered release times, plus the varying winds, helped increase variance among plumes.
ensembles and an individual plume is shown graphically plumes and has undergone an extensive validation pro-
in Figure 3. While ensemble plume models are useful for cess using real field measurements. CFDRC validated the
studying the full range of probable plumes under specific underlying models in the Urban Areas framework using
atmospheric and release conditions, they are not, nor were data recorded from the Joint Urban 2003 (JU2003) field
they intended to be, the best tool for studying individual test, which took place in Oklahoma City in July 2003 [7].
plume realizations. JU2003 was sponsored by the Department of Homeland
This is an important consideration because high- Security, DTRA, and the U.S. Department of Energy, and
density CB sensor networks exploit the unmixed nature was designed to study the atmospheric transport of mate-
of individual plumes. To correctly assess the performance rials by conducing multiple releases of inert tracer gases
of high-density CB sensor networks, we needed a disper- within Oklahoma City’s urban environment [8].
sion model capable of generating individual plume real-
izations. Currently, computational fluid dynamics (CFD)
1.0
models are one of the most accurate methods for creating
high-fidelity models of individual plumes, but are typically 0.5
complex and computationally expensive and are usually
0
built for very specific modeling tasks. These characteris-
-0.5
tics can make the development of CFD models difficult
and time-consuming. Fortunately, we were able to work -1
with Computational Fluid Dynamics Research Corpora- -1.5
tion (CFDRC) to develop a version of their “Urban Areas”
-2
model to simulate multiple instances of individual plumes 800
400
[6]. CFDRC’s Urban Areas framework uses CFD calcula- -2.5
400
200
tions to simulate the transport and dispersion of chemical, 0 200 -3
0
biological, radiological, nuclear, and explosive (CBRN/E) -200 -200
materials within three-dimensional urban environments.
figuRe 5. A sarin release is visualized within the CFD
The Urban Areas simulation framework can generate
model. The color of the plume indicates the instantaneous
plumes that closely approximate individual, realistic sarin concentration.
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By using this simulation framework, we were able 10-2
to simulate thirty outdoor releases of sarin within Okla- )
m
p
homa City’s central business district. We selected sarin as p
a release material because it is a chemical warfare agent vity ( 10-3
that is lethal in small doses and has been used to attack siti
n
e
urban environments in the past [9]. We extracted hypo- r s 10-4
o
thetical sensor data from the CFD model’s wind and con- s
n
e
centration fields by using a custom-built post-processor s
d
that outputs time-series data from any coordinate within re 10-5
ui
q
the model’s virtual environment. This capability allowed e
R
us to build networks of hypothetical CB sensors and ane-
10-6
mometers anywhere within the model. The thirty different 5 10 15 20 25 30
plume dispersions along with the post-processing tools Number of sensors in network
formed a highly capable test platform on which we could
figuRe 6. The addition of more sensors reduces the
assess the detection performance of high-density sensor
required sensitivity to detect 100% of all releases. The sen-
networks. Figure 4 displays the three-dimensional simu-
sor networks were generated by randomly deploying n sen-
lation environment and the ten unique release points. A sors across the CFD model’s virtual environment at a fixed
simulated sarin release is shown in Figure 5. height of 3 m above ground level.
Required Sensitivity versus Sensor Density. Central
to the application of high-density sensor networks is the Sensor Placement. In addition to the choice between
notion that as the sensor density increases, the required few high-fidelity sensors and many inexpensive sensors,
sensitivity for the individual sensors can be reduced while the question of sensor placement is an important consid-
maintaining a fixed detection performance. This reduction eration when deploying sensors. If you only have a few
in required sensitivity permits the use of lower-cost sen- sensors, you had better place them in the right locations;
sors, as illustrated in Figure 6. As more sensors are added otherwise, you may miss threats completely. The most
to the network, increasing the sensor density, the sensitiv- convenient approach to sensor placement is a random,
ity requirements for the individual sensors are significantly or ad hoc, placement of sensors rather than an optimized
reduced. Note that the values on the axes in Figure 6 are sensor configuration. An optimized sensor configura-
specific to the modeled environment, but the relationship tion requires an in-depth analysis of all potential threats,
between sensor density and required sensitivity holds true which is expensive and time-consuming. Conversely, an
for the general scenario of point-release detection. ad hoc configuration can be deployed quickly and with-
Application-Specific Performance Gains. The out prior knowledge of quirks of the local environment.
flexibility of the CFD simulation framework allowed us Figure 8 shows that increasing the sensors’ spatial den-
to analyze specific questions regarding the application sity diminishes the required sensor sensitivity between an
of high-density sensor networks. A pertinent applica- optimal and an ad hoc sensor configuration. The ability to
tion of high-density CB sensing is for fixed-site or facility rely upon an ad hoc sensor configuration greatly reduces
protection. When it comes to this type of application, the risk and effort involved in sensor placement.
a commonly asked question is: With my fixed budget,
should I deploy a small number of higher-cost, higher- field Tests
performing sensors or a large number of lower-cost, When testing the performance of CB sensor systems on
lower-performing sensors? This question is not easily individual plumes, there is no substitute for field tests.
addressed because of the wide variety of threats that may Field tests enable experimentation with real plumes and
be of concern, but for defense against point attacks, the ambient backgrounds influenced by real atmospheric con-
cost-benefit analysis (summarized in Figure 7) shows ditions. Obtaining approval to release large quantities of
that having more low-cost sensors is preferable to fewer materials into the atmosphere within urban environments
higher-cost sensors. for scientific experiment is a difficult process, so we chose
VOLUME 18, NUMBER 1, 2009 n LINCOLN LABORATORY JOURNAL 29
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to conduct releases of tracer $50K $100K $200K
budget budget budget
material in a large open field
0.9 100% sensor 1 100%
away from urban structures.
sensor 1
In addition, we selected water-
0.8
based smoke as the tracer com- n
o 100%
pound because it is relatively cti sensor 1
e 9% sensor 1
safe and its dispersion is visually et0.7
d
observable. Figure 9 displays of
y
our test grid and sensor nodes. bilit0.6 Points represent different
a
We also note that out of the b network compositions
o
r
twenty-five sensors, some failed P
0.5
to report readings for at least 0% sensor 1 Sensor 1: sensitivity = x ppm,
part of the trials. Since there cost = $6K
0.4 Sensor 2: sensitivity = 0.03x ppm,
is usually a non-negligible risk
33% cost = $19K
of sensor failure in real fielded sensor 1
systems, distributed deploy- 0 5 10 15 20 25 30 35 40
ments offer the advantage of Trial number
fault tolerance (in the sense figuRe 7. This cost benefit analysis shows the probability of detection for sensor net-
that they are less susceptible work compositions for three different procurement budgets. Each data point represents
a sensor network configuration consisting of a mixture of two commercially available
to information blackout than
chemical sensors, one high cost and one low cost with corresponding performance. For
single-node deployments).
every budget, the networks consisting solely of low-cost sensors achieve the highest
Making the Field Data overall probability of detection.
More Realistic. Because the
smoke releases consisted of particle concentrations of contaminant presence. To do this, we used ground
that were significantly higher than the particle counts truth about the smoke releases to estimate the number
observed within the ambient background, we devised of counted particles that originated from the release
a method for suppressing the signal strength of each and not from interferents. Then we adjusted the data by
release, so that we could experiment with more challeng- including only a specified fraction of the smoke particles
ing concentrations while retaining fully realistic plume in the total particle counts. Figure 10 shows a time series
behavior. We controlled the signal strength by adjusting of particle counts at several different signal strengths for
the particle counts to amplify or suppress the evidence a sample node and contaminant release.
)
m
p 103 figuRe 8. Comparison of optimal versus an ad hoc sen-
p
y ( sor placement identifies the necessary sensor sensitivity
vit for each network configuration to achieve a probability of
siti detection (PD) of 1. The y-axis is the difference between this
n
e
s required sensitivity for the optimal and ad hoc networks.
r
so 102 The optimal configuration is the best possible sensor place-
n
e ment if one had complete knowledge of the plume’s future
s
d trajectory—information that is unattainable in real scenar-
e
r
ui ios. For networks consisting of few sensors, the optimized
q
e placement requires the individual sensors to be two to three
r
n
e i 101 orders of magnitude more sensitive than the sensors within
nc the optimal configuration. As the sensor density increases,
e
r the required sensor sensitivity for the ad hoc configuration
e
Diff 0 5 10 15 20 25 30 35 40 45 50 approaches that of the optimal sensor configuration, making
an ad hoc configuration achievable.
Number of sensors in network
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Network False-Alarm Rate Reduction. In addition
to supporting our CFD analysis, the recorded field-test
data could also be used to study the false-alarm rates
of high-density networks of CB sensors. Generally, it is
m
thought that as more sensors are added to a network, the
0
0 overall probability of false alarms of the whole network
2
will increase. This is true, in the absence of further data
processing, but even the application of simple rules can
mitigate unnecessary false alarms while maintaining a
constant probability of detection.
We ran experiments to examine how detection per-
figuRe 9. The outdoor field tests consisted of 25 sensor formance varies with the number of sensors included in
nodes arranged on a square grid, spaced 50 m apart. Each the network. In order to measure performance, we set
node consisted of one battery-powered, tripod-mounted
the alarm threshold low enough to achieve a specified
particle counter (either a TSI AeroTrack 8220 or MetOne
benchmark for successful detections, then counted the
HHPC-6 EX) and one anemometer (Davis 7911). Wind
speed and direction measurements were transmitted in false alarms triggered at that same threshold. We defined
real-time from every sensor node to a base station by using the false-alarm ratio as the number of false alarms divided
low-power mesh radios (Crossbow Mica2). Sonic anemom-
by the total number of background data samples. Figure
eters were placed in two locations on the grid for high-fidel-
11 shows the false-alarm ratio of the sensor network as a
ity wind data to compare to the Davis 7911 anemometer
readings. During the test, 36 smoke candles that produced function of the number of available sensors. The curves
40,000 ft3 of water-based smoke in 30 s were activated. In for all three benchmarks exhibit the same general trend:
addition to the concentrations and spread of the 36 smoke we see a false-alarm reduction in the range of 2 to 3 orders
releases, measurements of the ambient background were
of magnitude as the number of distributed sensors is
collected periodically throughout the test.
increased from 1 to 23 (the maximum number of func-
We evaluated detection performance in terms of both tioning particle counters for which data were collected).
successful detections and false alarms. We declared a
successful detection of a contaminant release if the con-
taminant score exceeded an alarm threshold at least once × 104 Node no. 5, Release no. 12
4
during that release. Correspondingly, we declared a false
Containment window
alarm every time the contaminant score exceeded the 3.5
same threshold during periods of background data col- 3 Signal factor: 1
lection. Note that either event depends upon the alarm unt SSiiggnnaall ffaaccttoorr:: 11 //24
threshold. In the ideal case, there exists a threshold that co2.5 Signal factor: 1 /20
e
yields successful detection of all releases while triggering cl 2
rti
no false alarms. In fact, we can achieve this ideal perfor- Pa
1.5
mance on the original, unadjusted data by using a detec-
1
tion algorithm that fuses information gathered from the
entire sensor network. The data facilitate good detection 0.5
performance in part because the smoke was released from
0
within the sensor network, creating a relatively strong con- 49.34 49.35 49.36 49.37 49.38 49.39 49.4
taminant presence. Therefore, in our analysis, we evaluate Time (hours)
detection performance in the more difficult case when the
contaminant signal is suppressed by a factor of 20. The figuRe 10. The graph shows the particle count time series
for several different contaminant signal strengths, ranging
suppressed data simulate sensors with reduced discrimi-
from the original signal strength (factor 1) to low strength
nation between contaminant and background—a more
(factor 1/20). The number of particles due to background
realistic model of common low-cost CB sensors.
noise remains the same.
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100 ing that point in time. We compute the sample mean and
Detection benchmark = 36/36 standard deviation of the set of readings, then express the
Detection benchmark = 33/36
Detection benchmark = 30/36 particle count at the current time step in units of standard
10–1 deviations above or below the mean.
o
ati Normalization method #2. This method is similar to
r
m method #1, except that the set of preceding particle counts
r
a
al10–2 is specific to each sensor node. As a result, the readings
e-
als are normalized by using sample means and standard devi-
F
ations that are local in space as well as time. In addition
10–3 to normalization, we also apply a simple anomaly detector
to help screen out extreme particle counts that are due to
sensor malfunction or short bursts of noise.
0 5 10 15 20
Number of sensors We considered several types of centralized contami-
nant detection algorithms. The simplest is the “max algo-
figuRe 11. Increasing the number of sensors lowers the
rithm,” which takes the maximum reading reported by any
incidence of false alarms. Each curve plots the false-alarm
ratios corresponding to a particular detection requirement sensor node within a particular time frame as the indica-
for the 36 releases. The top curve plots the false-alarm tor of contaminant presence (also called the “contaminant
ratio corresponding to a benchmark of perfect detection,
score”). The max algorithm, when applied to normalized
while the lower two curves plot the false-alarm ratio cor-
sensor readings, performs well on most of the field data
responding to relaxed benchmarks of 33-out-of-36 and
30-out-of-36 successful detections, respectively. Since the samples; however, when any one of the sensors experi-
placement of the sensors affects the experimental results, ences high levels of interferent noise, the contaminant
we averaged each curve across 200 trials, with randomly score is inflated, causing false alarms. A better approach
selected sensor locations for each trial.
is to base the detection algorithm on a collection of net-
work-wide features that better characterize the sensed
Network detection Algorithms phenomenon. Figure 12 shows an example of one such
With a spatially distributed sensor network in place, useful cue: spatial entropy, which measures the distribu-
one might enhance the network’s overall detection per- tion of the particulate across the network.
formance by applying detection algorithms that operate We computed the following features for six instances,
across the entire network. These detection algorithms are on the basis of readings reported during 16-second time
able to identify spatial and temporal correlations that may frames: max readings of normalization methods #1 and
help discriminate plume-like activity from random frac- #2, mean readings of normalization methods #1 and #2,
tionations in the individual sensor readings. To explore spatial entropy of normalization method #2, and largest
the capabilities of network detection algorithms, we evalu- temporal gradient of normalization method #2. We used
ated several algorithms on suppressed-signal field data. two different methods to derive a contaminant score from
Such data present more of a detection challenge than do these six features. The first method builds Gaussian mix-
the original data. It is important to note that the field data ture models (GMM) [10] to characterize the distributions
exhibit several properties that require data preprocessing. of both the contaminant data and the background data
The level of background noise drifts significantly through- in the six-dimensional feature space. New data points are
out the time period of data collection because of varying assigned a contaminant score by computing the ratio of the
particle concentrations in the ambient backgrounds. To contaminant GMM value to the background GMM value.
deal with this noise drift, we apply two different forms of The second method computes the contaminant score of
data normalization that extract more meaningful informa- a data point by taking a weighted linear combination of
tion from the particle counts, as described below: its feature values. The weights of this linear discriminant
Normalization method #1. At each time step during (LD) are derived via iterative optimization so that con-
data collection, we take all particle counts reported any- taminant data points yield high scores and background
where in the network within a 30-minute window preced- data points yield low scores [11]. The weight values, in
32 LINCOLN LABORATORY JOURNAL n VOLUME 18, NUMBER 1, 2009
AdAm NoRige, JASoN ThoRNToN, CuRRAN SChiefelbeiN, ANd ChRiSTiNA RudziNSki
Background instance Contaminant instance
figuRe 12. Particle count readings are shown, as reported by the 5-by-5 fielded sensor network within two different time
frames. The green channel inside each circle is set proportional to the log of the normalized particle count for that sensor.
left: a noisy background instance with high maximum and low spatial entropy. Right: a contaminant release instance with
lower maximum but higher spatial entropy.
combination with an alarm threshold, define a hyperplane mance is critically dependent upon the algorithm used to
that partitions the feature space into a contaminant region fuse the information from the individual sensors.
and a background region, as illustrated in Figure 13.
Table 1 summarizes detection results for the algo-
rithms described above on adjusted field data, for which
0.8
the contaminant signal strength has been suppressed by
a factor of 20 (see Figure 10). The “baseline” algorithm,
0.6
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classifiers in a feature space that better characterizes Te(n 0
the joint information from multiple sensors, as dem-
–0.2
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requires learning a small set of parameters; therefore, Max Mean
(normalized units) (normalized units)
we evaluated these algorithms by averaging the results
across two-fold cross-validation trials [12]. Figure 14 figuRe 13. This visualization of the feature space used to
shows the algorithm performances graphically in the characterize the sensor network data shows three out of six
feature dimensions. A sample set of the field data is plot-
form of receiver-operator-characteristic curves. From
ted, with each point representing the feature values mea-
these results, we conclude that merely establishing a
sured within a single time frame. Red points indicate data
distributed sensor network with good coverage does not collected during contaminant release periods of a field test;
guarantee significantly improved performance, especially blue points indicate data collected during background peri-
ods. The algorithm learned to use a linear discriminant,
when dealing with relatively weak contaminant signa-
shown as the plane in the figure, to separate the contami-
tures. As demonstrated in Table 1, the network’s perfor-
nant and background data.
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1
were able to build a self-forming
and adaptive wireless network
0.9
while maintaining our low
0.8 power consumption and low
cost requirements.
0.7
n
o Distributed Sensing and
cti
e 0.6 Wireless Communications.
et
of d 0.5 The vision of tiny cooperative
y sensors, so small as to be ubiq-
abilit 0.4 uitous, is at least thirty years
b
o old (older if you include science
r
P
0.3 fiction). However, it took more
Alg. 1: norm 1, max detection
0.2 Alg. 2: norm 2, max detection than twenty years of technol-
Alg. 3: stats, GMM detection ogy improvements before truly
Alg. 4: stats, LD detection
0.1 tiny, low-power sensors became
Alg. 5: baseline
an approachable target, and in
0
10-3 10-2 10-1 100 order to have sensors cooperate,
Probability of false alarm they must have communication
elements as well. Advances in
figuRe 14. Receiver-operator-characteristic curves compare the performance of
microelectromechanical sys-
various detection algorithms. The baseline algorithm is displayed in black; the colored
curves represent the algorithms performing detection by using the network’s spatial tems (MEMS) and lower-cost
and temporal features. manufacturing have brought
many new devices and radios to
Communications infrastructure a price and size/weight/power point appropriate for large-
To pursue our vision of widely distributed, low-cost sen- scale, long-term sensing deployments. It was for these
sors for CB threats, we sought a communication infra- reasons that we became interested in using an emerging
structure that was inexpensive and highly flexible. The communication platform, termed motes, for our data col-
possibilities offered by sensing based on “smart dust,” lection campaigns.
more commonly known as “motes,” were promising [13]. For our purposes, a mote is a computing and commu-
By employing motes within our sensing architecture, we nication platform hosting one or more sensors [14, 15].
PREPROCESSInG DETECTIOn PROBABILITy OF
LABEL METHOD METHOD FALSE ALARM
Baseline None Max algorithm 66.14%
Algorithm 1 Normalization method #1 Max algorithm 2.60%
Algorithm 2 Normalization method #2 Max algorithm 0.53%
Algorithm 3 Network-wide features Gaussian mixture models 0.07%
Algorithm 4 Network-wide features Linear discriminant 0.04%
TAble 1. A comparison of the detection algorithm’s probability of false alarm (for PD = 1) on field data with suppressed con-
taminant signal strength shows the need for some form of particle-count normalization. The performance of the baseline
algorithm, which employs no preprocessing or feature extraction, is unacceptable. Significant improvement is obtained from
classifiers that better characterize the joint information from multiple sensors, as shown by the results of algorithms 3 and 4.
34 LINCOLN LABORATORY JOURNAL n VOLUME 18, NUMBER 1, 2009