this scenario: A terrorist carefully negotiates city streets,
moving ever closer to his target, an air force base on the outskirts
of town. In the rear of his van, a homemade bombcontaining
plutonium and high explosiveswaits for the signal to explode.
As one of the good guys, youve received information
that the attack is imminent, but your sources dont know
its timing, the direction from which the vehicle will come, or
what route it will take. What can you do to detect, identify,
and track the van and its contents so that you can prevent the
attack? At Lawrence Livermore, researchers in the Nonproliferation,
Arms Control, and International Security (NAI) Directorate have
been exploring responses to this threat and others like it.
researchers are focusing on systems for detecting and tracking
threats. The systems go by many namescorrelated sensor networks,
wide-area tracking systems, sensor or network fabricsbut
the concept behind them is the same. Take a number of wireless
sensors (for instance, seismic, magnetic, pressure, acoustic,
nuclear, or particle-counting), tie them together with a communications
network, develop a scheme for fusing the data (that is, converting
the data into forms easily interpreted by users), and make the
system easy to deploy.
Such correlated sensor
networks can help detect a nuclear terrorist attack, track the
movement and characteristics of a wildfire, assist military operations
in taking out a target, determine earthquake damage to large structures
such as bridges, and even protect the president.
ChallengeSmaller, Smarter, More Energy-Efficient Sensors
Most of todays
wireless sensors are big and heavy. They have large
power requirements and limited intelligence. Thus,
large networks of such sensors are impractical.
In the Nonproliferation, Arms Control, and International
Security (NAI) Directorate, researchers are working
to create sensors that use less energy, are more
intelligent, and scale better to large networks.
issue, notes engineer Rob Hills, is a big concern
for sensors that are networked. We have a
saying that power is everything, he explains.
Power requirements make a network feasibleor
not. For instance, the Joint Biological Remote
Early Warning System (JBREWS) prototype network
used 132 commercial sensors, each requiring two
batteries (one in the sensor, one in the charger)
to operate continuously, for a grand total of nine
tons of batteries. To address this problem, the
Laboratory developed a communication system that
requires an average power of only 1 watt. And
were pushing the power requirement down from
there, says Hills.
In a back-to-basics
project, Laboratory engineer Dave Harris is researching
the underlying physics that is key to creating microsensors
for seismic networks. I believe that Daves
workalong with our data-fusion techniqueswill
allow us to create cheap and small sensors, which
can be delivered from a remote platform such as
unmanned aerial vehicles, says Hills. Harris
has been been working with engineer Bruce Henderer,
who has developed a prototype sensor about 3 centimeters
thick and 6 centimeters square small enough
to hold in the palm of your handand containing
a low-power communications device that allows the
sensors to network and to configure themselves.
In other words, says Hills, once
laid down, the sensors would talk to each other
and, by determining their neighbors, build a network
and paths back to control. The data processing
would take place out in the network, with the network
sensors themselves being capable of pattern recognition,
information fusion, and decision making.
prototype sensor being developed at Livermore.
Power of Networking
The power of correlated
sensor systems arises from their networked nature. You could
ask, Why not just use a bunch of stand-alone sensors?
says Rob Hills, acting leader of the Tactical Systems Section
in NAI. Part of the problem is that many sensors, particularly
those that detect nuclear signals such as gamma rays and neutrons,
have a hard time differentiating between a hit and
normal variations in the background radiation. And to compound
the challenge, the farther one moves away from a nuclear source,
the weaker the signals become. Sid Niemeyer from the NAI
directorate office agrees, saying that Weapons-usable nuclear
materials are difficult to detect under circumstances of unconventional
nuclear warfare, as nuclear terrorism is sometimes called. As
the distance between a detector and source increases, the radiation
signature quickly fades into the background caused by other artificial
and natural sources.
One solution is to network
the sensors, that is, have them share the information they gather.
Networked sensors allow the user to see more
by creating a more complete picture of the situation, something
that stand-alone sensors cannot do, says Niemeyer. For this
articles opening scenario, for instance, a correlated sensor
network could do double duty. First, it could provide a way to
discard signals that are false alarms. Second, it could pick up
on signals that might be real alarms but would have been ignored
by stand-alone sensors because the signals were under a preset
threshold of sensitivity.
The figure below shows
how correlated network and stand-alone systems differ. In the
figure, the truck carries a device that emits signals averaging
50 counts per second; signals in the natural background are on
average 300 counts per second. Three stand-alone sensors are set
to register detections, or hits, of 350 or more counts, which
would reduce false alarms caused by background variations.
truck passes the first sensor, which detects 355 countsa
possible hit. It goes on its route and the stand-alone sensors
detect five other instances of over 350-count signals. The detections
provide no information to the person at the central command post
as to whether they are real alarms or not.
However, if the sensors
were networked and able to communicate with each other, a different
picture would emerge. For one thing, notes Hills,
you can include other information in the system, such as
the approximate travel time of the vehicle. A reasonable assumption
would be that the vehicle is traveling at the speed limit, because
its driver probably would not want to attract attention to himself.
In the correlated sensor
case, the 355-count signal at sensor one is noted as a possible
hit. This information is shared with sensor two, and then the
system clock starts to track travel time. The travel time between
sensors one and three is assumed to be about 5 minutes. Sensor
two is on the alert for signals above background that appear within
a certain window of time centered on a predetermined time mark,
say, 2.5 minutes. The closer a signal is detected to that 2.5-minute
mark, the more weight is given to the probability that the signal
is from a real source, rather than some random hiccup from background.
The ensuing signals at
sensors one and three are discounted as false alarms because they
are uncorrelated, that is, they show no relation to previously
recorded data. If the truck is proceeding forward, it would not
register at sensor one, which it just passed, and the signal at
sensor three comes much too soon. However, the 340 counts detected
at sensor two, even though a trifle low, is viewed as a possible
hit because it falls within the allotted window of time and is
considerably higher than background. This information is passed
along to sensor three.
Three signals follow and
are discounted as false alarms because of their location and timing.
However, the 348-count signal at sensor three is recorded and
its probability of being real is calculated and correlated with
the preceding hits.
here is that were actually correlating signatures in different
domains, explains Hills. For this example, were
correlating data from both temporal and spatial domains: correlating
whether the appropriate sensor gets the hitwhich is the
spatial domainand whether that hit may be due to the source
based on the time of travel between sensorswhich is the
temporal domain. We then perform some statistical calculations
to determine how probable it is that the hit is real, based on
the number of counts detected and whenwithin the allowable
window of timethe counts are detected.
Performing these kinds
of calculations for three networked sensors is one thing, but
widen a network to include 100 sensors and it becomes extraordinarily
challenging. The computer algorithms needed to track and follow
more than one likely pattern and calculate all of the probabilities
are extremely complex (see box below) and are only now possible
with the increases in computing power.
illustration shows the different results produced by stand-alone
sensors versus a correlated sensor network. Here, the sensors
are set to register signals of 350 or more counts per second
from a truck carrying a signal-emitting device. The stand-alone
sensor system simply detects six instances of over-350 signal
counts (blue boxes). The networked system, having access to
more information, correlates the information to discount all
but the first detection as false alarms and to register two
others that are under the 350-count threshold as likely hits,
which are then correlated to the first hit.
and Other Applications
have been working on many applications of correlated sensor networks.
For instance, the Laboratory has developed a prototype correlated
sensor network for detecting and tracking a ground-delivered nuclear
material. The Wide-Area Tracking System (WATS) is a network of
gamma and neutron detectors and communications links, with information
continuously evaluated by Laboratory-developed data-fusion algorithms.
The sensors can be permanently deployed at chosen locations or
mounted in vans for deployment on demand to protect specific areas
for specific situations or events.
The individual sensors
share their data with neighboring sensors, process the data, integrate
and combine them with other available information (for example,
data gathered previously; observed radiation signatures, spectra,
and backgrounds; road maps), and finally determine the probability
that the signal comes from a real sourceall while the system
is in the field. In this way, a WATS sensor network can drastically
reduce false alarms and detect the entry of a nuclear device or
radioactive material into the protected area and track its movement.
analysis could be performed by a centralized computer at, say,
command headquarters, but researchers have found that communications
limitationslatencies, available bandwidth, and so oncan
be a significant bottleneck for these types of networks. When
data are processed in the field, it is necessary to send only
bits of information between neighbors, with the final result going
to the human user. This type of operation makes the network much
Another example of correlated
sensor network development involves a recently concluded project
called Joint Biological Remote Early Warning System (JBREWS).
For JBREWS, the Laboratory was responsible for developing the
command, control, communications, computers, and intelligence
systems for a network of biodetectors that could provide U.S.
field troops with early warning of a biological attack. Although
the project is not continuing, it has allowed the Laboratory to
make important progress in developing data-fusion solutions that
could be applied to any type of correlated sensor network. The
communications paradigms that were developed in JBREWS let Laboratory
researchers take a big step toward solving one part of the data-fusion
problemthat is, how to quickly and automatically establish
a communications fabric for data fusion to work within.
In this communications
scheme, the array of sensors forms an automatically reconfiguring,
or self-healing, network, as follows. Once the sensors are in
place, they communicate with each other via radio frequencies
so each sensor can map where its neighboring sensors are. The
sensors then radio-test each other and develop an efficient communications
path back to the central command post. If, for example, one sensor
cant communicate directly with the command post on the other
side of a hill, it passes its data to its neighbors, to be relayed
with the neighbors data to other units, and so on, until
the information reaches its destination. If a unit is knocked
out by a malfunction or hostile action, its communication relay
functions are picked up by surrounding units and a secondary path
is formed. In short, the system quickly recognizes and adjusts
to the absence of any sensor units. A big plus for this type of
network and others like it, Hills notes, is that there are no
Another military application
would connect these sensor networks with other systems, such as
the Laboratory-developed Counterproliferation Analysis and Planning
System (CAPS). CAPS can model the various processes (chemical,
biological, metallurgical) used by proliferators to build weapons
of mass destruction and their delivery systems. CAPS helps users
identify critical processing steps or production facilities that,
if disabled or destroyed, would prevent that country from producing
weapons of mass destruction. Now imagine adding correlated
sensor networks to the mix, says Hills. Sensors on
the ground and in the air could track processes in real time.
A user could click on the Web-based CAPS page and find out whats
going on right then at such-and-such a facility.
Yet another application
for such networks is in tactical engagement systems. With sensor
networks as part of these systems, a soldier would never be alone
in the field. The sensor network could supply information not
just to people in the field, but to those who are out of harms
way as well. They would all be tied together in a collaborative
environment. With such a system, the electronic network would
be displayed in a chest-top system so that a soldier could see
the environment and watch his backall from one small device.
Correlated sensor networks
could also be used in nonmilitary applications to provide temporary
communication infrastructures after a destructive earthquake or
to provide information during large firestorms. For example, there
are microclimates within a large fire. A correlated sensor network
could track temperatures, humidity, and wind in three dimensions,
providing valuable information to firefighters.
David McCallen, director
for Livermores Engineering Center for Complex Distributed
Systems, notes that current research to develop self-healing,
self-configuring networks of seismic sensors would be useful in
studying how large structures respond in earthquakes. Once
these networks are developed, its a small step to apply
them to large structures, such as bridges, to gather data on how
these structures vibrate and respond under various circumstances,
he explains. When you consider that to densely instrument
a structure like the Golden Gate Bridge takes hundreds of sensors,
having a system thats wireless and self-configuring is very
attractive. He adds that the California Department of Transportation
is also interested in using such networks to monitor steep hillsides
for possible landslides.
Statistics at Work
the computer algorithms to perform distributed decision
making for a sensor network, a team of researchers,
including physicist Chris Cunningham, came up with
an approach based on Bayesian algorithms. As Cunningham
explains it, the Bayesian approach has a couple
of pluses. First, it is energy-efficient because
communication only occurs when there is a sufficient
probability that a target has been detected. Second,
each sensor independently extracts features from
its raw sensor signals, compares these features
with the targets, calculates the likelihood of detection,
fuses the likelihoods received from neighboring
nodes, and communicates only the new likelihoods
to its neighboring nodes. This statistical data
fusion can allow each sensor platform to make decisions
based on the total information in the network, while
reducing the volume of communications among sensors.
is based upon the work of an English mathematician,
the Reverend Thomas Bayes. Bayes developed a mathematical
formula that allows scientists to combine new data
with prior conditions. In a sense, it
the question, Given that an event has occurred
that may have been the result of any of two or more
causes, what is the probability that the event was
the result of a particular cause? The answer
lies not in an absolute yes or no, but in the set
of probabilities that the various causes are at
play. Bayesian methods allow scientists to combine
prior information about a population parameter with
information contained in a sample to guide a statistical
inference process. A prior probability distribution
for a parameter of interest is specified first.
Sample information is then obtained and combined
through an application of Bayess theorem to
confirm the prior assumptions. Bayesian methods
are used extensively in statistical decision theory.
Wide-Area Tracking System (WATS) is one example
of a correlated sensor network that uses algorithms
based on Bayesian constructs. In WATS, each sensor
computes and exchanges information with its near
neighbors in the form of Bayesian probabilities
for possible sources. Algorithms reduce the sensor
data to probability estimates and then fuse the
estimates among the multiple sensors.
Sensors in Their Place
One of the challenges to
using these networks is getting them in place, in real terrain.
In a battlefield scenario, for instance, or during a wildfire,
you cant have people tromping in to set down sensors,
says Hills. One answer is to use unmanned aerial vehicles (UAVs),
such as the U.S. Air Forces Predator or even smaller, 2-meter-wingspan
UAVs. In one project, researchers are evaluating the use of UAVs
to rapidly place, operate, and maintain sensor networks in rugged
terrain. Such vehicles could drop the sensors in predetermined
locations and then act as airborne routers. Once in place, the
sensors would form a network, communicate with each other, and
send information skyward to be collected and transmitted by the
software, researchers could create self-configuring and self-healing
networks made up of small, low-power sensors. If the sensors are
cheap enough, the result is a ready-to-use networka wireless
network on demand. In this kind of setup, the UAVs
become part of the system, sharing information about locations
of all the sensors and other UAVs, sensor data requirements, connectivity
maps, and UAV-sensor assignments; leveling the workload; and backing
up in case one or another UAV is put out of commission. This
is just one of the directions in which were moving
to position ourselves for the future, says Hills.
at Livermore are exploring the possibility of using unmanned
aerial vehicles (UAVs) to place, operate, and maintain sensor
networks in rugged terrain. In the figure, the sensors in
the network are shown sending their information.
Toward the Future
The idea of correlated
sensor networks is not Livermores alone. Other organizations
and commercial companies are exploring applications and, like
the Laboratory, pushing on whats possible in the laboratory
to get to whats feasible in the field. The key,
says Hills, is to find ways of gathering all those data
together and turning them into usable, real-time information to
let the user make decisions. Here at the Laboratory, weve
got the key in hand and are turning it in the lock. Its
only a matter of time before the door opens.
Bayesian statistics, correlated sensor networks, Counterproliferation
Analysis and Planning System (CAPS), gamma detector, Joint Biological
Remote Early Warning System (JBREWS), neutron detector, nuclear
terrorism, seismic detector, sensor or network fabrics, tactical
engagement systems, unmanned aerial vehicle (UAV), Wide-Area Tracking
information contact Rob Hills (925) 423-7344 (firstname.lastname@example.org).
is the acting associate division leader for the Tactical Systems
Section in the Nonproliferation, Arms Control, and International
Security Directorate. He leads a variety of projects that
include research and development for sensors and sensor networks,
military systems analysis, and computer-based battlefield
conflict simulation models. Hills received a B.S. in electrical
engineering from the Michigan Technological University in
1983. He joined Livermore in 1988 to perform research that
involved automating the transfer of existing digital designs
to new implementation technologies. Thereafter, he participated
in several projects to develop sensor systems and image-processing
technologies for astronomical telescopes. For example, he
was a member of the team that developed the camera system
to detect dark matter; the system won an R&D 100 Award
for being one of the most technologically significant new
products in 1993. Hills has led research and development efforts
for microtechnology tools, such as a polymerase chain reaction
system, used both in medical and national security applications.
And he has engineered optical interconnects for parallel computer
systems as well as overall architectures for self-configuring
and self-healing communications networks.