Sensor Networks: Evolution, Opportunities,
and Challenges
CHEE-YEE CHONG, MEMBER, IEEE AND SRIKANTA P. KUMAR, SENIOR MEMBER, IEEE
Invited Paper
Wireless microsensor networks have been identified as one of
the most important technologies for the 21st century. This paper
traces the history of research in sensor networks over the past
three decades, including two important programs of the Defense
Advanced Research Projects Agency (DARPA) spanning this
period: the Distributed Sensor Networks (DSN) and the Sensor
Information Technology (SensIT) programs. Technology trends that
impact the development of sensor networks are reviewed, and new
applications such as infrastructure security, habitat monitoring,
and traffic control are presented. Technical challenges in sensor
network development include network discovery, control and
routing, collaborative signal and information processing, tasking
and querying, and security. The paper concludes by presenting
some recent research results in sensor network algorithms, in-
cluding localized algorithms and directed diffusion, distributed
tracking in wireless ad hoc networks, and distributed classification
using local agents.
Keywords—Collaborative signal processing, microsensors, net-
work routing and control, querying and tasking, sensor networks,
tracking and classification, wireless networks.
I. INTRODUCTION
Networked microsensors technology is a key technology
for the future. In September 1999 [1], Business Week her-
alded it as one of the 21 most important technologies for the
21st century. Cheap, smart devices with multiple onboard
sensors, networked through wireless links and the Internet
and deployed in large numbers, provide unprecedented op-
portunities for instrumenting and controlling homes, cities,
and the environment. In addition, networked microsensors
provide the technology for a broad spectrum of systems in
the defense arena, generating new capabilities for reconnais-
sance and surveillance as well as other tactical applications.
Manuscript received January 7, 2003; revised March 17, 2003.
C.-Y. Chong was with Booz Allen Hamilton, San Francisco, CA 94111
USA. He is now with Alphatech, Inc. San Diego, CA 92121 USA (e-mail:
cchong@alphatech.com, cychong@ieee.org).
S. Kumar is with the Defense Advanced Research Projects Agency, Ar-
lington, VA 22203 USA (e-mail: skumar@ darpa.mil).
Digital Object Identifier 10.1109/JPROC.2003.814918
Smart disposable microsensors can be deployed on the
ground, in the air, under water, on bodies, in vehicles,
and inside buildings. A system of networked sensors can
detect and track threats (e.g., winged and wheeled vehicles,
personnel, chemical and biological agents) and be used for
weapon targeting and area denial. Each sensor node will
have embedded processing capability, and will potentially
have multiple onboard sensors, operating in the acoustic,
seismic, infrared (IR), and magnetic modes, as well as
imagers and microradars. Also onboard will be storage,
wireless links to neighboring nodes, and location and po-
sitioning knowledge through the global positioning system
(GPS) or local positioning algorithms.
Networked microsensors belong to the general family of
sensor networks that use multiple distributed sensors to col-
lect information on entities of interest. Table 1 summarizes
the range of possible attributes in general sensor networks.
Current and potential applications of sensor networks in-
clude: military sensing, physical security, air traffic control,
traffic surveillance, video surveillance, industrial and man-
ufacturing automation, distributed robotics, environment
monitoring, and building and structures monitoring. The
sensors in these applications may be small or large, and the
networks may be wired or wireless. However, ubiquitous
wireless networks of microsensors probably offer the most
potential in changing the world of sensing [2].
While sensor networks for various applications may be
quite different, they share common technical issues. This
paper will present a history of research in sensor networks
(Section II), technology trends (Section III), new applica-
tions (Section IV), research issues and hard problems (Sec-
tion V), and some examples of research results (Section VI).
II. H
ISTORY OF RESEARCH IN SENSOR NETWORKS
The development of sensor networks requires technolo-
gies from three different research areas: sensing, commu-
nication, and computing (including hardware, software, and
0018-9219/03$17.00 © 2003 IEEE
PROCEEDINGS OF THE IEEE, VOL. 91, NO. 8, AUGUST 2003 1247
Table 1
Attributes of Sensor Networks
algorithms). Thus, combined and separate advancements in
each of these areas have driven research in sensor networks.
Examples of early sensor networks include the radar net-
works used in air traffic control. The national power grid,
with its many sensors, can be viewed as one large sensor net-
work. These systems were developed with specialized com-
puters and communication capabilities, and before the term
“sensor networks” came into vogue.
A. Early Research on Military Sensor Networks
As with many technologies, defense applications have
been a driver for research and development in sensor net-
works. During the Cold War, the Sound Surveillance System
(SOSUS), a system of acoustic sensors (hydrophones) on the
ocean bottom, was deployed at strategic locations to detect
and track quiet Soviet submarines. Over the years, other
more sophisticated acoustic networks have been developed
for submarine surveillance. SOSUS is now used by the
National Oceanographic and Atmospheric Administration
(NOAA) for monitoring events in the ocean, e.g., seismic
and animal activity [3]. Also during the Cold War, networks
of air defense radars were developed and deployed to defend
the continental United States and Canada. This air defense
system has evolved over the years to include aerostats
as sensors and Airborne Warning and Control System
(AWACS) planes, and is also used for drug interdiction.
These sensor networks generally adopt a hierarchical
processing structure where processing occurs at consecutive
levels until the information about events of interest reaches
the user. In many cases, human operators play a key role in
the system. Even though research was focused on satisfying
mission needs, e.g., acoustic signal processing and interpre-
tation, tracking, and fusion, it provided some key processing
technologies for modern sensor networks.
B. Distributed Sensor Networks Program at the Defense
Advanced Research Projects Agency
Modern research on sensor networks started around 1980
with the Distributed Sensor Networks (DSN) program at the
Defense Advanced Research Projects Agency (DARPA).
By this time, the Arpanet (predecessor of the Internet) had
been operational for a number of years, with about 200 hosts
at universities and research institutes. R. Kahn, who was
coinventor of the TCP/IP protocols and played a key role
in developing the Internet, was director of the Information
Processing Techniques Office (IPTO) at DARPA. He wanted
to know whether the Arpanet approach for communica-
tion could be extended to sensor networks. The network
was assumed to have many spatially distributed low-cost
sensing nodes that collaborate with each other but operate
autonomously, with information being routed to whichever
node can best use the information.
It was an ambitious program given the state of the art.
This was the time before personal computers and work-
stations; processing was done mostly on minicomputers
such as PDP-11 and VAX machines running Unix and VMS.
Modems were operating at 300 to 9600 Bd, and Ethernet
was just becoming popular.
Technology components for a DSN were identified in a
Distributed Sensor Nets workshop in 1978 [4]. These in-
cluded sensors (acoustic), communication (high-level proto-
cols that link processes working on a common application
in a resource-sharing network [5]), processing techniques
and algorithms (including self-location algorithms for sen-
sors), and distributed software (dynamically modifiable dis-
tributed systems and language design). Since DARPA was
sponsoring much artificial intelligence (AI) research at the
time, the workshop also included talks on the use of AI for
understanding signals and assessing situations [6], as well
as various distributed problem-solving techniques [7]–[9].
Since very few technology components were available off
the shelf, the resulting DSN program had to address dis-
tributed computing support, signal processing, tracking, and
test beds. Distributed acoustic tracking was chosen as the
target problem for demonstration.
Researchers at Carnegie Mellon University (CMU),
Pittsburgh, PA, focused on providing a network operating
system that allows flexible, transparent access to distributed
resources needed for a fault-tolerant DSN. They developed
1248 PROCEEDINGS OF THE IEEE, VOL. 91, NO. 8, AUGUST 2003
Fig. 1. Components in the DSN test bed around 1985.
a communication-oriented operating system called Accent
[10], whose primitives support transparent networking,
system reconfiguration, and rebinding. Accent evolved into
the Mach operating system [11], which found considerable
commercial acceptance. Other efforts at CMU included
protocols for network interprocess communication to
support dynamic rebinding of active communicating com-
putations, an interface specification language for building
distributed system software, and a system for dynamic load
balancing and fault reconfiguration of DSN software. All
this was demonstrated in an indoor test bed with signal
sources, acoustic sensors, and VAX computers connected
by Ethernet.
Researchers at the Massachusetts Institute of Technology
(MIT), Cambridge, focused on knowledge-based signal
processing techniques [12] for tracking helicopters using a
distributed array of acoustic microphones by means of signal
abstractions and matching techniques. Signal abstractions
view signals as consisting of multiple levels, with higher
levels of abstraction (e.g., peaks) obtained by suppressing
detailed information in lower levels (e.g., spectrum). They
provide a conceptual framework for thinking about signal
processing systems that resemble what people use when
interactively processing and interpreting real-world signals.
By incorporating human heuristics, this approach was
designed for high signal-to-noise ratio situations where
models are lacking. In addition, MIT also developed the
Signal Processing Language and Interactive Computing
Environment (SPLICE) for DSN data analysis and algorithm
development, and Pitch Director’s Assistant for interactively
estimating fundamental frequency using domain knowledge.
Moving up the processing chain, tracking multiple targets
in a distributed environment is significantly more difficult
than centralized tracking. The association of measurements
to tracks and estimation of target states (position and ve-
locity) given associations have to be distributed over the
sensor nodes. In the 1980s, Advanced Decision Systems
(ADS), Mountain View, CA, developed a multiple-hy-
pothesis tracking algorithm to deal with difficult situations
involving high target density, missing detections, and false
alarms, and decomposed the algorithm for distributed
implementation [13], [14]. Multiple-hypothesis tracking is
now a standard approach for difficult tracking problems.
For demonstration, MIT Lincoln Laboratory developed
the real-time test bed for acoustic tracking of low-flying
aircraft [15]. The sensors were acoustic arrays (nine micro-
phones arranged in three concentric triangles with the largest
being 6 m across). A PDP11/34 computer and an array pro-
cessor processed the acoustic signals. The nodal computer
(for target tracking) consists of three MC68000 processors
with 256-kB memory and 512-kB shared memory, and a
custom operating system. Communication was by Ethernet
and microwave radio. Fig. 1 (extracted from [16]) shows the
acoustic array (nine white microphones), the mobile vehicle
node with an acoustically quiet generator in the back, and the
equipment rack with the acoustic/tracking node and gateway
node in the vehicle. Note the size of the system and that
practically all components in the network were custom built.
That was the state of the art in the early 1980s. The DSN test
bed was demonstrated with low-flying aircraft, which was
successfully tracked with acoustic sensors as well as TV
cameras. The tracking algorithm was fairly sophisticated,
since the acoustic propagation delay is significant relative to
the speed of the aircraft.
Another test bed in the DSN program was the distributed
vehicle monitoring test bed at the University of Massachu-
setts, Amherst. This was a research tool for empirically
investigating distributed problem solving in networks. The
distributed knowledge-based problem solving approach used
a functionally accurate, cooperative architecture consisting
of a network of Hearsay-II nodes (blackboard architecture
with knowledge sources). Different local node control
approaches were explored [17].
C. Military Sensor Networks in the 1980s and 1990s
Even though early researchers on sensor networks had
in mind large numbers of small sensors, the technology
for small sensors was not quite ready. However, planners
of military systems quickly recognized the benefits of
sensor networks, which become a crucial component of
network-centric warfare [18]. In platform-centric warfare,
platforms “own” specific weapons, which in turn own
sensors in a fairly rigid architecture. In other words, sensors
and weapons are mounted with and controlled by separate
platforms that operate independently. In network-centric
warfare, sensors do not necessarily belong to weapons or
platforms. Instead, they collaborate with each other over a
communication network, and information is sent to the ap-
propriate “shooters.” Sensor networks can improve detection
CHONG AND KUMAR: SENSOR NETWORKS: EVOLUTION, OPPORTUNITIES, AND CHALLENGES 1249
and tracking performance through multiple observations,
geometric and phenomenological diversity, extended detec-
tion range, and faster response time. Also, the development
cost is lower by exploiting commercial network technology
and common network interfaces.
An example of network-centric warfare is the Cooperative
Engagement Capability (CEC) [19] developed by the U.S.
Navy. This system consists of multiple radars collecting data
on air targets. Measurements are associated by a processing
node “with reporting responsibility” and shared with other
nodes that process all measurements of interest. Since all
nodes have access to essentially the same information, a
“common operating picture” essential for consistent military
operations is obtained. Other military sensor networks in-
clude acoustic sensor arrays for antisubmarine warfare such
as the Fixed Distributed System (FDS) and the Advanced
Deployable System (ADS), and unattended ground sensors
(UGS) [20] such as the Remote Battlefield Sensor System
(REMBASS) and the Tactical Remote Sensor System
(TRSS).
D. Sensor Network Research in the 21st Century
Recent advances in computing and communication have
caused a significant shift in sensor network research and
brought it closer to achieving the original vision. Small and
inexpensive sensors based upon microelectromechanical
system (MEMS) [21] technology, wireless networking, and
inexpensive low-power processors allow the deployment of
wireless ad hoc networks for various applications. Again,
DARPA started a research program on sensor networks to
leverage the latest technological advances.
The recently concluded DARPA Sensor Information
Technology (SensIT) program [22] pursued two key re-
search and development thrusts. First, it developed new
networking techniques. In the battlefield context, these
sensor devices or nodes should be ready for rapid de-
ployment, in an ad hoc fashion, and in highly dynamic
environments. Today’s networking techniques, developed
for voice and data and relying on a fixed infrastructure, will
not suffice for battlefield use. Thus, the program developed
new networking techniques suitable for highly dynamic
ad hoc environments. The second thrust was networked
information processing, i.e., how to extract useful, reliable,
and timely information from the deployed sensor network.
This implies leveraging the distributed computing environ-
ment created by these sensors for signal and information
processing in the network, and for dynamic and interactive
querying and tasking the sensor network.
SensIT generated new capabilities relative to today’s
sensors. Current systems such as the Tactical Automated
Security System (TASS) [23] for perimeter security are
dedicated rather than programmable. They use technologies
based on transmit-only nodes and a long-range detection
paradigm. SensIT networks have new capabilities. The
networks are interactive and programmable with dynamic
tasking and querying. A multitasking feature in the system
allows multiple simultaneous users. Finally, since detection
ranges are much shorter in a sensor system, the software and
algorithms can exploit the proximity of devices to threats to
drastically improve the accuracy of detection and tracking.
The software and the overall system design supports low
latency, energy-efficient operation, built-in autonomy and
survivability, and low probability of detection of operation.
As a result, a network of SensIT nodes can support detection,
identification, and tracking of threats, as well as targeting
and communication, both within the network and to outside
the network, such as an overhead asset.
III. T
ECHNOLOGY TRENDS
Current sensor networks can exploit technologies not
available 20 years ago and perform functions that were
not even dreamed of at that time. Sensors, processors, and
communication devices are all getting much smaller and
cheaper. Commercial companies such as Ember, Crossbow,
and Sensoria are now building and deploying small sensor
nodes and systems. These companies provide a vision of
how our daily lives will be enhanced through a network
of small, embedded sensor nodes. In addition to products
from these companies, commercial off-the-shelf personal
digital assistants (PDAs) using Palm or Pocket PC operating
systems contain significant computing power in a small
package. These can easily be “ruggedized” to become
processing nodes in a sensor network. Some of these devices
even have built-in sensing capabilities, such as cameras.
These powerful processors can be hooked to MEMS devices
and machines along with extensive databases and communi-
cation platforms to bring about a new era of technologically
sophisticated sensor nets.
Wireless networks based upon IEEE 802.11 standards
can now provide bandwidth approaching those of wired
networks. At the same time, the IEEE has noticed the low
expense and high capabilities that sensor networks offer.
The organization has defined the IEEE 802.15 standard
for personal area networks (PANs), with “personal net-
works” defined to have a radius of 5 to 10 m. Networks of
short-range sensors are the ideal technology to be employed
in PANs. The IEEE encouragement of the development of
technologies and algorithms for such short ranges ensures
continued development of low-cost sensor nets [24]. Further-
more, increases in chip capacity and processor production
capabilities have reduced the energy per bit requirement for
both computing and communication. Sensing, computing,
and communications can now be performed on a single chip,
further reducing the cost and allowing deployment in ever
larger numbers.
Looking into the future, we predict that advances in
MEMS technology will produce sensors that are even more
capable and versatile. For example, Dust Inc., Berkeley,
CA, a company that sprung from the late 1990s Smart
Dust research project [25] at the University of California,
Berkeley, is building MEMS sensors that can sense and
communicate and yet are tiny enough to fit inside a cubic
millimeter. A Smart Dust optical mote uses MEMS to aim
submillimeter-sized mirrors for communications. Smart
Dust sensors can be deployed using a 3
10 mm “wavelet”
1250 PROCEEDINGS OF THE IEEE, VOL. 91, NO. 8, AUGUST 2003
Table 2
Three Generations of Sensor Nodes
Fig. 2. Three generations of sensor nodes.
shaped like a maple tree seed and dropped to float to the
ground. A wireless network of these ubiquitous, low-cost,
disposable microsensors can provide close-in sensing
capabilities in many novel applications (as discussed in
Section IV).
Table 2 compares three generations of sensor nodes; Fig. 2
shows their sizes.
IV. N
EW APPLICATIONS
Research on sensor networks was originally motivated by
military applications. Examples of military sensor networks
range from large-scale acoustic surveillance systems for
ocean surveillance to small networks of unattended ground
sensors for ground target detection. However, the avail-
ability of low-cost sensors and communication networks has
resulted in the development of many other potential applica-
tions, from infrastructure security to industrial sensing. The
following are a few examples.
A. Infrastructure Security
Sensor networks can be used for infrastructure security
and counterterrorism applications. Critical buildings and
facilities such as power plants and communication centers
have to be protected from potential terrorists. Networks of
video, acoustic, and other sensors can be deployed around
these facilities. These sensors provide early detection of
possible threats. Improved coverage and detection and a
reduced false alarm rate can be achieved by fusing the data
from multiple sensors. Even though fixed sensors connected
by a fixed communication network protect most facilities,
wireless ad hoc networks can provide more flexibility and
additional coverage when needed. Sensor networks can also
be used to detect biological, chemical, and nuclear attacks.
Examples of such networks can be found in [26], which also
describes other uses of sensor networks.
B. Environment and Habitat Monitoring
Environment and habitat monitoring [27] is a natural can-
didate for applying sensor networks, since the variables to be
monitored, e.g., temperature, are usually distributed over a
large region. The recently started Center for Embedded Net-
work Sensing (CENS) [28], Los Angeles, CA, has a focus on
environmental and habitat monitoring. Environmental sen-
sors are used to study vegetation response to climatic trends
and diseases, and acoustic and imaging sensors can identify,
track, and measure the population of birds and other species.
On a very large scale, the System for the Vigilance of the
Amazon (SIVAM) [29] provides environmental monitoring,
drug trafficking monitoring, and air traffic control for the
Amazon Basin. Sponsored by the government of Brazil, this
large sensor network consists of different types of intercon-
nected sensors including radar, imagery, and environmental
sensors. The imagery sensors are space based, radars are lo-
cated on aircraft, and environmental sensors are mostly on
the ground. The communication network connecting the sen-
sors operates at different speeds. For example, high-speed
networks connect sensors on satellites and aircraft, while
low-speed networks connect the ground-based sensors.
C. Industrial Sensing
Commercial industry has long been interested in sensing
as a means of lowering cost and improving machine (and
perhaps user) performance and maintainability. Monitoring
machine “health” through determination of vibration or
wear and lubrication levels, and the insertion of sensors
into regions inaccessible by humans, are just two examples
of industrial applications of sensors. Several years ago,
the IEEE and the National Institute for Standards and
Technology (NIST) launched the P1451 Smart Transducer
CHONG AND KUMAR: SENSOR NETWORKS: EVOLUTION, OPPORTUNITIES, AND CHALLENGES 1251