Tracking Moving Targets in a Smart Sensor Network
Department of Electrical & Computer Engineering
and Computer Science
University of Cincinnati
Cincinnati, OH 45221-0030
Samir R. Das
Computer Science Department
SUNY at Stony Brook
Stony Brook, NY 11794-4400
Contact Email: firstname.lastname@example.org
Networks of small, densely distributed wireless sensor nodes are capable of solving a va-
riety of collaborative problems such as monitoring and surveillance. We develop a simple
algorithm that detects and tracks a moving target, and alerts sensor nodes along the projected
path of the target. The algorithm involves only simple computation and localizes communica-
tion only to the nodes in the vicinity of the target and its projected course. The algorithm is
evaluated on a small-scale testbed of Berkeley motes using a light source as the moving target.
The performance results are presented emphasizing the accuracy of the technique, along with
a discussion about our experience in using such a platform for target tracking experiments.
Rapid advances in miniaturization in computing and sensor technologies and advent of low-power
short-range radios recently have given rise to strong interest in smart sensor networks [8, 7]. The
idea is to be bring together sensor nodes with on-board processing capability and radio interface
into a large network to enable them to process higher level sensing tasks in a co-operative fash-
ion. Several new design themes have emerged for such networks. For example, the network must
be fully self-configuring and highly fault-tolerant as the sensors may be deployed in an “ad hoc”
fashion. The network must minimize battery power usage; this enables untethered and unattended
operations for an extended time. A corollary of the latter property is that the system must lever-
age data processing and decision making ability inside the network as much as possible, instead
of shipping the data to a central controller to make decisions. This is because with current day
technology, the power budget for communication is many times more than that for computation.
An emerging application area for smart sensor networks is intelligent surveillance or monitor-
ing. Sensors are distributed, likely randomly, in a geographic area to be monitored. The goal is to
track and predict the movement of an appropriate target and alert the sensors which are close to
the predicted path of the target. The target can be a moving vehicle, for example, or can be a phe-
nomenon such as an approaching fire. It is assumed that each individual sensor node is equipped
with appropriate sensory device(s) to be able to detect the target as well as to estimate its distance
based on the sensed data. The sensors that are triggered by the target collaborate to localize the
target in the physical space to predict its course . Then the sensor nodes that lie close to the
predicted course of the target are alerted. This alert is meant to serve as a trigger for these nodes
to activate additional on-board sensors. For example, these additional sensors may be of a differ-
ent modality (e.g., alerts coming from heat sensors activating vibration sensors) that are ordinarily
turned off or not sampled to conserve power. The alert can also serve as a trigger to actuate certain
on-board devices, depending on the capability of the nodes and the application.
The goal of this paper is to develop techniques for the above moving target tracking problem
and report our experience in testing them in a live low-cost sensor network testbed using Berkeley
motes . Variations of these motes have been used in several experimental testbeds recently.
See, for example, [6, 14, 9, 16]. We will demonstrate the feasibility of our approach. We will also
discuss performance results, and several practical problems a designer/implementor must be aware
The paper is organized as follows. In the next section we describe the hardware and software
architecture of the sensor nodes used. The tracking algorithm is described in Section 3, and exper-
imental evaluation is reported in Section 4. In Section 5, we discuss the problems we faced in our
experiments and some ways to alleviate them. We conclude in Section 6.
2 Sensor Network Testbed
Our testbed comprises of 17 Berkeley motes  based on the MICA platform and manufactured
by Crossbow technology . For sake of completeness, we briefly review the hardware and soft-
ware architecture of these motes.
2.1 Hardware Overview
In order to create a low-power system, MICA platform is based on a single central microcontroller
that performs all sensing, communication and computation tasks. MICA motes use ATmega 128L
processor by Atmel , that is driven by a 8 MHz external crystal and has a 8-bit data bus. It
has 128 KB of program memory and 4 KB of data SRAM. There is a 10-bit internal ADC that is
directly connected to the sensors. Hence a 10 bit reading is obtained from the sensors in the range
0-1023. This will later be used to measure sensor signal strength.
The RF module consists of an RF Monolithics 916.50 MHz transceiver (TR1000) . It can
be externally controlled, through a potentiometer, to have a communication radius ranging from a
few inches to tens of yards, and can operate at data rates up to 115 kbps. A key characteristic of
the radio is that it only consumes 12 mA while transmitting and 5 mA while receiving. An antenna
for the radio has been integrated into the surface of the printed circuit board and connecting an
external antenna is optional. The system is designed to operate off a pair of AA batteries.
The medium access protocol used is a variant of the Carrier Sense Multiple Access (CSMA)
protocol . There is a random delay before the transmission of every packet. If the channel is
busy, the node backs-off for a random amount of time. During backoff, the radio is powered off
to save energy and no communication is possible during this period. This MAC protocol does not
have maximum number of backoffs, and keeps trying until a clear channel is found.
The I/O subsystem consists of a 51-pin expansion connector. It helps in interfacing the mi-
crocontroller with the sensing board. This connector is also used to program the microcontroller
by connecting it to the programming board, which in turn is connected to the parallel port of the
PC. The connector also provides a serial port interface to communicate with the MICA mote and
hence can be used to transfer data to PC and debugging. Three LEDs present on the board act as
actuators that can be controlled through software and act as a user interface.
2.2 Software Architecture
The key requirements of an operating system that run on resource-constrained sensor nodes are (i)
smallmemory footprint, and (ii) effectivemanagement of hardware in terms of power consumption
and processing time.
MICA motes run on an event-based operating system, called TinyOS . TinyOS fits in 178
bytes of memory. It manipulates the hardware directly and there is no kernel layer. To avoid the
overhead associated with context switch and process management, only one process can be active
in the system at one time. There is no dynamic memory allocation and the memory is allocated
at compile time. The TinyOS code and applications are compiled together and run in a single
linear address space. This reduces the memory management overhead. TinyOS is divided into a
collection of software components. The complete system software consists of a scheduler and an
interconnection of these components. An application may consist of a number of layers of these
componenets stacked on top of each other. There are three types of components:
? Hardware Abstraction Components: These components directly map to a physical device
(such as LEDs, UART, ADC) and are used to manipulate them.
? Synthetic Hardware Components: These components simulate the behavior of hardware, in
case the hardware is absent in the mote.
? High Level Components: These components perform various data manipulations and trans-
formations, such as a routing algorithm or any other application.
TinyOS provides two levels of scheduling - Events and Tasks. This helps to do all the pro-
cessing in real-time. Events are synchronous in that they are serviced to completion as soon as
they occur. This includes interrupts from the hardware. Events cannot be preempted. Tasks are
asynchronous and involve time-consuming computations. Tasks are managed by a scheduler that
schedules these tasks when no event is to be processed. Events can preempt a task.
For communication, a fixed size of 38-byte packets are used in TinyOS, with 30 bytes of pay-
load. Message header includes a 16-bit CRC error checking. A mote is uniquely identified by a
16-bit ID that is assigned while uploading the application code to the mote. Each message contains
the destination mote ID. Two special IDs - 0xffff and 0x7e - are reserved for broadcast and UART,
respectively. A message destined for UART is sent to the serial port.
A message-based model called Active Messages  is used by MICA motes to communicate
with each other. Each message contains the name of a user-levelhandler to be invoked on the target
mote, and a data payload that is passed as arguments. If no message handler is intended for the
message, the message is discarded without further processing.
3 Tracking Moving Targets
We assume that the sensor nodes are scattered randomly in a geographical region. Each node is
aware of its location. Location information can be gathered using an on-board GPS receiver. Ab-
solute location information is, however, not needed. It is sufficient for the nodes to know their
location with respect to a common reference point. Many localizing techniques can be used with
varying degree of hardware complexity and accuracy. See, for example, [16, 5]. The sensor nodes
are stationary in our model; this makes the localization problem somewhat simpler. Since the work
presented here is not dependent on any particular localization method used, we do not emphasize
any particular technique. In the experiments reported, we have directly encoded the location infor-
mation into the sensor nodes to eliminate the possibility of any localization error.
The sensors must be capable of estimating the distance of the target to be tracked from the
sensor readings. It is assumed that the sensor has already learned the sensor reading to distance
mapping. We conducted a separate set of experiments to determine this mapping and encoded the
mapping directly as a table in the application component.
Tracking a target involves three distinct steps:
1. Detecting the presence of the target.
2. Determining the direction of motion of the target.
Figure 1: Three distance estimates
location coordinates result in three circles. Two straight lines are drawn through the points of
intersection of two pairs of such circles. The target is localized at the intersection (?) of these two
?, respectively, with known
3. Alerting appropriate nodes in the network.
These steps are discussed in detail in the following subsections.
Each node periodically (every 1 sec in our experiments) polls its sensor module to detect the
presence of any target to be tracked. Sensor reading above a particular threshold indicates the
presence of a target in the vicinity. As soon as this threshold is crossed, a TargetDetected message
is broadcast by the node. Each TargetDetected message contains the location of the originating
node and its distance from the target, as determined from the sensor reading. When this message is
received by a neighboring node, it stores the coordinates of the originator and the target’s distance
from the originator in a table. Table entries expire after a timeout (4 sec in our experiments) unless
The next step is estimating the location of the target. A minimum of three nodes sensing the target
are needed to apply the commonly used triangulation method . See Figure 1 for an explanation
of how we used it. When a node that has already detected the target hears two additional Target-
Detected messages from two different neighbors, it computes a location estimate via triangulation.
Note that any node that hears three TargetDetected messages from three different neighbors can
estimate the location of the target. However, we limit this computation only to the nodes that
themselves have detected the target, and hears from two other neighbors that also detected the tar-
get. This limits the estimation to be done only in nodes within a close vicinity of the target, thus
localizing the computation.
In order to estimate the trajectory of the target, its location must be estimated at a minimum
of two instants of time. A straight line through these two points defines the trajectory in the
direction of the latest location estimate. We found that with only two estimates, the impact of any
estimation error was significant. Three or more estimates, however, worked significantly better.
In the experimental results that follow we used three estimates with linear regression to compute
a best-fit straight line. This line defines the estimated trajectory of the target. Note that more
estimates, along with a higher-order curve fitting, will improve accuracy further. More estimates,
however, will require a larger network to experiment with. For better accuracy, location estimates
are used for trajectory estimation only when they are separated by at least a minimum distance (3
inches in our experiments).
After estimatingthetrajectory, thenetworkmustalert nodesthatlienear thetrajectory (specifically,
within a perpendicular distance
Warning message so that they are aware of the approaching target and can take appropriate actions.
Any node that is able to estimate the trajectory by using three location estimates broadcasts a
Warning message. The message contains the location of the sender and parameters describing the
equation of the straight line trajectory. Any node receiving the Warning message rebroadcasts it, if
it is located within a distance
Care must be taken to prevent propagation of this warning message in the direction opposite to
the direction of motion of the target. This is done via some simple geometric considerations. The
node receiving a Warning message computes, through itself, a line perpendicular to the trajectory.
The line divides the geographic area into two regions
motion. A node forwards the Warning message if (i) it lies within a distance
and (ii) the Warning message is received from a node in region
only the nodes within
warning. This localizes message propagation only in the relevant part of the network.
Note that the above technique assumes that the network has enough density such that the subset
of the sensor network nodes, that lie in the region where warning message must be propagated,
must form a connected graph among themselves. This condition is needed as the warning message
propagation is suppressed outside this region. Without this assumption, simply larger regions need
to be flooded with warning messages.
Note that multiple nodes may originate Warning messages for the same detected target. This
is because any node detecting an target independently attempts to carry out location and trajectory
estimations. To conserve bandwidth and power, we stipulate that a node refrain from forwarding
? from it,
? being 5 inches in our experiments) by sending them a
? from the trajectory.
?being towards the direction of
? from the trajectory,
?. See Figure 2. This ensures that
? distance from trajectory and towards the direction of motion forward the
= Sensor Node
Figure 2: Sensor network with a moving target.
?defines the trajectory of the moving
a Warning message for some time (20 sec in our experiments) after it has forwarded one. This
also implicitly assumes the presence of only one source in the network at any time. Detecting the
presence of multiple sources and tracking them on an individual basis will require sophisticated
sensing and signal processing algorithms  that is beyond the scope of our current work.
4 Experimental Evaluation
In our experimentsthe movingtarget is a lightsource (bulbof a flashlight, taken out of the casingto
minimize shadows and operated using four AA batteries). The experimental platform is a 60 inch
? 60 inch square area with 16 motes placed at random locations. Another mote is used as a probe
to capture all packets transmitted in the network for debugging and tracing functions. The probe
does not participate in the algorithm. Recall that due to the absence of any localization system, the
locations are encoded in the motes before the start of the experiment.
4.1 Characteristics of the Photo Sensor
We first performed a set of experiments to determine the relationship of the sensor reading with the
distance of light source. This relationship would later be used to estimate the target distance from
sensor readings. We faced several complications here. First, different sensors generated different
Distance of base of light source from sensor node (inches)
468 10 1214
Photo sensor reading (arbitrary units)
Figure 3: Relationship of the “average” sensor readings with the distance of the light source. Three
different sensors are shown to point out the difference in sensor characteristics.
readings for the same distance of the light source. The readings was variant enough that we felt
some calibration would be necessary to reduce errors. While statistical methods using parameter
estimation techniques such as reported recently in  could be used, we chose to determine the
exact sensor reading versus distance relation for each individual sensor. This was feasible as we
were dealing with a small number of sensors. Second, we found that light falling on the photo-
sensor at an angle made the reading sensitive to the direction of the light source relative to the
sensor. To reduce this sensitivity, we experimented with the light source at an elevated plane
(at a height of 9 inches). This also resolved shadowing problems at small heights caused by the
hardware components on the mote. The light source was always kept at the same height as we are
interested in solving the tracking problem in two dimensions only.
Figure 3 shows the average sensor reading vs. distance plots for three different sensors. Notice
the differences in sensor characteristics. We tested all individual sensors and encoded the sensor
reading versus distance function in their application components.
4.2 Target Tracking
Experiments are performed by randomly placing 16 motes in a 60 inch
room. A small area is chosen intentionally so that the experiments can be performed on a table top;
this keeps the experiments manageable. A threshold of 15 inches is used for the distance of the
light source; beyond this distance the sensor reading is assumed too low to be reliable. The target
? 60 inch area in a dark
Figure 4: The sensor network setup, showing the probe node connected to the laptop and the
elevated light source.
must be closer than this distance for a sensor to be able to detect it.
A probe mote is kept inside the experimental region such that it can hear packets transmittedby
all motes in the network. This probe is used to monitor all network traffic, and used for debugging
and tracing activities. The probe passively listens to all transmissions and does not transmit any-
thing. The packets gathered by the probe are transmitted via a serial interface to a laptop computer.
See the picture of our experimental setup in Figure 4. Many random placements of sensors are
used as test cases; however, because of space limitations results from only a few placements are
In the first set of experiments, we evaluate the location estimation error. To evaluate the error,
the light source is placed at approximately 5 inch intervals spanning the whole region in a grid-like
fashion, and its location is estimated by three sensors exactly as described previously. The error
(i.e., distance) between the actual and estimated location is computed. The average and standard
deviation of the error is presented in Table 1. Note that the average error is small (less than 2.5
inches), while the standard deviation of the error is relatively high. This is due to the fact that the
error due to orientation of the light source relative to the photo sensor is not completely eliminated
even with the elevated light source. A secondary reason is the inherent variability of the sensor
The next set of experiments evaluates the performance of the tracking algorithm itself. Here,
for each random placement of the motes, we perform a series of experimentswhere the light source
is dragged slowly along a straight line path in the experimental region. The network responds by
predicting the trajectory and lighting the LEDs in the motes which receive Warning messages. A
Placement Avg. estimation error
Std. dev. of error
Table 1: Location estimation error statistics for three different random mote placements.
large number of experiments are performed; but only a few are reported here for brevity. The
nature of these experiments is such that it is hard to present the results in a quantitative fashion for
readers to get a fair idea of the performance. So we have chosen to present the results in a visual
fashion in Figures 5 through 8. These figures are automaticallygenerated by a script running on the
trace of the packets gathered by the probe node and transmitted to the laptop. They are explained
Small rectangles indicate the position of sensor nodes. The light source representing the mov-
ing target is actually moved from point
Circles around some nodes indicate the distance at which the light source is being sensed by the
corresponding nodes. Circles are drawn only for the last time any node “sees” the source and sends
out TargetDetected message. The line through
mated trajectory. Thus, the difference between
following notations are used to designate nodes participating in warning message propagation:
?in the experiments, indicated by a line.
?(in the direction of
?) denotes the esti-
?denotes the estimation error. The
? “WO” denotes the node that originates the Warning message.
? “WR” denotes the node that receives a Warning message and lies within distance
timated trajectory, but does not forward the message because it lies in the direction opposite
to the direction of motion.
? of the es-
? “WF” denotes the node that receives a Warning message, lies within distance
timated trajectory and also forwards the message. WF nodes are highlighted by drawing a
rectangle around them.
? of the es-
As mentioned before, three different placements of motes are reported. For each of these
placements, the light source is slowly moved from point
were performed with different placements of motes and different movement paths of the light
source, for brevity only three sample results are shown in Figures 5 through 7. In Figure 5, note
that even though several motes originate a warning message, they all compute the same path. In
Figure 7, note that the predicted direction of motionis somewhatoff from the actual direction. This
is because of a large error in one or more of the three location samples that are used in estimating
the trajectory. The accuracy of the prediction can be improved naturally by taking many more
?. While many experiments
Figure 5: Tracking moving target: experimental scenario 1.
samples, which will, however, require “observing” the target (light source) for longer time. This
will also require larger experimental area and a larger number of motes.
We have noted several interesting scenarios in the cases we studied. One example is pre-
sented in Figure 8 where different trajectories are estimated by two originating nodes, because
they “heard” different sets of nodes that detected the target. The accuracies of the two estimates
are very different. We looked carefully into the traces of this scenario, and found that the location
estimation errors are not very high (maximum 3 inches); but biased errors for one estimation re-
sulted in a very different trajectory. Once again, larger number of samples with a larger network
should improve such situations significantly. The second problem in Figure 8 is that no warning
message is ever propagated. The reason is simply that there are no sensors within distance
ther of the predicted trajectory. This situation will trivially improve by choosing a denser network
to experiment with or choosing larger value of
? of ei-
It is worthwhile to outline here briefly the problems we faced in our experimental work using
low-cost sensor nodes, not capable of sophisticated signal processing.
The photo sensor used in MICA motes is sensitive to the angle at which light rays are incident
on the sensor. This makes estimating distance from sensor readings hard, as the angle influences
Figure 6: Tracking moving target: experimental scenario 2.
Figure 7: Tracking moving target: experimental scenario 3.
Figure 8: Tracking moving target: an interesting experimental scenario.
the reading. We also noticed that very small changes in the distance influenced sensor readings
significantly when the light source is near the sensor. This is because relative changes in the angle
is more significant at closer distance. An elevatedsource of lightreduces thisproblem, but does not
completely eliminate it – one reason for large variations in the errors in location estimates reported
in Table 1. In addition, it was hard for us to procure a light source which is truly omnidirectional in
nature. We observed variations in sensor readings in different directions even when elevation (and
hence angle) and the distance of the light source were kept constant. We conjectured that either
the light source or the sensor (or both) have some directional properties, which we ignored and
relied on average properties to predict distance from sensor readings. While these observations are
particular to light source and photo sensors on MICA motes, we believe some of these problems
will also confront designers when other signals and sensors (e.g., acoustic or magnetic) are used.
To ensure that the light source always emits light with the same power, we used freshly
recharged batteries for all experiments. While this is possible for controlled experiments as re-
ported here, it will be impossible in real scenarios. So we conjecture that estimating distance of
the target from a set of sensor readings will be difficult to impossible in general settings, as the
source signal cannot be always expected to be of a consistent strength. Unless the sensors are very
sophisticated, they will at best be programmed to simply detect presence or absence of a signal
(and not to estimate any distance), and then collaborate with neighboring sensors to increase the
confidence that an event or phenomenon has occurred in the vicinity. The strength of the signal can
be gathered indirectly by determining how many sensors can detect the signal and how spread out
they are geographically. We feel that this would be a reasonable approach to pursue, which will not
be dependent on omnidirectional signals of consistent strength. However, this simple technique,
for all practical usage, will require a large number of densely disposed sensors. This will be a
direction we plan to pursue in our future work.
We had to calibrate each sensor individually, so that the variability introduced by the manufac-
turing process that influences their sensitivity does not impact our experiments. This will be hard
to do for a very large number of sensors. Thus, sophisticated statistical methods such as reported
in  will need to be adopted.
In our experiments we have directly encoded the location of the sensor in its program. But
in real applications, they need to be localized. While many methods have been reported in recent
literature [5, 10, 16] – some of which applicable to MICA motes – they will all introduce their
own sources of error. A statistical analysis of errors has been done in . It will be interesting
to analyze the combined effect of two types of errors – localization errors of the sensor themselves
and errors in location estimates and trajectory computation of the moving target.
As discussed previously, a large number of location samples (we used only three in our ex-
periments) has a strong potential to reduce errors in estimating the trajectory. However, this will
require a larger testbed. Also, the times at which different sensor nodes are sampling the signal
are not synchronized. Thus, errors could be introduced when signals sampled at different times are
combined for locating the moving target, as the actual location of the target could change. This
error can be minimized by sampling sensors at a much higher rate relative to the maximum speed
of the target. Of course, time synchronization can also be introduced at the cost of higher design
complexity or possible power usage.
We have developed a simple algorithm for tracking moving targets in a smart sensor network.
The sensor nodes detect and track the moving target in a collaborative fashion and alert the nodes
near the predicted path of the target. The algorithm localizes the communication in the vicinity of
the location of the target and its estimated trajectory. This is critical as the sensor nodes usually
run on a low power budget. The strength of our work is that we implemented and evaluated the
algorithm in a real sensor network testbed using Berkeley motes. We used a moving light source
as target. We described several factors that influence the accuracy of target tracking using low-cost
sensor nodes such as the motes, and the potential problems a designer can face. The accuracy in
our experiments has been fairly good, but not excellent. The accuracy is greatly influenced by
the number of location estimation samples the designer can work with. With a small number of
motes and a small experimental area, we could use only a small number of such samples. Thus,
our experiments have a higher error margin than is possible to achieve. However, our experience
demonstrates strong potential for this approach for large and dense sensor networks. We are in the
process of acquiring a larger testbed that will make such large-scale experiments feasible.
Experimental research using distributed networks of smart sensors is in its infancy. While re-
search groups are working on algorithmic and performance aspects of collaborativesignal process-
ing in the target tracking arena [4, 13], experimental work focusing on localized communication
has not yet appeared in mainstream literature in our knowledge. We expect that our experience
will be useful to researchers pursuing research in this direction.
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