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
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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