10-05-2013, 12:46 PM
Exploiting Data Fusion to Improve the Coverage of Wireless Sensor Networks
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Abstract
Wireless sensor networks (WSNs) have been increasingly
available for critical applications such as security surveillance
and environmental monitoring. An important performance measure
of such applications is sensing coverage that characterizes how
well a sensing field is monitored by a network. Although advanced
collaborative signal processing algorithms have been adopted by
many existing WSNs, most previous analytical studies on sensing
coverage are conducted based on overly simplistic sensing models
(e.g., the disc model) that do not capture the stochastic nature of
sensing. In this paper, we attempt to bridge this gap by exploring
the fundamental limits of coverage based on stochastic data fusion
models that fuse noisy measurements of multiple sensors. We
derive the scaling laws between coverage, network density, and
signal-to-noise ratio (SNR). We show that data fusion can significantly
improve sensing coverage by exploiting the collaboration
among sensors when several physical properties of the target signal
are known.
INTRODUCTION
RECENT years have witnessed the deployments of
wireless sensor networks (WSNs) for many critical
applications such as security surveillance [1], environmental
monitoring [2], and target detection/tracking [3]. Many of these
applications involve a large number of sensors distributed in
a vast geographical area. As a result, the cost of deploying
these networks into the physical environment is high. A key
challenge is thus to predict and understand the expected sensing
performance of these WSNs. A fundamental performance
measure of WSNs is sensing coverage that characterizes how
well a sensing field is monitored by a network. Many recent
studies are focused on analyzing the coverage performance of
large-scale WSNs [4]–[10].
RELATED WORK
Many sensor network systems have incorporated various
data fusion schemes to improve the system performance.
In the surveillance system based on MICA2 motes [1], the
system false alarm rate is reduced by fusing the detection
decisions made by multiple sensors. In the DARPA SensIT
project [11], advanced data fusion techniques have been employed
in a number of algorithms and protocols designed for
target detection [3], [13], localization [14], [15], and classification
[11], [12]. Despite the wide adoption of data fusion in
practice, the performance analysis of large-scale fusion-based
WSNs has received little attention.
There is vast literature on stochastic signal detection based
on multisensor data fusion. Early works [22], [23] focus on
small-scale powerful sensor networks (e.g., several radars). The
theories on decentralized detection are surveyed in [24]. Recent
studies on data fusion have considered the specific properties
of WSNs such as sensors’ spatial distribution [11], [12], [16],
limited sensing/communication capability [13], and sensor
failure [25].
Data Fusion Model
Data fusion can improve the performance of detection systems
by jointly considering the noisy measurements of multiple
sensors. There exist two basic data fusion schemes—namely,
decision fusion and value fusion. In decision fusion, each sensor
makes a local decision based on its measurements and sends
its decision to the cluster head, which makes a system decision
according to the local decisions. The optimal decision fusion
rule has been obtained in [22]. In value fusion, each sensor
sends its measurements to the cluster head, which makes the
detection decision based on the received measurements. In this
paper, we focus on value fusion, as it usually has better detection
performance than decision fusion [23].We will discuss how
to extend the results of this paper to address a decision fusion
model in Appendix-E of the supplementary file.
COVERAGE UNDER PROBABILISTIC DISC MODEL
As the classical disc model deterministically treats the detection
performance of sensors, existing results based on this
model [4]–[10], [29], cannot be readily applied to analyze
the performance or guide the design of real-world WSNs. In
this section, we extend the classical disc model based on the
stochastic detection theory [23] to capture several realistic
sensing characteristics and study the -coverage under the
extended model.
In the probabilistic disc model, we choose the sensing range
such that: 1) the probability of detecting any target within the
sensing range is no lower than ; and 2) the false alarm rate is
no greater than . As the probabilistic disc model ignores the
detection probability outside the sensing range of a sensor, the
detection capability of a sensor under this model is lower than in
reality. However, this model preserves the boundary of sensing
region defined in the classical disc model. Hence, the existing
results based on the classical disc model [4]–[10], [29] can be
naturally extended to the context of stochastic detection.
IMPACT OF DATA FUSION ON COVERAGE OF
RANDOM NETWORKS
Many mission-critical applications require a high level of
coverage over the surveillance region. As an asymptotic case,
full coverage is required, i.e., any target/event present in the
region can be detected with a probability of at least while
the false alarm rate is below . For random networks, a higher
level of coverage always requires more sensors. Therefore, the
network density for achieving full coverage is an important cost
metric for mission-critical applications.
IMPACT OF DATA FUSION ON COVERAGE OF REGULAR
AND MOBILE NETWORKS
It has been shown that random network deployments can
lead to undesirable overprovision of sensing coverage [18],
i.e., many fully covered areas have redundant sensors. In
Section VII-A, we will study the coverage of regular networks,
in which sensors are deployed at grid points. Our analysis
shows that the data fusion can still reduce the network density
for achieving full coverage of regular networks. Recent
works [18], [34], [35] show that mobility can be introduced
to trade with network density in achieving coverage. In such
a scheme, randomly distributed mobile sensors relocate themselves
to fill coverage holes in the initial network deployment.
In Section VII-B, we will extend a relocation strategy proposed
in [18] to the data fusion model. Our analysis shows that data
fusion results in lower network density without increasing the
moving distance of mobile sensors.
Full Coverage of Mobile Networks With Limited Mobility
Recent works [18], [34], [35] have exploited limited sensor
mobility to reduce the network density for achieving full
coverage under the disc model. In such a scheme, randomly
distributed mobile sensors relocate themselves to fill coverage
holes in the initial network deployment. In this section, we first
extend an existing mobile relocation strategy [18] to the probabilistic
disc and data fusion models, respectively, such that the
relocated networks provide full -coverage. Moreover, we
show that data fusion can reduce the network density without
increasing the moving distance of sensors.
CONCLUSION
Sensing coverage is an important performance requirement
of many critical sensor network applications. In this paper, we
explore the fundamental limits of coverage based on stochastic
data fusion models that jointly process noisy measurements of
sensors. The scaling laws between coverage, network density,
and SNR are derived. Data fusion is shown to significantly improve
sensing coverage by exploiting the collaboration among
sensors. Our results help understand the limitations of the existing
analytical results based on the disc model and provide key
insights into the design and analysis of WSNs that adopt data fusion
algorithms. Our analyses are verified through simulations
based on both synthetic data sets and data traces collected in a
real deployment for vehicle detection.