04-03-2013, 01:05 PM
Ship Detection with Wireless Sensor Networks
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Abstract
Surveillance is a critical problem for harbor protection, border control or the security of commercial facilities. The effective
protection of vast near-coast sea surfaces and busy harbor areas from intrusions of unauthorized marine vessels, such as pirates
smugglers or, illegal fishermen is particularly challenging. In this paper, we present an innovative solution for ship intrusion detection.
Equipped with three-axis accelerometer sensors, we deploy an experimental Wireless Sensor Network (WSN) on the sea’s surface to
detect ships. Using signal processing techniques and cooperative signal processing, we can detect any passing ships by distinguishing
the ship-generated waves from the ocean waves. We design a three-tier intrusion detection system with which we propose to exploit
spatial and temporal correlations of an intrusion to increase detection reliability. We conduct evaluations with real data collected in our
initial experiments, and provide quantitative analysis of the detection system, such as the successful detection ratio, detection latency,
and an estimation of an intruding vessel’s velocity.
INTRODUCTION
INTRUSION detection on the sea is a critical surveillance
problem for harbor protection, border security, and the
protection of commercial facilities, such as oil platforms
and fisheries. The traditional methods of detecting ships
entail the use of radars or satellites which are very
expensive. Besides the high cost, satellite images are easily
affected by cloud cover, and it is difficult to detect small
boats or ships on the sea with marine radar due to the noise
or clutter generated by the uneven sea surface.
Terrestrial intrusion detection with Wireless Sensor Networks
(WSNs) have recently been developed [1], [2], [3].
These networks deploy magnetometers, thermal sensors, and
acoustic sensors in monitored areas to detect the presence of
intruders [1], [4]. Though such networks may work well on
the land, it is challenging to deploy these sensors on the sea
surface for ship detection.
DISTINGUISH BETWEEN SHIP-GENERATED
WAVES AND OCEAN WAVES
As mentioned in Section 1, node movement on the sea
surface makes it hard to detect an intrusion target.
Observing ship-generated waves, as shown in Fig. 1, we
intend to detect ships by detecting the ship’s waves.
Ship Wave Patterns and Wave Dissipation
When a ship moves across a surface of water, it generates
waves which comprise divergent and transverse waves as
shown in Fig. 2. Kelvin found that V-shaped patterns were
formed by two locus of cusps whose angle with the sailing
line is 19280 in deep water [7], and the angle between the
sailing line and the diverging wave crest lines at the cusp
locus line should be 54440. Note that this pattern is
independent of the size and velocity of the ship.
When the ship’s waves spread out sideways and
propagate from the sailing line, both the height and energy
of the waves decrease. The research in [15] pointed out that
the transverse waves decrease inversely proportional to the
square root of the distance from the vessel, which means
that transverse waves decline much faster than divergent
waves. In addition, when we observe ship-generated waves
at a fixed spatial point, the ship-generated wave train has a
limited duration [16].
Measure Waves with Accelerometers and the
Spectrum of the Ship Waves
The old method of measuring ship-generated waves is to
measure the pressure fluctuations at some elevation points
in the water column, then transform the pressure into wave
height [16]. However, this method requires expensive
equipment. In addition, it is difficult to deploy the devices
underwater. In this paper, we use accelerometers to measure
the actual surface movement of ship-generated waves.
When the accelerometer is used in an ocean environment,
the buoy and the accelerometer undergo a generally
oscillatory, sinusoidal-like vertical acceleration due to wave
action. The details of the experimental setup are in the
supplemental files, which can be found on the Computer
Society Digital Library at http://doi.ieeecomputersociety.
org/10.1109/TPDS.2011.274.
Node-Level Detection
At node-level detection, the task for a single node is to
detect ship waves generated by a nearby passing ship. In
order to do that, the individual node periodically samples
the event and processes the sampled data to extract features
for node-level detection. In our scheme, the node first
samples for a period of time after being deployed, then
filters out any frequencies above 1 Hz.
Since the z-accelerometer signal fluctuates around 1g, we
deduct this value and let the signal fluctuates around zero.
Before computing the average and standard deviation, we
have the absolute value of those signals below zero. The
reason being that, when the ship’s waves disturb the buoy,
all fluctuations either above 1g or below 1g contain the
disturbance information.
Cluster-Level Detection
Though a passing ship can be detected by an individual
node, many factors affect the detection results. For example,
wind may affect the sensors and cause a flurry of false
positives by directly moving sensors. Animals such as birds
or fish may also disrupt the sensor readings. In addition,
some nodes with hardware errors may not detect the ship
when it is passing. Even with perfect detection, its positive
report may not be transmitted back in a timely fashion due
to wireless communication errors [21] and possible network
congestions [20].
To improve the detection performance and decrease the
false positive rate, it is useful that multiple nodes cooperatively
detect the ship. In this section, we first present innetwork
data processing with spatial and temporal correlations
between nodes, and then estimate the speed of a
passing ship.
PERFORMANCE EVALUATION
In this section, we evaluate the detection system and
provide analysis based on the real data which we collected
in our initial experiments as shown in Fig. 6.
The experimental system is with 30 nodes deployed in a
grid fashion as Fig. 4 shows, five nodes in a row and a total
of six rows. The node’s deployment distance D is 25 m. The
ship travels along one side of the deployed area with three
speed levels about 10, 16, and 20 knots, and with each speed
the test runs 10 rounds. A more detailed description of the
experiments are in the online supplemental files.
RELATED WORK
There is numerous research for terrestrial intrusion detection,
classification, and target tracking [8], [9], [10], [11], [12],
[13], in which magnetometers, thermal sensors, or acoustic
sensors are deployed in a monitored area to detect intruders
[22], [23], [24]. Some researchers have also deployed a
number of successful real-world systems [1], [2], [3].
However, the sensors are mostly static after deployment,
and as mentioned in Section 1, when sensors are deployed
on the sea surface, they move randomly tossed by ocean
waves which makes it difficult for most sensors to detect
intrusion targets.
There is a small amount of research dealing with intrusion
detection on the water. In [25], Carapezza et al. describe a
coastal sensor network to detect, classify, and track submerged
objects that may pose a threat. The unattended inwater
sensors first perform the initial and coarse target
detection, then the shore-based optical sensors develop
refined tracks on the targets. Bunin et al. [26] developed an
experiment on the Hudson River Estuary to detect ships.
CONCLUSIONS AND FUTURE WORK
In this paper,wepresent a three-tier ocean intrusion detection
system by using accelerometer sensors to detect intrusion
ships.Wealso exploit the spatial and temporal correlations of
the intrusion to increase the detection reliability. We
conducted evaluations with real data collected by our initial
experiments, and provide analysis of the system.
Compared with traditional ship detection methods
which can monitor a large area (e.g., radars or satellites)
but cost a lot, our methods can be cheaper. Moreover, the
satellites cannot perform real-time monitoring. With radar,
we need some place to set up the equipment, and it is
difficult to detect small boats. The schemes with WSNs are
cheaper and can be deployed almost everywhere we want.
More importantly, it can perform situ real-time monitoring,
and provide more information of the monitored targets.