IEEE - Aerospace and Electronic Systems - October 2023 - 10

Monitoring of Critical Undersea Infrastructures: The Nord Stream and Other Recent Case Studies
remotely operated vehicles (ROVs) and autonomous
underwater vehicles (AUVs) [34]. The former ones are
tethered to ships or marine platforms and operated from
above the water's surface. The tether allows operators to
receive sensor data, e.g., sea-bottom images from cameras,
and, if needed, guide remotely the ROVs almost in real
time; however, the operation range is limited by the tether's
length. On the other side, AUVs are untethered and computer
controlled, with little or no operator interaction while
performing their preprogrammed subsea mission; multiple
AUVs can also cooperatively form an intelligent sensing
network for the monitoring oflarge regions ofinterest [35],
[36]. Even though AUVs can be programmed to survey
larger areas than ROVs, their operating range is dependent
on the duration of their batteries. Furthermore, underwater
communications, commonly exploiting the sound channel,
are unreliable and characterized by limited bandwidth and
range, thus limiting the ability ofAUVs to effectively share
sensor information in real time. Therefore, the choice
among ROVs and AUVs is dependent on the mission
requirements as well as on maximum range, operating
depth, time to cover a required distance, and type and size
of sensors they could bring on board, e.g., acoustic, magnetic,
optical, and oceanographic.
Since optical and EM waves do not propagate well in
seawater, acoustic sonars, which employ sound waves to
detect and consecutively localize underwater objects, are
nowadays the most common technology for undersea surveillance.
Passive sonars rely on the reception and processing
of acoustic information that is radiated by
underwater noise sources e.g., the noise produced by ships
or submarines propellers; active sonars, instead, send an
acoustic waveform and process the signals reflected by
underwater objects. Synthetic aperture sonars (SAS) [37]
represent an established technology to collect high-resolution
images of the seabed and underwater infrastructures.
Similarly to SAR, an SAS continuously transmits acoustic
signals and combines successive received pulses reflected
by an object or a surface along a known track to create a
2D image of the illuminated area.
DISTRIBUTED ACOUSTIC SENSING (DAS)
DAS is an emerging technology which is commonly
employed for the detection and analysis of seismic waves
on the ocean bottom and for submarine structural characterization
[38], [39]. It is enabled by fiber optic installed along
underwater infrastructures, that continuously allows the
monitoring in real time of underwater assets. While traditional
monitoring systems rely on discrete sensors measuring
at prefixed points, the fiber optic cable enables a
continuous monitoring along a very long portion of the
underwater infrastructure. Even though current commercial
DAS systems allow a thorough monitoring along a
10
maximum distance of 50 km, recent studies have shown
that persistent monitoring could be enabled up to a hundred
kilometers. The most common DAS technologies are based
on phase sensitive optical time domain reflectometer
(f-OTDR) and coherent OTDR [40]. A DAS interrogator
unit generates a series of laser pulses, sends them through
the optical fiber cable, and collects the backscattering of
the light along the length of the fiber. The analysis of the
backscattered signal by means of classification algorithms
allows to detect and locate events, such as leaks, intrusion
activities, cable faults, or other anomalous events.
CONTEXTUAL INFORMATION
Contextual information is generally intended as information
that does not directly refer to the assets under surveillance,
but to their surroundings. Contextual information
adds to the operational picture the clarity that is needed to
drive the actions to be taken. As such, contextual information
is seldom conveyed by a single piece of information
alone, but is rather derived from a mixture of experience,
domain knowledge, and data artifacts. In the MS setting,
examples of contextual information include geographic
databases, such as the bathymetry and the displacement of
critical infrastructures; geospatial information, such as
meteorology and oceanography; intelligence reports, comprising
HUMINT and OSINT; reports on business ownership
structures, sanctions, and criminal behavior of ship
owners; and derived information from past/historical data,
such as maritime patterns-of-life (POLs). To make a practical
example, a vessel's trajectory could raise major concern
if the vessel was previously involved in criminal
activities, if only opaque ownership-related information of
the vessel is available, or if there is evidence of the vessel
deploying specialized equipment in proximity of sensitive
infrastructures. Moreover, taking into account bathymetry
is crucial, since the difficulty and risk in performing sabotage
operations are directly proportional to the sea depth.
Furthermore, POLs can be considered as particular sets of
behaviors and movements, e.g., waiting, navigating, or
drifting, associated with specific entities, e.g., fishing vessels,
cargo vessels, and oil tankers, over a defined period
of time. In this context, density maps, built using historical
AIS data in a given time interval and area, provide a preliminary
insight on the most common POLs. This crucial
information can be used for a preliminary classification of
AIS trajectories. In fact, a selected AIS trajectory can be
considered more or less suspect depending on whether its
behavior seems compatible with the detected POLs in the
given period of time and area. Figure 3(a) shows a density
map of the entire maritime traffic in the Baltic Sea, built
using AIS data collected from 1 September to 15 September,
2022. Purple patterns highlight the most common maritime
routes in the considered region. The same data have been
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OCTOBER 2023

IEEE - Aerospace and Electronic Systems - October 2023

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