The Meandering Current Mobility Model and its
Impact on Underwater Mobile Sensor Networks
Abstract—Underwater mobile acoustic sensor networks are
promising tools for the exploration of the oceans. These networks
require new robust solutions for fundamental issues such as:
localization service for data tagging and networking protocols
for communication. All these tasks are closely related with
connectivity, coverage and deployment of the network. A realistic
mobility model that can capture the physical movement of the
sensor nodes with ocean currents gives better understanding
on the above problems. In this paper, we propose a novel
physically-inspired mobility model which is representative of
underwater environments. We study how the model affects a
range-based localization protocol, and its impact on the coverage
and connectivity of the network under different deployment
scenarios.
I. INTRODUCTION
Sensor networks represent a new remote monitoring and
control technology, and recently, have become a promising
technology for underwater environment monitoring.
The idea of applying sensor networks into underwater
environments, forming underwater sensor networks (UWSN)
started an exciting research area, attracting a growing interest
from the network community. These networks are envisioned
to enable new applications including: military underwater
surveillance, oceanographic data collection, ecology (e.g. pol-
lution, water quality and biological monitoring), public safety
(e.g. disaster prevention, seismic and tsunami monitoring),
industrial (offshore exploration).
Recent works have addressed some of the challenges pre-
sented by underwater sensors [1]–[3]. Since UWSN is an
emerging topic, up to now, most of the researches have mainly
focused on fundamental sensor networking problems such
as data gathering [4], synchronization [5], localization [6],
routing protocols [7], [8], energy minimization and MAC
[9], [10] issues. Various architectures have been proposed for
UWSN, they can be classified in the following groups: i) ocean
floor embedded sensor networks [1], ii) UWSNs with sensors
attached either to anchors on the ocean floor [1] or to surface
moorings [11], iii) hybrid architectures [12] iv) Autonomous
Underwater Vehicle (AUV) aided UWSNs where AUVs are
used for additional support in any of the above architectures
[13] v) networks with free-floating sensors (mobile underwater
sensor networks) [14].
Currently, only sensors without networking capability are
widely used in oceanographic research. These sensors are
used in two distinct and complementary ways to perform
measurements in the oceans; Eulerian and Lagrangian. In the
Eulerian approach data are taken at positions that do not
change in time (e.g. from a mooring or from a ship standing
still with respect to the bottom). In the Lagrangian approach,
data are taken from autonomous devices that passively fol-
low the ocean currents, for a review see [15]. Lagrangian
autonomous devices (usually named floats or drifters)give
unique insights into the structure and patterns of ocean flows,
at many different temporal and spatial scales. An operational
forerunner of future global arrays of lagrangian devices is the
Argo project: a set of thousands of free-drifting profiling floats
that measure temperature, salinity, and velocity of the ocean
water [16].
Although the devices in use today are not able to com-
municate with each other, there is a growing trend of using
lagrangian devices for monitoring regional and coastal areas
[17]. In those settings the small distance between the devices
makes it possible to acoustically interconnect them and deploy
them as underwater mobile acoustic sensor networks.
Terrestrial sensor networks generally assume fairly dense
deployment with continuously connected coverage of an area
using inexpensive, stationary nodes. In contrast with this, eco-
nomics push underwater networks toward sparse and mobile
deployments. A recent survey [2] on underwater networks
highlights the importance of sparse and mobile networks due
to the immense volume of the underwater domain.
In this paper, we study underwater mobile acoustic sensor
networks that consist of free-floating sensors with network-
ing capability. We present a mobility model for underwa-
ter environments, the Meandering Current Mobility model
(MCM hereafter). This model considers sensors moving by
the effect of meandering sub-surface currents and vortices.
The domain model is representative of a large coastal en-
vironment. Therefore, unlike previous works, we assume a
domain spanning several kilometers. In this case, deployment
of the network with sensors uniformly distributed over this
large domain would be unrealistic. Instead, we consider an
initial deployment of nodes in a small subarea where they are
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE INFOCOM 2008 proceedings.
978-1-4244-2026-1/08/$25.00 © 2008 IEEE 771
released and thereafter move according to the mobility model.
This scenario is more realistic for underwater mobile sensor
networks applications, especially in monitoring the dynamics
of the oceans.
For any sensor network, the lifetime of the network is
usually defined as a set of application specific requirements:
the connectivity among nodes, the coverage of the network, i.e.
the fraction of the area where sensors can effectively collect
information and on the performances of network protocols. In
a mobile network connectivity and coverage vary when the
nodes move. Hence studying these metrics with a realistic
mobility model is essential. The performance of any protocol
is directly related with these issues. We study the dynamic cov-
erage and connectivity as a function of time under the MCM
model. We also consider the effect of different deployment
strategies on network coverage and connectivity.
Underwater sensor networks, like other sensor networks,
require a localization service in order to geo-reference each
measurement. We present a localization service, tailored to the
specifics of underwater sensor network, and study the effect
of the mobility model on the level of service provided by the
localization protocol.
The paper is organized as follows: In Section II, we define
a mobility model from oceanography that provides a good
degree of accuracy in modeling coastal deep water ocean
currents. In Section III, we present the network model, the
deployment process, the connectivity and coverage metrics. In
Section IV, we present the localization scheme. In Section V
we present the simulation outcomes and discuss the impact
of the mobility model on the connectivity, coverage and
localization using different deployment schemes. Section VI
draws the main conclusions and possible future works.
II. MOBILITY MODEL
In order to study the networking properties of intercon-
nected sensors, it is crucial to use a mobility model that takes
into account the fluid nature of the medium in which they
move. Almost all models in the existing literature on mobile
sensor networks assume that each sensor moves independently
from the others [18]–[20]. Typically, the path of each sensor
is taken as an independent realization of a given stochastic
process, such as a random walk, or a random way point
process. In a fluid, instead, the same velocity field advects
all the sensors. Their paths are deterministic (albeit often
chaotic), and strong correlations between nearby sensors must
be expected. Then, in order to simulate the movement of
sensors, one needs to model the movement of the ocean in
which they are immersed. This may be achieved in several
ways, with varying levels of realism.
On one hand, the latest advances in computational tech-
niques allow for very realistic but complex “ocean forecasts”,
similar to weather forecasts [21]; this approach, in addition
to the sheer computational cost of the simulation, requires
additional detailed knowledge of atmospheric forcing, bottom
topography and boundary conditions, which comes from ex-
tensive field observations.
On the other hand, progress in the understanding of la-
grangian transport have been made with a purely kinematic
approach, where a (reasonable) velocity field is prescribed
beforehand. For our applications, we exploit the fact that the
oceans are a stratified, rotating fluid, hence vertical movements
are, almost everywhere, negligible with respect to the hori-
zontal ones [22]. Thus we will assume that our lagrangian
sensors move on horizontal surfaces, and neglect their vertical
displacements. Models of this sort are very well known in fluid
dynamics, because they allow to describe the kinematics of
quasi-two-dimensional flows in a simple way, while retaining
a good level of realism. The book [23] is a general introduction
for the interested reader, while the very recent monograph [24]
focuses on geophysical applications.
In oceanography, the absence of vertical movements is a
design feature of drifters, where the sensors hang at a fixed
(small) depth under a buoyant object floating at the surface
[25], [26]. In the case of floats, the operating depths are usually
much larger, and there is no direct contact with the surface.
The hull of the device is built in such a way to maintain its
density almost constant, so that the float can be calibrated
to follow a precisely defined isopycnal surface
1
; in this case,
vertical movements of the float are usually limited to damped
oscillations around the reference density surface triggered by
internal waves [27].
Of course, in the presence of strong wind–driven upwelling
or downwelling, or during events of deep water formation,
or at the passage of exceptionally intense internal waves, the
assumption of negligible vertical motions ceases to be valid. In
our preliminary investigation, we feel appropriate to skip these
exceptional events and propose a model that mimics conditions
of ordinary water circulation.
Any incompressible, two-dimensional flow is described by
a streamfunction ψ from which the two components of the
divergenceless velocity field u ≡ (u,v) may be computed as: