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Underwater Wireless Sensor Networks: How Do Acoustic Propagation Models Impact the Performance of Higher-Level Protocols?

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Several Medium Access Control (MAC) and routing protocols have been developed in the last years for Underwater Wireless Sensor Networks (UWSNs). One of the main difficulties to compare and validate the performance of different proposals is the lack of a common standard to model the acoustic propagation in the underwater environment. In this paper we analyze the evolution of underwater acoustic prediction models from a simple approach to more detailed and accurate models. Then, different high layer network protocols are tested with different acoustic propagation models in order to determine the influence of environmental parameters on the obtained results. After several experiments, we can conclude that higher-level protocols are sensitive to both: (a) physical layer parameters related to the network scenario and (b) the acoustic propagation model. Conditions like ocean surface activity, scenario location, bathymetry or floor sediment composition, may change the signal propagation behavior. So, when designing network architectures for UWSNs, the role of the physical layer should be seriously taken into account in order to assert that the obtained simulation results will be close to the ones obtained in real network scenarios.
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Sensors 2012, 12, 1312-1335; doi:10.3390/s120201312
sensors
ISSN 1424-8220
www.mdpi.com/journal/sensors
Article
Underwater Wireless Sensor Networks: How Do Acoustic
Propagation Models Impact the Performance of Higher-Level
Protocols?
Jesús Llor * and Manuel P. Malumbres
Physics and Computer Engineering Department, Miguel Hernandez University, Ave. Universidad S/N,
Ed. Alcudia, 03202 Elche (Alicante), Spain; E-Mail: mels@umh.es
* Author to whom correspondence should be addressed; E-Mail: jllor@umh.es;
Tel.: +34-96-665-8393; Fax: +34-96-665-8814.
Received: 12 December 2011; in revised form: 13 January 2012 / Accepted: 29 January 2012 /
Published: 31 January 2012
Abstract: Several Medium Access Control (MAC) and routing protocols have been
developed in the last years for Underwater Wireless Sensor Networks (UWSNs). One of
the main difficulties to compare and validate the performance of different proposals is the
lack of a common standard to model the acoustic propagation in the underwater
environment. In this paper we analyze the evolution of underwater acoustic prediction
models from a simple approach to more detailed and accurate models. Then, different high
layer network protocols are tested with different acoustic propagation models in order to
determine the influence of environmental parameters on the obtained results. After several
experiments, we can conclude that higher-level protocols are sensitive to both: (a) physical
layer parameters related to the network scenario and (b) the acoustic propagation model.
Conditions like ocean surface activity, scenario location, bathymetry or floor sediment
composition, may change the signal propagation behavior. So, when designing network
architectures for UWSNs, the role of the physical layer should be seriously taken into
account in order to assert that the obtained simulation results will be close to the ones
obtained in real network scenarios.
Keywords: underwater wireless sensor networks; network simulation; acoustic propagation
models; MAC and routing protocols
OPEN ACCESS
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1. Introduction
There has been an increasing interest in the development of Underwater Wireless Sensor Networks
(UWSNs) in the last years. The first attempts to analyze UWSN behavior were based on the mature
technology developed during the last decade in terrestrial wireless sensor networks (TWSNs). Despite
having a very similar functionality, UWSNs exhibit several architectural differences with respect to the
terrestrial ones, which are mainly due to the transmission medium characteristics (sea water) and the
signal employed to transmit data (acoustic ultrasound signals) [1]. Then, the design of appropriate
network architecture for UWSNs is seriously complicated by the conditions of the communication
system and, as a consequence, what is valid for terrestrial WSNs is perhaps not valid for UWSNs, so a
general review of the overall network architecture is required in order to supply an appropriate network
service for the demanding applications in such an unfriendly submarine communication environment.
Basically, an UWSN is formed by the cooperation among several network nodes (often called
sensor nodes) that establish and maintain the network through the use of bidirectional acoustic links.
Every node is able to send/receive messages from/to other nodes in the network, and also to forward
messages to remote destinations in case of multi-hop networks. Every node may have one or several
sensors that are actively recording environmental data which should be forwarded to special sink
nodes, typically platforms or buoys at the surface. Sink nodes have communication channels to forward
(and/or local store) the collected data to the remote control station in the coast, typically through a
Radio Frequency (RF) link.
Since acoustic signals are mainly used in UWSNs, it is necessary to take into account the main
aspects involved in the propagation of acoustic signals in underwater environments, including: (a) the
propagation speed of sound underwater is around 1,500 m/s (five orders of magnitude slower than the
speed of light), and so the communication links will suffer from large and variable propagation delays
and relatively large motion-induced Doppler effects; (b) phase and magnitude fluctuations lead to
higher bit error rates compared with radio channels’ behaviour, being mandatory the use of forward
error correction codes (FEC); (c) as frequency increases, the attenuation observed in the acoustic
channel also increases, being this a serious bandwidth constraint; (d) multipath interference in
underwater acoustic communications is severe due mainly to the surface waves or vessel activity,
being a serious problem to attain good bandwidth efficiency.
So, designing an efficient and reliable communication channel is not an easy task, being roughly
different from TWSN approaches. This fact may be the reason for the existence of a lot of simulation
tools that define different models of underwater acoustic signal propagation. In fact, there is no
agreement about when to use a particular simulation tool and/or standard model to represent the
underwater acoustic propagation behavior; indeed there are almost as many simulation tools for this
purpose as MAC and routing protocol proposals. In general, these studies have mainly been focused in
developing higher layer protocols without paying much attention to the physical layer and its
components. AUVNetSim [2] is an example where the physical layer is too simple, based on Thorp
approximations, so different environment conditions cannot be considered in the propagation model,
leading to simulation results that may be far from the ones obtained in real network deployments.
Other approaches define complex underwater acoustic propagation models that are closer to the real
behavior of underwater acoustics. This is the case of the acoustic propagation model proposed by Xie
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and Gibson [3] which it is based on the Monterrey Miami Parabolic Equation (MMPE). The MMPE
equation calculates the evolution of sound pressures produced by an acoustic source in a specific
underwater scenario. It divides the scenario in a grid of 3-D cells, performing the requiring
computations to get a representative sound pressure for each cell. If we reduce the cell size, we can
obtain more accurate prediction results, but the computational demand for the corresponding
calculations is overwhelming at medium to large size network scenarios. The MMPE model is
implemented with OPNET Modeler [4], so small scale network simulations may be performed, but
there is an intrinsic scalability problem when performing network simulations.
Another underwater network simulation framework is the World Ocean Simulator System
(WOSS) [5]. It is composed by several tools like (a) ns2 simulator [6], (b) Bellhop tool that accurately
models the underwater sound propagation by an specific ray tracing algorithm, and (3) network
scenarios defined with real data (temperature, salinity, wave activity, etc.) from well-known world
ocean databases. The WOSS framework and the MMPE model are two approaches that perform an
accurate acoustic propagation modeling, but they suffer from high complexity limiting the simulation
to small network scenarios and low numbers of network nodes.
In order to alleviate the complexity constraint, in [7] we proposed a statistical prediction model
based on the Bellhop ray tracing tool that reduces its complexity and achieves similar levels of
prediction accuracy, so we will be able to perform network modeling with reasonable high accuracy
level and low computational overhead.
These modeling tools and a lot of variations around them lead to the hard task of comparing two
different proposals unless they are implemented on the same platform, and even in this case, the
simulator should be as realistic as possible towards the real environment conditions. Otherwise the
results will lack of accuracy, and empirical testing, at least in scale-down experiments, should be done
before releasing the final implementation of the underwater nodes, reducing the power of simulation
tools for predicting real network behavior.
At simulation time, when we define the parameters of a network scenario and the location where
network nodes would be deployed, we may use a simple assumption through general scenario
parameters or define those scenario parameters that will have a direct influence in the acoustic
propagation behavior. For example, we may decide to use a simple scenario where the sound speed
propagation is considered as a fixed value of 1,500 m/s, with a two dimensional deployment area
(depth is not considered) and a simple acoustic propagation approach like the one proposed by
Thorp [8], to evaluate the performance of a point-to-point link between two nodes. On the contrary, we
could define a more detailed network scenario by including, among others, the scenario location with
bathymetry and floor sediment composition that will impact on the way sound propagation is
reflected/absorbed at the ocean floor. Also, the temperature of the water will depend on both the
latitude and longitude of network scenario and the season of the year. This fact together with the water
salinity and the depth may change the sound speed between 1,450 and 1,540 m/s. There are other
important factors that may change sound propagation behavior such as the well-known ocean wave’s
influence which is different for shallow and deep waters, or the noise produced by ships, biological
activity or shoals. All of these scenario parameters should be taken into account in order to develop
detailed acoustic propagation models for UWSN, so modeling higher-level network protocols will be
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aware of network scenario conditions, and the obtained simulation results would be closer to the ones
obtained in experimental tests [9].
In this work, we will review several acoustic propagation models from simple approaches to the
more accurate ones, and observe their behavior when different network scenario parameters are
changed (i.e., wave activity), so we can determine their sensibility to environmental network scenario
parameters. Then, we will choose the most appropriate acoustic propagation model in terms of accuracy
and low-complexity, in order to analyze the performance behavior of different MAC protocols and also
check how the scenario environmental changes impact on their network performance in terms of
throughput, delay and collisions. From the result obtained in this study, we will appreciate: (a) the
importance of defining an accurate and low complexity propagation model, and (b) the sensibility of
higher layer protocols to the time-varying scenario environmental conditions.
This paper is organized as follows: in Section 2, we present several acoustic propagation models,
and in Section 3 we introduce the higher-layer protocols (MAC and routing protocols) used in this
work. In Section 4, we evaluate the selected acoustic propagation models with a fixed MAC protocol
in order to observe their impact on network performance under different scenario conditions. Then, in
Section 5, we will analyze the behavior of several MAC and routing proposals under a simple and
accurate acoustic propagation model. Finally in Section 6, some conclusions and are drawn future
work proposed.
2. Acoustic Propagation Models
Simulating UWSN communications requires modeling the acoustic wave’s propagation while a
node tries to transmit data to another one. There are several models proposed in the literature from the
simplest ones based in the sound propagation theory to more elaborate and complex models based on
the physics of acoustic sound propagation. In this section, we will describe several acoustic propagation
models that represent different approaches to the same problem, but with different degrees of
complexity/accuracy. We will present them in order of increasing complexity, so for each approach we
will know how propagation acoustics are predicted and what parameters are taken into account for
that purpose.
2.1. Urick Description and Thorp Formula
The theory of the sound propagation is according to the description by Urick [10], a regular
molecular movement in an elastic substance that propagates to adjacent particles. A sound wave can be
considered as the mechanical energy that is transmitted by the source from particle to particle, being
propagated through the ocean at the sound speed. The empirical formula presented by Thorp [8] is
defined as the sound intensity decrease through the path between the source and destination nodes. The
absorption coefficient factor α depends on the sound frequency f. The proposed acoustic attenuation
expression is represented as follows:
,

(1)
where d: Distance, k: Geometry (k = 1: Cylindrical, k = 2: Spherical).
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In the same set of formulas is also available the definition for power spectral density to calculate the
noise in the receiver nodes (see [8] for more details).
2.2. Monterrey Miami Parabolic Equation (MMPE)
The Monterey-Miami Parabolic Equation model [11] is used to predict underwater acoustic
propagation using a parabolic equation which is closer to the Helmholtz equation (wave equation) [12];
this equation is based on Fourier analysis. The sound pressure is calculated in small incremental
changes in range and depth, forming a grid. It incorporates randomness and wave motion to the
approximation, using a dynamic propagation loss calculation. The authors show that small changes in
depth and node distances can drive to big differences in the path loss as a result of the ocean wave’s
motion impact on acoustic propagation (more details in [3]). The propagation loss formula based on
the MMPE model is the following one:

,,, (2)
where:
PL(t): propagation loss while transmitting from node A to node B.
m( ): propagation loss without random and periodic components; obtained from regression of
MMPE data.
f: frequency of transmitted acoustic signals (in kHz).
dA: sender’s depth (in meters).
dB: receiver’s depth (in meters).
s: Euclidean distance between nodes A and B (in meters).
w(t): periodic function to approximate signal loss due to wave movement.
e(): signal loss due to random noise or error.
The m() function represents the propagation loss provided by the MMPE model. According to the
logarithmic nature of the data, a nonlinear regression is the best option to provide an approach to the
model based on the coefficients, An, supplied by the preliminary model. The proposed expression to
calculate this function is the following one:
,,,log
.
  
1
1
40
4100
0.002750.003
914
6
8
(3)
The w() function approximates the signal loss due to the wave movement. It considers the
movement of a particle that will oscillate around its location in a sinusoidal way [13]. That movement
is represented as circular oscillations that reduce their radius as the depth of the particle increases. The
length of that radius is dependent of the energy of the wave and is related to its height value. The
common waves have hundreds of meters of wavelength and have an effect up to 50 m of depth.
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For the calculation of the effects of the wave we will consider:
,,,,, (4)
where:
h(): scale factor function.
lw: ocean wave length (meters).
dB: depth of the receiver node (meters).
hw: wave height (meters).
Tw: wave period (seconds).
E(): function of wave effects in nodes.
This function contains the elements that resemble the node movement, first by calculating the scale
factor h( ) and then the effect of the wave in a particular phase of the wave motion. The calculation of
the scale factor is as follows:
,,,,1

0.5 2 
 (5)
The e() function represents a random term to explain background noise. As the number of sound
sources is large and undetermined, this random noise follows a Gaussian distribution and is modeled to
have a maximum of 20 dB at the furthest distance. This function is calculated by the following equation:
20
 (6)
where:
e(): random noise function
s: distance between the sender and receiver (in meters).
smax: maximum distance (transmission range in meters)
RN: random number from a Gaussian distribution centered in 0 and with variance 1.
2.3. Bellhop (BH)
Bellhop Ray Tracing requires the solution of the ray equations to determine the ray coordinates of
the acoustic signal propagation. Amplitude and acoustic pressure requires the solution of the dynamic
ray equations which are described in detail in [14]. This tool is integrated with empirical data updated
from world databases that measure the Sound Speed Profile (SSP), bathymetry and floor sediment such
as the General Bathymetric Chart of the Oceans (GEBCO) and National Oceanic and Atmospheric
Administration (NOAA) [15,16]. The ocean wave’s motion is also included to calculate the rays’
trajectories; so taking into account the type of sediments and the sound speed profile (SSP) this
propagation model shows a behavior that it is very close to experimental studies for acoustic
propagation in underwater environments (more details can be found in [14,17]).
For a system with cylindrical symmetry, the ray equations can be written as:

,
1

 (7)
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
,
1


where r(s) and z(s) represent the ray cylindrical coordinates and s is the arclength along the ray; the
pair c(s) [ξ(s), ζ(s)] represents the tangent versor along the ray. Initial conditions for r(s), z(s), ξ(s) and
ζ(s) are 0  , 0 ,
0
,
0
(8)
where θs represents the launching angle, (rs, zs) is the source position, and cs is the sound speed at the
source position. The coordinates are sufficient to obtain the ray travel time:
τds
cs
(9)
which is calculated along the curve [r(s), z(s)].
Figure 1 shows how the Bellhop tool draws the ray trajectories to calculate the travel of the acoustic
signals and thus obtain the attenuation at different points of the scenario.
Figure 1. Bellhop ray trace.
2.4. Bellhop-Based Statistical Prediction Model (BH-SPM)
While the Bellhop model provides an accurate calculation of the acoustic propagation model,
network modeling requires near continuous references to propagation delays and signal attenuation
values, being computationally infeasible to support this complex model in UWSN simulation
frameworks. Thus, it would be of interest to provide an approximation of Bellhop model that supports
the time constraints of the network simulation while preserving most of its accuracy.
In [7] we proposed a statistical model based on the Bellhop approach (BH-SPM) that it is able to
produce the acoustic signal attenuation map by means of a statistical prediction model integrated in the
simulator tool that significantly reduces complexity. BH-SPM model enables computationally-efficient
inclusion of fading and multipath effects into a network simulator. Namely, to assess the average
0100 200 300 400 500 600 700 800 900 1000
0
50
100
150
200
250
300
Range (m)
Dept h (m)
BELLHOP- ANGLE = 60.0º FREQ = 10000
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system performance, network operation has to be simulated over a large set of channel realizations
(e.g., varying surface conditions). Whereas repeated computation of the ray trace for different hops
that each of the data packets traverses in a given network may be computationally prohibitive,
statistical modeling requires only a single call to the Gaussian random generator for each packet
transmission. Thus, the overall simulation time is considerably reduced, allowing a system designer to
freely experiment with varying protocols and resource allocation strategies in an efficient manner. The
ultimate goal of such computational experiments is to choose the best upper-layer protocol suite and to
relate the necessary system resources (power, bandwidth) to the propagation conditions, i.e., to the
statistical parameters of the transmission loss.
Figure 2. Tradeoff between model propagation accuracy and computational complexity.
In Figure 2, the tradeoff between model complexity and accuracy is shown. In this figure, we also
define the thresholds for the desirable accuracy and complexity of the sound propagation model. Thus,
the shaded area covers those propagation models with the minimum acceptable model propagation
accuracy that leads to get reliable results and, at the same time, low computational complexity levels
that allow detailed and scalable network simulations.
In Figure 3 the acoustic attenuation map from the selected propagation models is shown. It was
obtained with a specific underwater scenario where a source node, located at 10 m depth, is emitting an
acoustic beam of 120 degrees at 10 kHz. The scenario environmental conditions are the same
(bathymetry, surface activity, temperature, etc.). As it can be shown, the waves (at top) and the
underwater floor (at bottom) are represented in blue and brown colors, respectively. We can quickly
compare the attenuation values for the different models whereas in (a) Thorp, the simple model only
shows signal degradation in accordance with the distance without taking into account neither the
scenario depth nor the source radiation pattern, in (b) the MMPE model is able to define a more
accurate attenuation map taking into account, depth, distance and ocean wave activity. Finally, in
Figure 3(c) the acoustic physics are taken into account by using Bellhop model which introduces the
ray reflections/absorptions depending on floor sediment composition, the floor shape and the surface
wave’s geometry. In addition, a different sound speed profile is calculated based on the scenario world
location and its bathymetry.
Complexity
Complexity threshold
Accuracy threshold
BH-SPM
MMPE
THORP
BH
Accuracy
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Figure 3. Attenuation of acoustic waves.
(a) Thorp
(b) MMPE
(c) BH/BH-SPM
Once these models are presented, the next step is to determine if the differences appreciated in
Figure 3 may be transferred to the upper network layers in such a way that performance of higher layer
protocols is affected in a significant way. If so, then it would be necessary to consider the use of
complex propagation models that represent as accurate as possible the underwater acoustic propagation
in order to obtain simulation results close to the ones obtained in experimental test-beds.
3. Higher-Layer Protocols: MAC & Routing
After reviewing and analyzing several acoustic propagation models, in this section we will
introduce several higher network layer protocols that we will use later in our experiments. Most of the
higher protocols presented below were proposed for terrestrial network technologies, and their network
performance is well-known. However, as stated in the introduction, UWSNs exhibit several
architectural differences with respect to the terrestrial ones. In particular, the acoustic signal propagation
delay is several orders of magnitude higher than RF signals, resulting in large signal propagation
versus packet transmission time ratios, something which it is not desirable for obtaining high utilization
levels of network resources, and as a consequence limiting the overall network performance. So, the
behavior of higher level protocols may seriously change when they are employed in UWSN scenarios.
Range (m)
Depth (m)
0500 1000 1500 2000 2500 3000
0
5
10
15
20
25
30
35 0
20
40
60
80
100
Range (m)
Depth (m)
0500 1000 1500 2000 2500 3000
0
10
20
30
0
20
40
60
80
100
Range (m)
Depth (m)
0500 1000 1500 2000 2500 3000
0
10
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30
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As with acoustic propagation models, the selected protocols for the evaluation include: (a) several
MAC layer protocols: from simple proposals, like ALOHA that only cares of sending the packet
message, until more complex protocols like DACAP that include carrier sense and collision avoidance
by means of at least three-way handshake exchanges; and (b) a couple of routing protocols: a simple
static routing and a cross-layer routing proposal FBR. We will test all of them to determine how the
scenario environmental changes impact on their performance in terms of throughput and delay.
3.1. ALOHA
The ALOHA protocol [18] is the simplest MAC protocol since it does not care about channel status
or packet delivery success. So, it quickly reaches the network saturation point producing a huge
number of collisions. This MAC approach is avoided in other network technologies due its lack of
ability to proper order the access to a shared medium, but it does not requires handshaking exchange.
3.2. CSMA
The Carrier Sense Multiple Access (CSMA) [19] is an evolution of ALOHA that includes a channel
sensing mechanism, so before sending a data packet, the CSMA protocol checks if the shared channel
is free. If not, it defers data packet transmission until the channel is freed. This protocol reduces
considerably the channel collisions when compared with the ALOHA protocol, without requiring
extra signaling.
3.3. CSMA/CA
The Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) incorporates a
handshaking process to establish the communication channel between two nodes. It uses request to
send (RTS) and clear to send (CTS) control packets to create a tunnel free of collisions at both
communication ends. After acquiring the channel, the data packet is sent to the destination node.
Finally, the sender waits for an acknowledgment control packet (ACK) that will indicate the successful
reception of data packet. If no ACK is received, then a contention mechanism, typically based on a
back-off scheme, randomly delays the packet retransmission. Also, a maximum number of consecutive
retransmission attempts is defined. If this maximum is reached, then the packet is discarded.
3.4. DACAP
Distance Aware Collision Avoidance Protocol (DACAP) [20] is a handshaking protocol designed
for Ad Hoc Underwater Acoustic Sensor Networks. The protocol includes a power aware behavior that
that it is intended to reduce power consumption by avoiding/reducing collisions and at the same time
achieving good network throughput. It also minimizes the handshake time by using the tolerance to
interference of receiver node, especially when receiver is close to the reception range limits. The
network nodes do not need to be synchronized and it supports node mobility (dynamic scenarios). The
throughput achieved by DACAP is several times higher than the one achieved with Slotted
FAMA [18], while offering similar protection from collisions.
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The improvement introduced by DACAP towards the traditional CSMA/CA mechanism lies on the
behavior of the receiving node when is waiting for a data packet. If it overhears a control message
coming from other node, it will send a warning packet (WAR) to this node in order to let it know that
there is already a transmission in process. Moreover, after receiving the CTS control packet, the sender
node defers the transmission data packet for a defined delay time. The transmission attempt is aborted
if by any chance the sender node receives another control or warning message. The delay times are
determined according to the distance between the nodes involved in the transmission, that can be solve
during the handshake by measuring the roundtrip time. Even though when the receiver node sends a
warning message, it has no feedback that lets it know if the interfering node cancels its transmission.
That is the reason why the receiver keeps listening to the channel after sending the warning message,
and thus the defer state is set to a minimum delay time between the CTS and the DATA so that it
avoids a collision.
3.5. Static Routing
This kind of routing protocol identifies a simple approach at routing layer, where the path between
two network nodes is always the same in those networks without node mobility (every node is fixed at
a specific location). Although there are several routing protocols that fit into this category, the
geographical-based routing protocols are very popular in Wireless Sensor Networks deployments,
where all network nodes are equipped with a localization system that determine their global position.
So, when a source node has to deliver a packet to a destination node located several hops away, it
would forward the packet to the neighbor node that is closer to the destination. Assuming that network
topology is static (no mobility pattern), the path provided by a static routing protocol to reach a
specific network node will be always the same.
3.6. FBR
The Focus Beam Routing (FBR) [21] protocol is based on node location capabilities, and is able to
find the path between two nodes in a random deployed network if each node knows its position and the
position of the destination (gateway) node. Assuming a communication between nodes A and B,
node A will send an RTS multicast packet to all the reachable neighbors. This packet will include the
information of the source (node A) and final destination (node B). This request is a short control
packet that contains the location of both the source node (A) and the final destination (B) node. Packet
collisions can happen but always will involve short packets as the link is safe for data packets which
have no risk of collisions. Although the chances of collision are small, if the source node detects a
collision, it will receive signal but it will not be able to decode the information of the incoming data
frame, it will resend the RTS once again.
4. Propagation Model Evaluation
In this section, we will analyze the behavior of different propagation models when simulating an
underwater wireless sensor network deployed in a specific network scenario location. We will study
both performance results and sensibility under network scenario parameters. For that purpose we will
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describe the characteristics and parameters associated to the network scenario, the MAC protocol we
will employ, and the traffic load characterization.
4.1. Scenario Deployment
The network scenario deployment is shown at Figure 4 (surface view). The volume size is defined
as a cube of 5,000 × 5,000 × 50 m (Length × Width × Depth); the covered area is divided in cells
of 1,000 × 1,000 m. The gateway (sink node) is always placed in the middle cell at a fixed depth
of 10 m. Then we put one node per cell in a random position inside the cell, as well as random depth
(this parameter will be bounded by the scenario bathymetry). Once all the nodes are deployed the
connectivity of the network is checked by guaranteeing that every node has a path to the gateway
(one-hop or multi-hop paths) and that there are not isolated subnetworks or nodes. Using the same area
and cell size, ten different random scenarios have been built and validated for the simulation test.
Figure 4. Network deployment 2D.
In Figure 5, a 3D representation of the network scenario is shown. It is located at coordinates
39°48'13.14"N and 0°4'34.53"W (Valencia, Spain). This view let us appreciate the different depths of
the nodes, close to the surface, middle depth and at the bottom of the scenario. We have fixed the wave
activity with waves of 2 m height and 80 m length. The network scenario floor is composed of gravel.
All of the scenario and environmental parameters were taken from National Geophysical Data Center
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databases [22,23] related to the specific global coordinates of our network. This example could
represent a typical network scenario of shallow waters with a low altimetry shape where the bottom
relief is deeper as it goes farther from the coast.
Figure 5. Network deployment 3D (Google EarthTM).
In Table 1 the main parameters used in the simulations are shown.
Table 1. Simulation and network scenario parameters.
Parameter Value
Propagation Models THORP, MMPE, BELLHOP
# Sensors 24
# Gateways 1
Month Annual Average
Wave Height (m) 2
Wave Length (m) 80
Frequency (kHz) 10
Scenario Depth (m) 50
Global Load (packets/s) 0.16 to 4
Data Packet Size (bits) 1,024
Control Packet Size (bits) 24
Bandwidth (kbps) 5
# Scenarios 10
Simulation Time (s) 3,600
With respect to network traffic load, we proposed a constant bitrate approach where every sensor
node generates fixed length data packets at a generation rate defined with an exponential distribution.
All sensor nodes in the network will send packets towards gateway node, so we will obtain a hot-spot
traffic pattern, where all the packets are delivered to the same destination.
In this section we will consider a One-Hop (OH) network topology, where all network nodes are
able to reach the gateway in one hop. The power transmission is set constant in all nodes and it is
calculated as the energy required by the farthest nodes (the ones in the perimeter) to reach the gateway,
considering Thorp’s attenuation model. That means that in Thorp simulation these nodes will always
reach the gateway but in both MMPE and Bellhop reachability it is not guaranteed, mainly due to the
more realistic assumptions about the acoustic propagation. So, the time-varying acoustic signal
Sensors 2012, 12
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attenuation may produce packet loses due to the lack of signal strength at gateway, which is supposed
to have an impact on the network performance.
Figure 6. Reachability of gateway from Node #1 (bottom leftmost).
(a) Thorp
(b) MMPE & Bellhop
In Figure 6(a) the transmission range of node #1 is fixed (are covered by the circle) during all the
simulation, indicating the set of nodes that always receive the transmissions from node #1 (in
particular the gateway node) using Thorp propagation model. However, in Figure 6(b), MMPE and
Bellhop models define the transmission range with two dashed circles. The smaller circle represents
the nodes that always receive node #1 transmissions; meanwhile the rest of nodes included in the
bigger circle (those that are out of the smaller one) may receive the transmissions with a certain
probability defined by the propagation model.
4.2. Evaluation Results
In this section we are going to evaluate the different propagation models proposed in Section 3,
assuming we have a CSMA/CA MAC protocol and the scenario and simulation parameters
defined above.
The simulation framework is based on OPNET, MATLAB and Bellhop ray tracing tool, and uses
information related to underwater scenario characteristics like bathymetry, salinity, and seafloor
composition, found at real worldwide locations which it is downloaded from NOAA and GEBCO
worldwide ocean databases. This information is combined with the OPNET network scenario module
in order to create the corresponding environmental files. With these files OPNET connects to
MATLAB through its interface and runs Bellhop obtaining the result files. A fully explanation of the
simulator framework can be found in [24].
The performance metrics we will show are:
Sensors 2012, 12
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(1) Goodput, defined as the throughput found at the application layer (note that in top of MAC
layer we have no other network layers, only the application), so only data packets that
successfully arrive to the gateway node are taken into account. This also means that control
packets like RTS, CTS, and ACK are not considered in the computation of goodput.
(2) Average Packet Delay, defined as the average delay incurred by a packet to reach its
destination. This delay is calculated from the time when MAC layer gets the packet at source
node to start delivery until the instant when this packet is correctly received at gateway node.
Figure 7. Average Goodput with different acoustic propagation models.
Figure 8. Valencia’s (scenario location) annual average sound speed as a function of node depth.
In Figure 7, we can see the average goodput from 10 random scenarios (as defined in previous
subsection) is shown. As it can be observed, the results appear to follow the same pattern with clearly
different goodput values depending on the propagation model used. The Thorp propagation model gets
the best performance, MMPE is estimable worse and finally Bellhop is the one with the worst
behavior. This behavior agrees with the prediction stated before as the connection links between the
nodes that are farther from gateway suffer the consequences of using more accurate propagation
models like MMPE and Bellhop. In other words, Thorp model always provides link reachability to
0.08
0.1
0.12
0.14
0.16
01234
Goodput (packets/s)
Network Load (packets/s)
THORP
MMPE
BELLHOP
0
5
10
15
20
25
30
35
40
45
50
1500 1510 1520 1530 1540 1550
Depth (meters)
Sound Speed (m/s)
January
August
Annual
Sensors 2012, 12
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network nodes during the simulation; however MMPE loses communication due to the wave effect and
this leads to reduced goodput performance. This behavior is even more pronounced with Bellhop
model where signal attenuation is calculated in a more accurate way, resulting in a higher number of
dropped packets during the n-way handshaking process of CSMA/CA protocol.
On the other hand we measure the average packet delay which will strongly depend on the channel
propagation delay. So, the propagation delay (Tprop) depends on the distance (d) between sensor and
gateway nodes, the specified inter frame delay (SIF), and the sound speed propagation (Tssp) that may
change with node depth and water temperature, as shown in Figure 8 obtained through databases [22,23].
In expression (10) we define the delay experienced by a packet delivery in one-hop transmission
without network contention/interference, taking into account the CSMA/CA protocol handshake and
the distance and sound speed parameters.
T
p
ro
p
 d /
T
ss
p
,
T
p
k
t
 packet_size / data_rate, SIF  Inter_Frame_Dela
y
Delay 
T
p
ro
p
RTS
T
p
k
t
RTS SIF 
T
p
ro
p
CTS 
T
p
k
t
CTS  SIF 
T
p
ro
p
DATA 
T
p
k
t
DATA
(10)
So the experienced delay of packet sent by a sensor located 1,500 m away from gateway node and
with 10 m depth would be:
T
p
ro
p
RTS
=
T
p
ro
p
CTS 
T
p
ro
p
DATA  1,500/1,520  0.9868
s
T
pkt
RTS  T
pkt
CTS  24/5,000  0.0048 s
T
pkt
DATA 1,024/5,000  0.2048 s
SIF  0.02048
s
Delay  3 * 0.9868  2 * 0.0048 0.2048  2 * 0.02048  3.21576
s
(11)
Figure 9. Average packet delay with different acoustic propagation models.
The results shown in Figure 9 reveal almost the same delay for all propagation models until the
network enters in a saturation state, where MMPE and Bellhop seem to produce better results. At first
sight this may suggest a lack of coherence, but if we take a close look at the distribution of packets
received in the gateway from the different source nodes, we will appreciate that with MMPE and
Bellhop, the gateway receives less packets from the farther nodes as they are more affected by the
attenuation variability introduced by these propagation models, as shown before in Figure 6, so, this is
the main cause of the lower overall packet delay with the use of more accurate acoustic propagation
models, since the average packet delay decreases.
3
3.4
3.8
4.2
4.6
5
0 0.5 1 1.5 2 2.5 3 3.5 4
Delay (sec)
Network Load (packets/s)
THORP
MMPE
BELLHOP
Sensors 2012, 12
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In the early first tests, it is clear that the propagation model is an important issue to take into
account but now we go a bit further by changing the environmental parameters of the network scenario
in order to assess their influence. For that purpose we will use one of the scenarios used before, fixing
the network load at 2 packets/s to the point just before network saturation, and introducing two
different months of the year (January and August) with different ocean average temperatures plus six
different levels of wave heights (varying surface conditions) from 1 to 11 m height, the rest of network
and environmental parameters remain the same as in Table 1.
Figure 10. Propagation loss (a) and goodput (b) values varying physical scenario parameters.
(a) Propagation Loss (b) Goodput
In Figure 10(a) the acoustic attenuation found between two network nodes, sensor 1 and gateway, is
shown. As expected, Thorp’s results remain constant since its equation does not include the effect of
the physical parameters. Meanwhile, MMPE and Bellhop propagation models significantly reduce the
obtained goodput as the wave height increases, i.e., they suffer from the wave motion effect. Also,
neither Thorp nor MMPE are affected by the change of season whereas Bellhop shows different results
for the months selected, we can appreciate worse performance in January than in August due to the
different propagation conditions derived from the average ocean temperature. The average delay
results including variable physical parameters are not included here as they are almost the same
behavior than in Figure 9.
So, summarizing this section, we can observe that in addition to having a detailed propagation
model, different environmental conditions have a great impact in network performance. This leads us
to seriously consider both: (a) an accurate acoustic propagation model, and (b) environmental and
scenario parameters to obtain reliable simulation results that efficiently predict the real behavior of the
sound propagation in a particular network scenario.
5. Higher Layer Protocol Evaluation
In the previous section we have reached interesting conclusions about the influence of the propagation
models, so in this section we are going to evaluate their impact on different higher layer protocols, MAC
and routing protocols, using an accurate propagation model like the one defined by Bellhop and taking
into account several physical parameters related to the network scenario and environmental conditions.
45
50
55
60
65
70
1357911
Propagation Loss (dB)
Wave Height (meters)
THORP
MMPE
BELLHOP AUG
BELLHOP JAN 0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
1357911
Goodput (packets/s)
Wave Height (meters)
THORP
MMPE
BELLHOP AUG
BELLHOP JAN
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In the following evaluation we will reuse the same network scenarios define in previous sections
but in two different operational modes: One-Hop (OH) and Multi-Hop (MH). In the first one, OH, all
network nodes are able to directly reach gateway node (packet destination); whereas the later mode,
MH, some network nodes require relaying their packets through other nodes to reach the gateway.
The difference between OH and MH modes is focused in the allocated transmission power level to
network nodes, which define their coverage area, so in OH network scenarios we adjust transmission
power level to reach gateway node from farthest nodes (the same as in previous section simulation
experiments). However, for MH network scenarios we will reduce the power transmission of nodes in
such a way that they will be able to reach only the nodes of the adjacent cells. In MH network
scenarios, a routing protocol is required to let the packets travel across the network towards their
destination (gateway node). By default, in MH network scenarios we define a static routing protocol.
In Figure 11 we can see the connections between nodes in both operational modes.
Figure 11. Network operational modes.
(a) One-Hop
(b) Multi-Hop
5.1. MAC Protocols
In the first test we choose two MAC protocols: CSMA and CSMA/CA. Although they seem to be
very similar approaches, CSMA it is a simple version with no signaling to handle a packet
transmission, meanwhile CSMA/CA is a 4-way handshake protocol as defined in Section 3.
Our purpose is to analyze how these MAC protocols tackle a network deployment with different
power transmission policies, clearing up where it is worth to focus the efforts in terms of power
consumption, throughput, packet delay, etc. The simulation parameters are the same as in Table 1
increasing the global load up to 12 packets/s.
In Figure 12(a), we can see that CSMA-OH soon reaches its highest performance, and after
saturation point its goodput performance degrades very quickly reaching a near network starvation
state. However, CSMA-MH follows the same pattern with a smoother curve. This behavior in OH
scenario can be easily explained because at lower loads the gateway receives more or less the same
number of packets from all network nodes, while in MH scenarios the effect of hot-spot traffic
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pattern leads to unbalance this behavior and as a consequence reduce network load in the
gateway neighborhood.
Figure 12. Goodput (a) and delay (b) of selected MAC protocols in OH and MH modes.
(a) (b)
In turn the CSMA/CA evolution is similar in both strategies, quickly reaching its best performance
and keeping it despite the increasing load; having a better overall result in the MH strategy due to the
same reasons commented before. It is important to remark that at higher network loads, in all cases but
especially in MH, sensors that are closer to the gateway have more chances to achieve successful data
packet transmissions than farther nodes (no fair resource sharing due to hot-spot traffic pattern and the
inherent large propagation delay).
From these results we can state that the MH strategy has an overall better performance, being at the
same time more energy efficient since it is able to reduce energy demands to half of the ones required
by OH. Also, it was observed that those nodes located at the scenario surroundings will have less
probability to successfully deliver packets to gateway, so this issue opens the way to define routing
protocols that will balance the overall packet delivery rate between all the sensor nodes with
independence of their location.
Now, we take a look at the delay behavior shown at Figure 12(b). As stated in Equation (10) the
CSMA/CA delay is the result of the acoustic propagation time and the transmission time of the
different packets involved in the handshaking, meanwhile in CSMA we only send DATA packets so it
is expected that it gets smaller delays. As shown in Figure 12(b) CSMA delays are slightly higher in
MH than in OH. This result obeys to the fact that the signal propagation delay between a source node
and the gateway is typically smaller than the sum of propagation delays of the paths followed to reach
gateway node, plus the time required to send at least n data packets (where n is the number of hops to
reach gateway) instead of one:
OH
Dela
y
T
p
ro
p
O
T
p
k
t
DATA
MH
Dela
y
T
prop
M1
T
prop
M2
 2*
T
pkt
DATA
T
p
ro
p
O
T
p
ro
p
M1
T
p
ro
p
M2
(12)
0
0.2
0.4
0.6
0.8
1
024681012
Goodput (packets/s)
Network Load (packets/s)
CSMA-OH
CSMA/CA -OH
CSMA-MH
CSMA/CA-MH
0
1
2
3
4
5
6
7
01234
Delay (sec)
Network Load (packets/s)
CSMA-OH
CSMA/CA-OH
CSMA-MH
CSMA/CA-MH
Sensors 2012, 12
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In Figure 13, we show an example involving two network nodes and the gateway. The propagation
time from sensor 11 to gateway use to be smaller than the propagation from sensor 11 to 12 plus the
propagation from sensor 12 to gateway. In the event where OH and MH strategies suffer the same
propagation delay, MH mode would require two data transmission cycles, so the overall packet delay
is always longer than in the OH strategy. This fact has a greater influence in ann-way handshaking
protocol, like CSMA/CA, where for each data packet transmission (each hop) n packet propagation
delays are required, increasing a lot the overall packet delay.
Figure 13. Signal propagation times: OH vs. MH.
The CSMA/CA protocol shows a more stable behavior in terms of goodput performance in OH
network scenarios, but environmental conditions and the influence of propagation time in the overall
packet delay dramatically affect handshaking protocols. On the other hand, the CSMA protocol
maintains the average packet delay low and stable in both OH and MH strategies, since no
handshaking is performed to complete one packet delivery. In Figure 12(b), CSMA protocols exhibit
higher average packet delay at very low network loads, decreasing as network loads increase. This
behavior is due to the hot-spot traffic pattern, since as network load increases the nodes closer to the
gateway are the ones with higher delivery rates and, at the same time, lower packet delay (signal
propagation delay), resulting in a reduction of the average packet delay.
Figure 14. Collisions one hop vs. Multihop.
(a) CSMA (b) CSMA/CA
Finally, another gauge to measure the power consumption in the network is packet collision statistic.
In Figure 14, we show CSMA and CSMA/CA protocols with both OH and MH network configurations.
As expected, CSMA shows a much higher number of collisions, leading to an increasing number of
10
100
1000
10000
100000
0123456
# Collisions
Network Load (packets/s)
CSMA
CSMA Multihop 10
100
1000
10000
0123456
# Collisions
Network Load (packets/s)
CSMA/CA
CSMA/CA
Multihop
Sensors 2012, 12
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packets lost, increasing the overall wasted energy. However, CSMA/CA shows better performance
arriving to a constant number of collisions just after the network reached saturation. In both cases, the
number of collisions is highly reduced with the MH approach.
5.2. Routing Protocols
In this subsection we perform a simple simulation experiment with a particular MAC protocol in
combination with different routing policies, in order to assess their behaviour under different scenario
environmental conditions. We propose the DACAP MAC protocol since it defines some crosslayer
support for routing protocols, and we want to quantify the benefits of crosslayer approaches when the
scenario environmental conditions change. So we will test the behaviour of DACAP MAC protocol
with two routing protocols, a static routing protocol (always supplies the neighbour with the nearest
node to gateway) and the FBR routing protocol. Also we will include in our experiments two different
propagation models, Thorp and Bellhop. For the simulation parameter we will use the ones in Table 1
except for the propagation models, we only use Thorp and Bellhop, the global network load is fixed
to 3 packets/s, and we define an MH scenario configuration.
Figure 15 depicts and interesting behavior of DACAP protocol in terms of goodput performance. In
addition there is a clear indication that also, at the routing layers, the environmental conditions of the
network scenario may considerably impact in the results of network performance.
Figure 15. Goodput results with DACAP + Routing using two different propagation models.
As also shown in previous results, the Thorp propagation model does not take into account
environmental conditions, so it plots a constant goodput value. As, expected the static routing protocol
gets better goodput results because FBR has an extra waiting time in order to accept more than one
CTS, but as every node has always the same reachable nodes in its neighborhood it always choses the
0.04
0.06
0.08
0.1
0.12
0.14
1234567891011
Goodhput (packets/s)
Wave Height (meters)
THORP STATIC
THORP FBR
BELLHOP STATIC
BELLHOP FBR
Sensors 2012, 12
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same node to reach to the gateway, that is the reason why using static routing in ideal conditions is a
better option. If we use Bellhop the static alternative loses performance as the attenuation grows due to
the physical changes, meanwhile FBR performance is not so affected in worse conditions as it can
dynamically change the routing paths when a connection link is lost.
6. Conclusions
One of the main difficulties to compare and validate the performance of different UWSN proposals
is the lack of a common standard to model the acoustic propagation in the underwater environment. In
this paper we analyzed several underwater acoustic propagation models from a simple approach to
more detailed and accurate models, in order to study whether differences between then may seriously
impact in the performance evaluation of higher layer protocols. As a first conclusion we found that
accurate and low-complexity propagation models are required for network simulation in order to
obtain reliable results attained to the specific scenario and environmental parameters.
Also we perform several simulation experiments to determine the sensibility of higher layer
protocols (MAC and routing protocols) to propagation models and scenario environmental parameters.
From the obtained results, we conclude that: (a) n-way handshake protocols, like CSMA/CA or
DACAP suffer from high packet delays, but they show better behavior in terms of goodput and energy
consumption; and (b) crosslayer approaches between routing and MAC layers are required to improve
network performance, so it is highly recommended to allow routing protocols to get appropriate
feedback from MAC layer about network and environmental conditions found at physical layer, since
in UWSNs we showed the impact of physical layer modeling on network performance.
The importance of not only choosing a realistic propagation model but also defining with precision
the environment, starting with the geographic position and the parameters that we can obtain from it to
the physical environment conditions like the season of the year or the ocean wave motion has been
settled, so when designing network architectures for UWSNs, the role of physical layer should be
seriously taken into account to be in a position to assert that simulation results will be close to the ones
obtained in real network scenarios.
Acknowledgments
This work was supported by the Ministry of Science and Education of Spain under Project
DPI2007-66796-C03-03.
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The growth of Internet-of-Things (IoT) technologies and the rapid advances in image-and video-based applications make it necessary to select the most appropriate network protocols that ensure reliable, efficient and robust system performance. This study presents a framework to help select appropriate network protocols for Narrowband IoT applications. This framework evaluates and compares the most popular wireless and wired IoT technologies, especially Narrow-band IoT (NB-IoT) and 5G, based on their characteristics, strengths and limitations. The research considers factors such as latency, bandwidth, scalability, energy efficiency and reliability to determine the suitability of these protocols for each application area. This study investigated the performance of these technologies in different environments, including underwater, airborne and land environments. By providing a structured approach to selecting the optimal network protocol for specific applications, this study aims to streamline and improve the deployment and performance of IoT technologies in various industries.While this study offers comprehensive insights into the optimal selection of network protocols for various IoT applications, it primarily focuses on popular technologies and does not account for emerging technologies.
... In the relevance to UWSNs, design of routing protocol in underwater environment requires intelligent system performance. However, hierarchical communication to the BS [15] encounters high delay bound due to Limited Bandwidth -Communication bandwidth is shortened over extended range by varying aspects which holds high medium absorption of sound at below density and low medium absorption at above density. Propagation Delay -Acoustic signal is employed in underwater environment where nodes move on water irrespective of distance, temperature and salinity thereby transmission speed is around 2x10 5 times slower than the terrestrial Wireless Sensor Networks (WSNs). ...
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Underwater Wireless Sensor Networks (UWSNs) reveal a diverse range of applications among varied networks where sensors are deployed for exploring resourceful activities such as tactical surveillance, ocean monitoring, offshore analysis, oceanographic data collection and instrument observing. All these activities are based on the number of sensors deployed in ocean for data collection and communication. Naturally, underwater medium through which the data transmits from source to destination i.e. network is volatile. Despite, sensing and transmitting over a selective range in UWSNs signifies to be challenging with relevance to limited bandwidth, long propagation delay and severe multipath fading. This research explicitly defines the recent proposed routing protocols in terms of clustering techniques. In addition, the research work revealed the summary of clustering protocols in UWSNs together suggesting future research exploration in the field of underwater environments.
... Applications related to the acoustic propagation modeling in oceans can be broadly categorized into two main classes: forward problems and inverse problems. The forward problems seek to estimate the acoustic field at various receiver locations assuming all required environmental parameters are known [2]- [4]. On the other hand, inferring unknown environmental parameters from acoustic measurements is of interest to the inverse problems [5]- [8]. ...
Preprint
Acoustic propagation models are widely used in numerous oceanic and other underwater applications. Most conventional models are approximate solutions of the acoustic wave equation, and require accurate environmental knowledge to be available beforehand. Environmental parameters may not always be easily or accurately measurable. While data-driven techniques might allow us to model acoustic propagation without the need for extensive prior environmental knowledge, such techniques tend to be data-hungry and often infeasible in oceanic applications where data collection is difficult and expensive. We propose a data-aided ray physics based high frequency acoustic propagation modeling approach that enables us to train models with only a small amount of data. The proposed framework is not only data-efficient, but also offers flexibility to incorporate varying degrees of environmental knowledge, and generalizes well to permit extrapolation beyond the area where data was collected. We demonstrate the feasibility and applicability of our method through four numerical case studies, and one controlled experiment. We also benchmark our method's performance against classical data-driven techniques.
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Practical acoustic propagation modeling is significantly affected by ocean dynamics, and then can be exploited in numerous oceanic applications, where “practical” refers to modeling acoustic propagation in simulations that mimic real-world ocean environments. Physics-based numerical models provide approximate solutions of wave equation and rely on accurate prior environmental knowledge while the environment of practical scenarios is largely unknown. In contrast, data-driven machine learning offers a promising avenue to estimate practical acoustic propagation by learning from dataset. However, collecting such a high-quality, noise-free, and dense dataset remains challenging. Under the practical hurdle posed by the above approaches, the emergence of physics-informed neural network (PINN) presents an alternative to integrate physics and data but with limited representation capacity. In this work, a framework, termed spatial domain decomposition-based physics-informed neural networks (SPINNs), is proposed to enhance the representation capacity in spatially dependent oceanic scenarios and effectively learn from incomplete and biased prior physics and noisy dataset. Experiments demonstrate SPINNs' advantages over PINN in practical acoustic propagation estimation. The learning capacity of SPINNs toward physics and dataset during training is further analyzed. This work holds promise for practical applications and future expansion.
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Acoustic propagation models are widely used in numerous oceanic and underwater applications. Most conventional models are approximate solutions of the acoustic wave equation, and require accurate environmental knowledge to be available beforehand. Environmental parameters may not always be easily or accurately measurable. While data-driven techniques might allow us to model acoustic propagation without the need for extensive prior environmental knowledge, such techniques tend to be data-hungry and often infeasible in oceanic applications where data collection is difficult and expensive. We propose a data-aided physics-based high-frequency acoustic propagation modeling approach that enables us to train models with only a small amount of data. The proposed framework is not only data-efficient, but also offers flexibility to incorporate varying degrees of environmental knowledge and generalizes well to permit extrapolation beyond the area where the data were collected. We demonstrate the feasibility and applicability of our method through four numerical case studies, and one controlled experiment. We also benchmark our method's performance against two classical data-driven techniques—Gaussian process regression and deep neural network.
Conference Paper
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One of the main difficulties when modeling Underwater Wireless Sensor Network (UWSN) has to do with the environment characteristics and the acoustic signal typically used. As stated in other works, the accuracy of the acoustic propagation model and the network scenario conditions are critical to obtain reliable results. In this work we proposed a simulator framework for UWSN modeling. For that purpose we have considered the information provided by global databases (temperature, salinity, etc.) located within the network. Namely, the bathymetry and floor sediment, node depth, wave effect, and other factors may affect to the underwater signal propagation behavior. The propagation model is calculated using Bellhop ray tracing tool in order to get the closest representation to the real behavior of the acoustic signal propagation. All these tools are integrated in OPNET Modeler. Finally, we have run several experiments in different locations testing network performance with a simple MAC protocol.
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The development of protocols to advance the state of the art in underwater acoustic networks (UANs) relies on the use of computer simulations to analyze protocol performance. It is typical for designers to abstract away much of the detail of the physical environment in order to simplify the development of the simulation and ensure the simulation runtime performance is reasonable. The validity of the simulation results becomes questionable. There are, though, very high fidelity models developed by acoustic engineers and physicists for predicting acoustic propagation characteristics. In addition to these models, empirical data collections have been generated for many geographic regions of interest to UAN planners. However, incorporating these engineering and physics models or data collections into a network simulation is problematic, as the models are computationally complex and the data sets are not directly usable for acoustic signal propagation characterization. This paper presents a statistical method for developing a computationally efficient and simulation friendly approximation of a physics model of path loss. This method may also be used to adapt empirical data sets for use in network simulation in the same manner. The method was applied to the output of the Monterey-Miami Parabolic Equation model to assess its impact on the runtime performance of an OPNET-based simulation. Results of that simulation are compared to results from a previous OPNET simulation that simply used distance to determine reception. The simulation results confirm the incorporation of the MMPE approximation does not noticeably impact the runtime performance of the simulation. Anecdotally, the simulation confirms earlier results that suggest contention-based access controls without collision avoidance techniques may outperform the typical access technique adapted from wireless radio networks that employs collision avoidance, contrary to conventional wisdom
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In the last years, wireless sensor networks have been proposed for their deployment in underwater environments where a lot of applications like aquiculture, pollution monitoring and offshore exploration would benefit from this technology. Despite having a very similar functionality, Underwater Wireless Sensor Networks (UWSNs) exhibit several architectural differences with respect to the terrestrial ones, which are mainly due to the transmission medium characteristics (sea water) and the signal employed to transmit data (acoustic ultrasound signals). So, the design of appropriate network architecture for UWSNs is seriously hardened by the specific characteristics of the communication system. In this work we analyze several acoustic channel models for their use in underwater wireless sensor network architectures. For that purpose, we have implemented them by using the OPNET Modeler tool in order to perform an evaluation of their behavior under different network scenarios. Finally, some conclusions are drawn showing the impact on UWSN performance of different elements of channel model and particular specific environment conditions
Conference Paper
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Network simulators are a fundamental tool for the performance evaluation of protocols and applications in complex scenarios, which would be too expensive or infeasible to realize in practice. With the aim to provide a shared environment for the simulation of underwater networks we have adapted the ns2 network simulator to provide a detailed reproduction of the propagation of sound in water (i.e., by means of ray tracing instead of empirical relations). This has been tied to formerly available simulation frameworks (such as the MIRACLE extensions to ns2) to provide a completely customizable tool, including acoustic propagation, physical layer modeling, and cross-layer specification of networking protocols. In this paper, we describe our tool, and use it for a case study involving the comparison of three MAC protocols for underwater networks over different kinds of physical layers. Our results compare the transmission coordination approach chosen by each protocol, and show when it is better to rely on random access, as opposed to loose or tight coordination.
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AUVNetSim is a simulation library for testing acoustic networking algorithms. It is written in Python and makes extensive use of the SimPy discrete event simulation package. AUVNetSim is interesting for both end users and developers. A user willing to run several simulations using the resources that are already available, can easily modify several system parameters without having to explicitly deal with Python code. For example, a developer who wants to include a new MAC protocol can simply do so by taking the advantage of the existing structure.
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The method of Gaussian beam tracing has recently received a great deal of attention in the seismological community. In comparison to standard ray tracing, the method has the advantage of being free of certain ray‐tracing artifacts such as perfect shadows and infinitely high energy at caustics. It also obviates the need for eigenray computations. The technique is especially attractive for high‐frequency, range‐dependent problems where normal mode, FFP, or parabolic models are not practical alternatives. The Gaussian beam method associates with each ray a beam with a Gaussian intensity profile normal to the ray. The beamwidth and curvature are governed by an additional pair of differential equations, which are integrated along with the usual ray equations to compute the beam field in the vicinity of the central ray of the beam. We have adapted the beam‐tracing method to the typical ocean acoustic problem of a point source in a cylindrically symmetric waveguide with depth‐dependent sound speed. We present an overview of the method and a comparison of results obtained by conventional ray‐tracing, beam‐tracing, and full‐wave theories. These results suggest that beam tracing is markedly superior to conventional ray tracing.