A new Algorithm Based on Discrete Fourier Transform to Improve the Lifetime of Underwater Wireless Sensor Networks Communications

Abstract

WSNs) have become an important means of gathering environmental and physical information from a wide range of areas. WSNs could be used in underground, aboveground and underwater applications. In this paper, we focus on underwater transmission. One of the main limitations of WSNs is the power consumption and short lifetime of the sensors. In this paper, we propose a new solution for underwater Wireless Sensor Networks to overcome the problem that is caused by the ionized nature of seawater. This work presents a methodology to improve the lifetime of WSNs. The wireless sensors have three main functions: sensing, processing and transmitting. The first two factors consume very less power compared to the third. Thus, we need to guarantee the successful transmission of signal with nominal and efficient use of power to improve the lifetime of the sensors. Improving the lifetime of these sensors will improve the experience of the end user, as the information-gathering lifetime of the sensors increases. Based on the results presented in this paper, we can reduce the power consumption, thus improving the lifetime and the signal loss rate.

Full text

Abstract—Wireless Sensor Networks (WSNs) have become an
important means of gathering environmental and physical
information from a wide range of areas. WSNs could be used in
underground, aboveground and underwater applications. In this
paper, we focus on underwater transmission. One of the main
limitations of WSNs is the power consumption and short lifetime
of the sensors. In this paper, we propose a new solution for
underwater Wireless Sensor Networks to overcome the problem
that is caused by the ionized nature of seawater. This work
presents a methodology to improve the lifetime of WSNs. The
wireless sensors have three main functions: sensing, processing
and transmitting. The first two factors consume very less power
compared to the third. Thus, we need to guarantee the successful
transmission of signal with nominal and efficient use of power to
improve the lifetime of the sensors. Improving the lifetime of
these sensors will improve the experience of the end user, as the
information-gathering lifetime of the sensors increases. Based on
the results presented in this paper, we can reduce the power
consumption, thus improving the lifetime and the signal loss rate.
Index Terms— under water communication, WSNs, frequency,
energy consumption.
I. INTRODUCTION
Underwater sensor networks have a wide variety of
applications in real world. They can be used in Ocean
Sampling Networks, Pollution, chemical, biological,
environmental Monitoring, alerting swimmers about
dangerous bacteria levels, disaster prevention, assisted
navigation, distributed tactical surveillance, mine
reconnaissance, etc [1].
Despite the tremendous potential and use of the
technology, there has not been a whole lot of research and
development conducted on it yet because there are several
challenges.
_________________________
Manuscript received January 21, 2014.
W. Elmannai is a Ph.D. candidate in the Department of Computer Science
and Engineering at the University of Bridgeport, Bridgeport, CT 06604 USA
(e-mail: welmanna@my.bridgeport.edu).
K. Elleithy is the Associate Dean for Graduate Studies in the School of
Engineering at the University of Bridgeport, Bridgeport, CT 06604 USA (e-
mail: elleithy@bridgeport.edu).
A. Shrestha is a Ph.D. candidate in the Department of Computer Science
and Engineering at the University of Bridgeport, Bridgeport, CT 06604 USA
(e-mail: shrestha@my.bridgeport.edu).
M. Alshibli is a Ph.D. candidate in the Department of Computer Science
and Engineering at the University of Bridgeport, Bridgeport, CT 06604 USA
(e-mail: malshibl@my.bridgeport.edu).
R. Alataas is a Ph.D. student in the Department of Computer Science and
Engineering at the University of Bridgeport, Bridgeport, CT 06604 USA (e-
mail: ralataas@my.bridgeport.edu).
In addition to the well-known resources constraints of the
WSNs, Underwater WSNs present even more restrictions that
are preventing its proliferation [2, 3]. Unlike the terrestrial
networks, Underwater WSNs face multi-path propagation [4]
and very long and irregular signal attenuation over long
distances. They are also prone to lower bandwidth because
lower frequency acoustic waves are used instead of higher
frequency electro-magnetic waves to transmit data underwater.
They also require more power to operate than their ground
counterparts. These and other restrictions have been impeding
its development.
Our focus in this paper is to improve the lifetime of
WSNs and to increase the distance of signal propagation.
Moreover, we propose a framework to alleviate the limitation
of wireless communication due to underwater environmental
conditions.
A. Underwater Wireless Sensor Networks Deployments
(UWSNs)
Historically underwater monitoring and data collection
were performed by recording devices that did not
communicate in real time. They simply recorded data and
were retrieved at a later point by physically collecting the
devices. This would be akin to the use of computers before the
advent of the Internet. The disadvantages of the traditional
approach included lack of real-time monitoring, inability to
interact with control station and limitation on the amount of
collected data [5]. Current state-of-art deployments of UWSNs
have overcome these shortcomings.
In a typical UWSN as shown in Figure 1.1, a varied range
of data including temperature, current, biological (ecosystem
productivity), chemical (nutrient fertilization), etc., is
collected, processed and finally transmitted by underwater
sensors to shore data-collection stations all in real-time [7].
The figure demonstrates the typical deployment of UWSNs.
The sensors at the bottom of the ocean floor collect process
and relay the data to the surface substation in the moving ship,
which in turn transmit the collected information to the onshore
sink.
UWSNs deployment differs from terrestrial WSN as
shown in Figure 1.2 deployments in a number of ways. The
UWSNs are undoubtedly more complex and vulnerable to
environmental conditions. The sensors have limited battery
power and are extremely difficult, if not impossible to
recharge. Due to the fact that acoustic waves are used instead
of RF or microwaves, the data transfer rates are very low,
A New Algorithm Based on Discrete Fourier
Transform to Improve the Lifetime of Underwater
Wireless Sensor Networks Communications
Wafa Elmannai, Khaled Elleithy, Ajay Shrestha, Mohamed Alshibli, Reem Alataas

which is further aggravated by propagation delays. The
underwater communication also suffers from multi-path
propagation [8] as shown in Figure 1.2.
Figure 1.1: An Underwater Acoustic Tested at the University at
Buffalo [6]
Figure 1.2: Diagram showing underwater Multi-path Propagation
[9]
The differences in the environmental and communication
channel characteristics with the terrestrial counterparts, calls
for a modification of communication protocols, localization
algorithms, error recovery protocols and several other design
parameters to fit the needs of UWSNs.
Despite these limitations and challenges, UWSNs have
tremendous potential and use to address real world problems.
Many governmental and private institutions have identified it
as a major research area and have invested in its development.
II. PROBLEM IDENTIFICATION
Unlike terrestrial WSNs, electromagnetic waves (RF) are
not used in UWSN because the microwave frequency radio
waves are absorbed by water. Thus, sound waves are used as a
medium to propagate the signal between two points. The
biggest challenge for underwater (sea water) communication is
that sea water is an ionized medium. Thus, the propagation of
sound gets dissipated as the sound travels through the sea
water. The sonar frequency has been used as a long term
strategy for underwater communications where the
communication needs to be done with a trained ear. In this
paper, we propose a technique that incorporates underwater
communications for sonar frequencies and make it more user-
friendly. III. RELATED WORK
During the last decades, developing and improving
communications of underwater have become an important
area, not only for military issues but also for environmental
reasons. Developing safe and efficient way to communicate
underwater is important for improving the field of study of
underwater world. One of the main tools that are used to keep
observation underground is the underwater communication.
Sensors are used for this purpose but there are many
challenges, e.g., the battery lifetime and the noise effect on the
propagated signal. Thus, we focused on how to improve
underwater communications. This section provides an
overview of the recent developments in this field.
Yusof et. al. [10] presents an in-depth review of
underwater communication based on sonar and
electromagnetic waves, a comparison of the two systems and a
discussion of the environmental impacts of using these waves
for underwater communication. As a tradeoff between
preserving the underwater environment and the need for
underwater communication, it appears that underwater
electromagnetic wave communications have the most potential
to be the environmentally friendly system of the future.
Construction of underwater communication systems may use
either sonar wave or electromagnetic wave. The passive sonar
communication system simply picks up any sonar waves
propagating in the underwater but the active sonar
communication system receives as well as emits sonar wave in
the underwater. Both passive and active mode use a
hydrophone as the device for emitting or receiving sonar
waves in the underwater itself. These sonar communication
modes are not practical for air-water trans-boundary
communication due to significant amplitude attenuation as
they cross the air-water interface. Alternatively,
electromagnetic wave communication systems are practical
for cross-boundary air-water underwater communication
almost without any limitation of speed and amplitude
attenuation.
Pompili et. al. [11] discuss efficient communication
protocols among underwater devices, which are based on
acoustic wireless technology for distances over one hundred
meters that need to be enabled because of the high attenuation
and scattering affecting radio and optical waves, respectively.
The unique characteristics of an underwater acoustic channel
such as very limited and distance-dependent bandwidth, high
propagation delays, and time varying multipath and fading
require new efficient and reliable communication protocols to
network multiple devices, either static or mobile, potentially
over multiple hops. This paper also discusses solutions for
medium access control, routing, transport-layer, and cross-
layer networking protocols.

Stojanovic [12] traces back the idea of sending and
receiving information under water all the way to the time of
Leonardo Da Vinci who is quoted for discovering the
possibility to detect a distant ship by listening on a long tube
submerged under the sea. In the modern sense of the word,
underwater communications began to develop during the
Second World War for military purposes. One of the first
underwater communication systems was an underwater
telephone which was developed in the United States for
communicating with submarines. While many problems
remain to be solved in the design of high speed acoustic
communication systems, recent advances in this area will
serve as an encouragement for future work and enable us to
remotely explore the underwater world.
Akyildiz et. al. [13] discusses the main challenges of
efficient communications in underwater acoustic sensor
networks. The paper outlined the peculiarities of the
underwater channel with particular reference to networking
solutions or monitoring applications of the ocean environment.
The objective of this paper is to encourage research efforts to
lay down fundamental basis for the development of new
advanced communication techniques for efficient underwater
communication and networking to enhance ocean monitoring
and exploration applications. The paper presents various 2-D
and 3-D models that can be used as the basis of future
research.
Lloret [14] compares a proposed communication system
with other existing systems. Although the proposal supports
short communication distances, it provides high data transfer
rates. It can be used for precision monitoring in applications
such as contaminated ecosystems or for device
communications at high depth. The authors have proposed a
cheap and efficient way for underwater communications using
IEEE 802.11 devices at 2.4GHz transmission.
Llor et. al. [15] presents the various parameters used for
underwater communication. The paper discusses the
transmission distance and frequency. Furthermore, the authors
investigate the multipath loss. Finally, the paper addresses the
modulation and demodulation of the signal for underwater
communication.
IV. PROPOSED WORK
In this paper, we introduce a compression sound
algorithm using Fourier series and Morse code in order to
improve the wireless underwater sensor communication. Our
main purpose of integrating Fourier series and Morse code is
to propagate the sound at various frequencies. This will enable
the communication to withstand longer multipath propagation.
Furthermore, we introduce GUI from text to sound
conversion. Figure 2 shows the steps of the 
process.
A. Morse Code:
Morse code is used to convert the text to on-off tones
series, clicks or lights. This can enable the observers and
skilled listener to understand the meaning with no specific
equipment.
In fact, Morse code is considered as less sensitive to poor
signal than the voice. But without a decoding device, it is still
a challenge for most researchers. Therefore, in this case the
Morse code can be considered as a useful technique to
produce speech for distribution of an automatic data to train
listeners on voice channels. That can make the first part of our
algorithm more efficient in transferring the sound to the data.
Figure 2: Flowchart of Proposed Model of Wireless Underwater Sensor
Communication
B. Discrete Fourier Transform (DFT):
DFT (Discrete Fourier Transform) or FFT (Fast Fourier
Transform) concepts are used interchangeably, and both work
with a finite set of values. DFT is used in many mathematical
modules where it is fast, since it could be built recursively to
reduce the amount of time in execution. DFT or FFT is used to
convert from time domain to frequency domain. We can work
with the data in the frequency domain easier than the data in
the time domain.
C. Compression Algorithm:
Analog audio compression allows the efficient storage
and transmission of sound data.
Figure3: Digital to Analog Conversion
Figure 3 shows how the digital audio resulting from
running Fourier Transform is converted to analog signals that
are ready for compression.
In this implementation, we use the µ-law transformation
which is a basic audio compression technique. This

transformation is a form of logarithmic data compression for
an audio data. Due to the fact that we hear logarithmically,
sound recorded at higher levels does not require the same
resolution as low-level sound. This allows us to disregard the
least significant bits in high-level data. This turns out to
resemble a logarithmic transformation. The resulting
compression forces a 16-bit number to be represented as an 8-
bit number.
The logarithmic step spacing represents low-amplitude
audio samples with greater accuracy than higher-amplitude
values. Thus the signal-to-noise ratio of the transformed
output is more uniform over the range of amplitudes of the
input signal. The µ -law transformation equation is:





  

  
Where µ = 255, and x is the value of the input signal
normalized to have a maximum value of 1.
V. SIMULATION SETUP
We used C# programming language to implement our
approach. The simulation is used to tune the parameters in
order to improve the distance in where that signal propagates
during transmission. There are some important parameters that
can affect the transmission. It can either reduce or increase the
power consumption. Hence, if these parameters are not set to
the optimal values, the transmitted signal will be lost due to
the ionized nature of sea water [15]. Table 1 shows the initial
values that can be adjusted using the implemented simulator.
TABLE I
INITIALIZING MORSE CODE AND DFT PARAMETERS
Parameters
Initial Values
Short beep
0.04 sec
Long beep
0.1 sec
Delay
0.05 sec
Frequency
1000 HZ
VI. SIMULATION RESULTS
Fresh water is ideal for underwater communications
because of the absence of ionization. But sea water is ionized,
thus it makes the sound dissipate faster. We have used DFT to
compress sound signals to a lower frequency to increase the
sound propagation distance. This allows us to control the
propagation distance while ensuring the security of the
information transferred as shown in Figure 4. We do this by
modifying the long and short beeps for the Morse code to
create new encryption methods to mask the original messages.
Figure 4: Text to Sound Converter based Morse code
A. Time Average versus Distance Scenario:
Figure 5 is generated for various sound frequencies over a
time average versus distance propagation. It represents a linear
format of graphical representation. It shows that increasing
sound frequency reduces the time average, but it reduces the
propagation distance too. With the reduction in sound
frequency, there is an increase in the time average, as well as
increase in the propagation distance. From our results, we can
conclude that the best solution is to have the maximum
propagation distance with the least time average that is close
to zero.
Figure 5: Time Average vs. Distance Propagation Scenario
B. Frequency versus Distance Average Scenario:
Figure 6 shows the optimal frequency which is required
for the best propagation distance that is based on the
frequency values. Since our proposed application allows us to
change and compress the sound frequency as shown in Figure
4, we can optimally set the frequency value based on the
propagation distance as required. However, the achieved chart
is based on the seawater conditions.

Figure6: Frequency (X-axis) vs. Distance (Y-axis) Average Scenario
VII. CONCLUSIONS
In this paper, we implemented an algorithm to integrate
both Morse code to convert the text to series of tones and DFT
to compress the sound signal. This algorithm addresses
important problems regarding real-time wireless underwater
communication, propagation and ionization.
Propagation distance and ionization of the seawater are
some of the most challenging issues of underwater
communications. Sound in seawater propagates up to a certain
distance and then dissipates.
We presented an efficient algorithm using Morse code
and DFT to modify the sound signal based on our
requirements. The algorithm tunes the frequency to an optimal
value that increases the propagation distance without
increasing power consumption.
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