# Energy Efficient Routing For Underwater Acoustic Sensor Network Using
Genetic Algorithm

Arjun Prasad Chaurasiya
Department of Computer Science & Engineering
Vel Tech University
Chennai  India
<EMAIL_ADDRESS>Roshan Sah
Department of Aerospace Engineering
Indian Institute of Technology  Kharagpur
Kharagpur  India
<EMAIL_ADDRESS>Dr.V.Sivakumar
Department of Computer Science & Engineering
Vel Tech University
Chennai  India
<EMAIL_ADDRESS>
###### Abstract

In underwater acoustic sensor networks (UWASN), energy-reliable data
transmission is a challenging task . This is due to acoustic transmission
disturbances caused by excessive noise, exceptionally long propagation delays,
a high bit error rate, limited bandwidth capability, and interference. One of
the most important issues of UWASN for research is how to extend life span of
data transmission. Data transfer from a source node to a destination node in
UWASN is a complicated topic for researchers. Many routing algorithms, such as
vector base forwarding and depth base routing, have been developed in past
years.We propose a genetic algorithm-based optimization method for improving
the energy efficiency of data transmission in the routing Path from a source
node to a destination node.

Keywords: Underwater Acoustic Sensor Networks, Routing Techniques, network
lifetime, Acoustic communications.

###### Contents

  1. 1 Introduction
  2. 2 Literature Review
  3. 3 Proposed Routing Scheme
    1. 3.1 A. Modifed Genetic Algorithm
    2. 3.2 Flowchart :
      1. 3.2.1 Begin
      2. 3.2.2 Initial population
      3. 3.2.3 Fitness
      4. 3.2.4 Selection
      5. 3.2.5 Crossover
      6. 3.2.6 Selection
      7. 3.2.7 Termination
    3. 3.3 Difference between Underwater Acoustic Sensor Network(UWASN) and Terrestrial Wireless Sensor Network(TWSN)
  4. 4 Implementation
    1. 4.1 Module 1: Configure the data and run in the terminal
    2. 4.2 Module 2: Launch the “nan” in the terminal
    3. 4.3 Module 3: Final stage of simulation
    4. 4.4 Module 4: Node movement in nam
  5. 5 Performance Evaluation
  6. 6 Conclusion
  7. 7 Future Enhancements

##  1 Introduction

According to a new survey undertaken by the United States National Oceanic and
Atmospheric Administration (NOAA), oceans occupy nearly 97 percent of the
Earth’s surface [1]. There is an increasing interest in studying the
underwater environment for a variety of purposes, including climate change,
oceanic animal research, oil platform tracking, surveillance, and unmanned
operations. All of these applications involve a medium of communication both
inside the underwater environment and the rest of the world. As a result,
research into underwater wireless communication has gained a lot of interest.

Communication devices based on acoustic waves, high frequency waves, and
optical waves are used to introduce underwater wireless communications today.
Since they can communicate over long distances, underwater acoustic wireless
networks have become one of the most commonly used underwater wireless
communications systems. In UWASN which are made up of several autonomous and
self-organizing sensor nodes. To collect exact information from deep water,
these nodes are manually dispersed in various depths in underwater
environments. Collecting data to a sink on the water’s surface through
acoustic waves. The normal sensor nodes are present in these networks, along
with an acoustic modem for connectivity. Sinks are fitted with both acoustic
and radio modems, allowing them to receive data from underwater nodes via
acoustic waves and transmit it via radio waves to the base station.

The traditional method of disaster control is to deploy an underwater sensor
and manually collect data [2]. The disadvantage of the traditional approach of
not being able to provide timely information inspired a wave of interest in
the concept of an underwater sensor network. To deal with these disasters
using an underwater sensor network, a reliable communication medium to
exchange data is needed. Electromagnetic waves are initially effective for
short-distance communication, but due to high attenuation in the sea, they are
insufficient for long-distance communications [3], while optical waves are
easily absorbed and dispersed in the water, allowing for very short-distance
communication [4]. Acoustic waves resolve the above limitations and can
transmit over long distances with low frequency absorption and attenuation
[5]. Since an acoustic wave propagates as a pressure wave, it may transmit its
energy over long distances. However, because of the low speed of the audio
signal underwater (approximately 1500 m/s), the acoustic signal has its own
drawbacks, such as restricted bandwidth [BW] and long transmission delay [6].

Several quality of service awarerouting protocols, such as vector-based
forwarding (VBF) [7] and depth-based forwarding (DBR) [8], have been proposed
for UWASN in recent years. In terrestrial wireless sensor networks, a wide
number of network applications have been proposed for determining a route from
source to sink.These protocols are built on an end-to-end approach, which is
ineffective in high-dynamic topology networks with long propagation delays.
Since UWASN is such a new topic in this field, most researchers are focusing
on the physical layer, connection layer, and localization, while network layer
analysis is still in its infancy. As a result, only a few routing protocols
for UWASN have been established. Optimization technique to increase the energy
efficiency of data transmission in routing Path from a source node to a
destination node in UWASN is the most promising approach in an underwater
environment.

This paper is organized as follows: Section I presents the Introduction model,
Section II present the Literature Review, Section III presents the Proposed
Routing Scheme, Section IV present Implementation model, Section V present the
performance evaluation study. Finally, Section VI Conclusion and Future
Enhancements.

##  2 Literature Review

In the field of underwater acoustic sensor network, several studies have used
a number of ideas and methods that have proven to be successful. Many
researchers have proposed a number of algorithms in underwater acoustic sensor
network ; we’ve briefly listed a few of them below.

A. Wahid et al.[9] propose a functional node cooperation (NC) protocol to
improve collection efficiency by taking advantage of the fact that underwater
nodes can hear other nodes’ transmissions.The underwater data collection area
is divided into several sub-zones to minimise the source level of underwater
nodes, but in each sub-zone, the mobile surface node using the NC protocol
could move adaptively between selective relay cooperation (SRC) and dynamic
network coded cooperation (DNC).

X. Zhuo et al. [10] purpose of this report is on the use of underwater
acoustic sensor networks (UASNs), which are distinguished by their large
scale, dispersed distribution, and differing traffic loads. The strategy to
access the popular communication medium is needed to improve the efficiency of
UASNs because the underwater sensor channel is known for its restricted
bandwidth, time variation, and high propagation delays.

M. Molins et al. [11] proposed a new protocol is known as slotted FAMA because
it employs time slotting. Time slotting reduces the need for unnecessarily
long control packets, resulting in energy savings. Via simulation of a mobile
ad hoc underwater network, protocol efficiency in terms of throughput and
delay is evaluated, demonstrating the presence of an acceptable rated power to
be used for a given user density.

Hongyu Cui et al.[12] present the platforms built by our lab, which are based
on the well-known ns2 and ns-miracle simulators and feature a layered
structure that supports cross-layer signalling. The simulation platform runs
on a machine with the Linux operating system installed. The platform’s
architecture is divided into four layers: an application layer, a routing
layer, a medium access layer, and a data access layer.

Jun-Hong et al.[13] present a novel networking model for exploring aqueous
environments is the large-scale mobile underwater wireless sensor network
(UWSN). Remote UWSNs, on the other hand, vary greatly from ground-based
wireless sensor networks in terms of communication bandwidth, propagation
delay, moving node mobility, and failure probability.

Z. Wu et al.[14] presented a new underwater acoustic network localization
algorithm is implemented. To improve the sensitivity of random noise, the
algorithm uses the raw data before calculating the localization. We use a more
reliable set of coordinates and a modified calculation technique to reduce the
consistency of the calculation results. The new algorithm is better suited to
the underwater acoustic sensor network.

Present Aqua-Sim,P. Xie et al.[15] a network simulator for underwater sensor
networks. Aqua-Sim is based on NS-2, among the most commonly used network
simulators. It is constructed in an object-oriented manner, with all network
entities configured as classes. Aqua-Sim accurately simulates underwater
acoustic channel attenuation and impact behaviours in long delay acoustic
networks.

Our method is implemented T. Hu et al.[16] on standard MAC protocols and aims
to increase the lifespan of networks by uniformly distributing residual energy
from sensor nodes. All throughout routing process, the energy consumption of
each node, and also the power generation among a group, is taken into account
to measure the reward function, which aids in the selection of appropriate
packet forwarders.

The Yi Cui et al.[17] maximum lifetime routing problem is given a novel
utility-based nonlinear formulation. A completely distributed localised
routing algorithm based on this concept is also introduced, and it is shown to
converge at the optimum stage, where the network lifetime is maximised. To
validate the proposed solution, proper mathematical analysis and simulator
results are presented.

A. Sankar et al.[18] propose a distributed routing scheme that, in a linearly
low percent error, reaches the optimal solution. Our method is formulated on
the basis of a multi-commodity flow, which allows various power consumption
models and bandwidth constraints to be considered. It works with both static
and dynamic networks that shift slowly.

V. D. Park et al.[19] presented for remote, multihop wireless networks, we
present a new distributed routing mechanism. The protocol is part of a group
of algorithms known as ”connection reversal” algorithms. The protocol’s
response is organised as a time-ordered series of dispersing calculations,
each of which is made up of a set of guided connection reversals.

##  3 Proposed Routing Scheme

To achieve cooperative sampling of a two-dimensional (2-D) ocean scenario, a
network of underwater sensors at various depths is deployed. The following
properties are considered in this model. First, in term of transmission range,
all randomly deployed sensor nodes have the same capabilities and located near
sea surface can communicate with each other directly.Second, each sensor node
uses a location information to determine its own location. Third, each node
not only travels in a specific horizontal position, but also slightly in a
vertical position. Fourth, the acoustic channel is symmetric, the energy
needed to transmit a message from source to destination is similar.Fifth, each
sensor node will change its low and high transmission power periodically.
Furthermore, it is believed that the sink node has a lot of resources and can
manage many packets at about the same time.

###  3.1 A. Modifed Genetic Algorithm

The Genetic Algorithm (GA) is a type of evolutionary heuristic search
algorithm that belongs to the evolutionary algorithms group. The Genetic
Algorithm is a search-driven optimization algorithm focused on genetics and
natural selection concepts. It’s often used to find ideal or near-optimal
solutions to complex problems that would require a lifetime to solve. It’s
widely used to solve optimization problems, as well as in science and machine
learning. They’re widely used to come up with high-quality solutions to
optimization and search issues. Genetic Algorithms are capable of delivering a
”good-enough” solution ”quickly enough.” As a consequence, genetic algorithms
are appealing for solving complex optimization problems.

Figure 1: Genetic Algorithm

###  3.2 Flowchart :

####  3.2.1 Begin

A population of n chromosomes is generated at random.

####  3.2.2 Initial population

The phase starts with a group of people known as a Population. Each person is
a possible solution to the problem you’re trying to solve. Genes are a
collection of parameters that define an organism.

####  3.2.3 Fitness

Chromosome’s fitness is calculated by an assigned fitness work. The fitness
function defines a person’s level of fitness. It assigns each individual a
fitness score. The fitness score determines the chances of a person being
chosen for reproduction.

####  3.2.4 Selection

Choose two chromosomes with the highest wellness prediction from the
population. The aim of the selection process is to find the fittest
individuals and allow them to transfer their genes down to future generations.

####  3.2.5 Crossover

A genetic algorithm’s most important step is crossover. A crossover point is
selected at random from within the genes for each pair of parents to be paired
up. Parents’ genes are shared among themselves to produce offspring until the
convergence point is reached. For example –

Figure 2: Genetic Algorithm

####  3.2.6 Selection

Any of the genes of certain new offspring may be exposed to a mutation with a
low crossover sampling. This means that some of the bits in the bit string can
be switched around. A mutation changes the value of one or more genes in a
chromosome from its original state. Mutation occurs to maintain diversity of
the population.

####  3.2.7 Termination

The population is reintroduced with the latest offspring. If the population
has converged, the algorithm will end. The genetic algorithm is being said to
have given a series of approaches to our problem.

###  3.3 Difference between Underwater Acoustic Sensor Network(UWASN) and
Terrestrial Wireless Sensor Network(TWSN)

In terms of various factors such as environmental conditions and communication
medium, UWASN differs significantly from TWSN, resulting in unique
characteristics and challenges. There are a few main differences are:

Table 1: Comparison of UWASN and TWSN  S.No. | Feature | UWASN | TWSN
---|---|---|---
1 | Architecture | Mostely 3D | Mostely 2D
2 | Node movement | Mobility | Fixed
3 | Propagation delay | high | low
4 | Frequency | Low | High
5 | Topology | High dynamic | Low
6 | Communication | Acoustic | Radio
7 | Path loss | High | Low
8 | Price | Expensive | Cheap
9 | Range | Vast Area | Small Area
10 | Sensor size | Large | Small

##  4 Implementation

The execution of these modules takes place on a terminal in a VMware sensor
machine, with input formats in the Network Simulator Version tcl format.

Figure 3: The nodes is moving towards the destination.

###  4.1 Module 1: Configure the data and run in the terminal

The data is installed and stored in the library feature in this module, which
can be used for future reference. The data code is configured using ns2
scripting, which is run in the terminal as ”$\\#$configure.” It goes through
all of the data that needs to be modified and then moves on to the next
version after all of the data has been configured.

Figure 4: The nodes is moving towards the destination.

###  4.2 Module 2: Launch the “nan” in the terminal

After configuring the data in the terminal, which tests and collects all of
the data, the process continues by installing nan in the terminal, which
includes all of the physical data constraints as well as a graphical
representation of the node mobility from source to destination.

Figure 5: Node moves from source node to destination node.

###  4.3 Module 3: Final stage of simulation

By running “ns project.tcl” in the terminal, which includes the entire
simulation module, the ended stage of the simulation component has been
achieved in this phase. This model includes all of the physical sorting lists
that must be built in order for the node movement to operate in a network
animator form, which is then visually presented.

###  4.4 Module 4: Node movement in nam

The node animator is used in the final phase of the system to display node
movement as it travels from source to destination through an underwater
acoustic sensor network. In the terminal, type ”nam out.nam” to see the
network animator’s dialogue while watching the node movement. When the
simulation is turned on, the nodes can travel from the source to the
destination in a specific amount of time in order to save energy for later
use. When the node arrives at its destination, the various movements of the
node are recorded in a visual analysis.

##  5 Performance Evaluation

PDR in all routing schemes gradually increases with the rise in round numbers
between 1 and 100, as shown in fig.6. When the network size is low, between 1
and 35, the VBF routing scheme performs better than the DBR routing scheme in
order to achieve higher PDR. When the size of the network is between 36 and
70, DBR performs slightly better than VBF in terms of achieving a higher PDR.
When the network size becomes large between round numbers 71 and 100. The PDR
of EER is found to be better than DBR and VBF routing schemes in all regions.
On the other hand, we find that DBR has a better PDR than VBF because data
packets are forwarding over better links among nodes in UWASN.

The efficiency of all routing schemes rapidly decreases with the increase in
nodes between 1 and 76, as shown in Fig.7. When the network has up to 76
sensor nodes, the delays rate efficiency of DBR is better than VBF.In all
three different regions, EER outperforms both DBR and VBF routing schemes in
terms of delay efficiency. We also note that DBR has a better delay
performance than VBF because it considers the shortest path when forwarding
data packets.

As shown in Fig. 8, the residual energy of sensor nodes decreases as the round
number between 1 and 100 increases in all of the routing schemes. Although
VBF’s energy consumption profile is better than DBR’s, DBR’s residual energy
output is better in round number between 36 and 70 and between 71 and 100. In
terms of residual energy efficiency, however, EER performs better both DBR and
VBF because it keeps sensor nodes alive throughout the rounds.

Figure 6: PDR versus round numbers between 1 and 100.

Finally, due to its novel genetic algorithm created to organise sensor nodes
into a connected structure in order to balance the load and prolong the
network lifetime. EER performs better than both DBR and VBF in terms of
network lifespan, as shown in Fig.9.

In Underwater Networking, as shown in Fig.10, Lifetime excellent efficiency in
terms of packet delivery ratio is achieved. For real-time UWASN-based
underwater applications, we have successfully enhanced data transfer
reliability for energy-delay. In the highly dynamic underwater environment,
EER significantly decreases the possibility of packet loss and maintains high
connection quality among nodes.

Figure 7: The delay versus node density between 1 and 76. Figure 8: The
residual energy versus number of round numbers between 1 and 100. Figure 9:
Network lifetime versus node density between 1 and 76.  Figure 10:  Underwater
Networking.

##  6 Conclusion

In this paper we have described genetic algorithm to transfer a data from
source node to destination node for underwater acoustic sensor network
applications is improved. Due to the growing propagation delays in the
underwater environment and the medium’s characteristic of being unable to
detect any collision, when any data is transmitted using the wireless medium,
energy consumption increases and data throughput falls. The results of the
performance show that EER performs exceptionally well in terms of metrics such
as PDR, average end-to-end packet loss, and energy consumption.

##  7 Future Enhancements

Future research will focus on increasing the quality and reliability of
underwater communication networks. The bandwidth, connection quality, and bit
error rate in UWASN are all lower than those of terrestrial radio frequency
channels, and work needs to be done to improve them.

## References

  * [1] NOAA, “How much water is in the ocean?,” in https://oceanservice.noaa.gov/facts/oceanfacts.php, 2017.
  * [2] K. M. Mridula and P. M. Ameer, “Localization under anchor node uncertainty for underwater acoustic sensor networks,” International Journal of Communication Systems, vol. 31, no. 2, p. e3445, 2018.  e3445 dac.3445.
  * [3] C.-M. Chao, C.-H. Jiang, and W.-C. Li, “Drp: An energy-efficient routing protocol for underwater sensor networks,” International Journal of Communication Systems, vol. 30, no. 15, p. e3303, 2017.  e3303 dac.3303.
  * [4] S. Rani, S. H. Ahmed, J. Malhotra, and R. Talwar, “Energy efficient chain based routing protocol for underwater wireless sensor networks,” Journal of Network and Computer Applications, vol. 92, pp. 42–50, 2017.  Underwater Acoustic Sensor Networks: Emerging trendsand current perspectives.
  * [5] G. Han, X. Long, C. Zhu, M. Guizani, and W. Zhang, “A high-availability data collection scheme based on multi-auvs for underwater sensor networks,” IEEE Transactions on Mobile Computing, vol. 19, no. 5, pp. 1010–1022, 2020.
  * [6] P. Jiang, Y. Xu, and F. Wu, “Node self-deployment algorithm based on an uneven cluster with radius adjusting for underwater sensor networks,” Sensors, vol. 16, no. 1, 2016.
  * [7] P. Xie, J.-H. Cui, and L. Lao, “Vbf: Vector-based forwarding protocol for underwater sensor networks,” in NETWORKING 2006. Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communications Systems (F. Boavida, T. Plagemann, B. Stiller, C. Westphal, and E. Monteiro, eds.), (Berlin, Heidelberg), pp. 1216–1221, Springer Berlin Heidelberg, 2006.
  * [8] H. Yan, Z. J. Shi, and J.-H. Cui, “Dbr: Depth-based routing for underwater sensor networks,” in NETWORKING 2008 Ad Hoc and Sensor Networks, Wireless Networks, Next Generation Internet (A. Das, H. K. Pung, F. B. S. Lee, and L. W. C. Wong, eds.), (Berlin, Heidelberg), pp. 72–86, Springer Berlin Heidelberg, 2008.
  * [9] Y. Wei and D.-S. Kim, “Reliable and energy-efficient routing protocol for underwater acoustic sensor networks,” in 2014 International Conference on Information and Communication Technology Convergence (ICTC), pp. 738–743, 2014.
  * [10] X. Zhuo, F. Qu, H. Yang, Y. Wei, Y. Wu, and J. Li, “Delay and queue aware adaptive scheduling- based mac protocol for underwater acoustic sensor networks,” IEEE Access, vol. 7, pp. 56263–56275, 2019.
  * [11] M. Molins and M. Stojanovic, “Slotted fama: a mac protocol for underwater acoustic networks,” in OCEANS 2006 - Asia Pacific, pp. 1–7, 2006.
  * [12] H. Cui, Y. Zhang, X. Liu, and D. Sun, “The simulation and emulation platforms of underwater acoustic sensor networks,” in 2016 IEEE/OES China Ocean Acoustics (COA), pp. 1–5, 2016.
  * [13] J.-H. Cui, J. Kong, M. Gerla, and S. Zhou, “The challenges of building mobile underwater wireless networks for aquatic applications,” IEEE Network, vol. 20, no. 3, pp. 12–18, 2006.
  * [14] Z. Wu and X. Li, “An improved underwater acoustic network localization algorithm,” China Communications, vol. 12, no. 3, pp. 77–83, 2015.
  * [15] P. Xie, Z. Zhou, Z. Peng, H. Yan, T. Hu, J.-H. Cui, Z. Shi, Y. Fei, and S. Zhou, “Aqua-sim: An ns-2 based simulator for underwater sensor networks,” in OCEANS 2009, pp. 1–7, 2009.
  * [16] T. Hu and Y. Fei, “Qelar: A q-learning-based energy-efficient and lifetime-aware routing protocol for underwater sensor networks,” in 2008 IEEE International Performance, Computing and Communications Conference, pp. 247–255, 2008.
  * [17] Y. Cui, Y. Xue, and K. Nahrstedt, “A utility-based distributed maximum lifetime routing algorithm for wireless networks,” IEEE Transactions on Vehicular Technology, vol. 55, no. 3, pp. 797–805, 2006.
  * [18] A. Sankar and Z. Liu, “Maximum lifetime routing in wireless ad-hoc networks,” in IEEE INFOCOM 2004, vol. 2, pp. 1089–1097 vol.2, 2004.
  * [19] V. Park and M. Corson, “A highly adaptive distributed routing algorithm for mobile wireless networks,” in Proceedings of INFOCOM ’97, vol. 3, pp. 1405–1413 vol.3, 1997.