Review on Localization Algorithms in Underwater Sensor Networks

Ritu Bhardwaj

and Ashwani Kush

DCSA, Kurukshetra University, Kurukshetra, India

IIHS, Kurukshetra University, Kurukshetra, India

Keywords: Localization, Sensor Nodes, Acoustic.

Abstract: This review paper provides a comprehensive examination of localization algorithms in underwater sensor

networks (UWSNs), addressing the unique challenges posed by the aquatic environment, such as severe

signal attenuation, multipath propagation, node mobility due to water currents, and significant energy con-

straints. It categorizes the localization algorithms into time-of-arrival (TOA), time-difference-of-arrival

(TDOA), received signal strength indicator (RSSI), angle-of-arrival (AOA), and hybrid and collaborative

approaches, highlighting their operational mechanisms, advantages, and limitations. Furthermore, the paper

discusses the current trends and future directions in UWSNs localization, including the integration of ma-

chine learning techniques and the potential for enhancing localization accuracy through the use of auxiliary

information like ocean current models. Through an analysis of existing literature and a discussion on the en-

vironmental challenges and technical limitations of underwater communication technologies, this paper

aims to provide insights into the advancements and remaining hurdles in the field of UWSN localization,

contributing to a deeper understanding of its critical role in enhancing the capabilities and applications of

underwater sensor networks.

INTRODUCTION

Underwater Sensor Networks (UWSNs) represent a

pivotal advancement in the domain of underwater

exploration and monitoring, facilitating a wide array

of applications from oceanographic data collection,

pollution monitoring, underwater pipeline monitor-

ing to surveillance and reconnaissance missions [1].

These networks comprise a multitude of sensor

nodes and vehicles deployed underwater, tasked

with collecting and transmitting data back to surface

stations or underwater bases. Unlike their terrestrial

counterparts, UWSNs operate in a uniquely chal-

lenging environment that significantly impacts

communication, localization, and network manage-

ment [2].

Localization, the process of determining the geo-

graphical positions of nodes within a network, is

crucial for the operational efficacy of UWSNs. It

underpins tasks such as data tagging with spatial

information, network routing, and the deployment

and retrieval of sensor nodes. However, the under-

water environment introduces a set of formidable

challenges not present in terrestrial settings, includ-

ing severe signal attenuation, multipath propagation

due to reflection from the surface and seabed, and

node mobility induced by water currents. These

factors necessitate specialized localization algo-

rithms tailored to the underwater environment.

The primary communication medium in UWSNs

is acoustic signaling, chosen over radio or optical

means due to its better propagation characteristics

underwater[19]. However, acoustic communication

is fraught with challenges, such as limited band-

width, high latency, and significant signal attenua-

tion with distance and due to

absorption by the water body, all of which compli-

cate the localization process[8]. Moreover, the speed

of sound in water varies with temperature, salinity,

and pressure, adding another layer of complexity to

accurate distance estimation based on signal propa-

gation time.

In addition to communication challenges, the un-

derwater environment itself poses significant hur-

dles. The variability in environmental conditions

affects sensor operations and acoustic signal propa-

gation, necessitating adaptive and robust localization

methods. Furthermore, the energy constraints of

underwater sensors, compounded by the difficulty of

battery replacement or recharging, demand highly

Bhardwaj, R. and kush, A.

Review on Localization Algorithm in Underwater Sensor Network.

DOI: 10.5220/0013343100004646

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Cognitive & Cloud Computing (IC3Com 2024), pages 257-265

ISBN: 978-989-758-739-9

257

energy-efficient localization algorithms to ensure the

longevity and sustainability of UWSN deploy-

ments[3].

Given these challenges, a variety of localization

algorithms have been proposed, each attempting to

address the intricacies of underwater environments.

These algorithms can be broadly classified into

range-based and range-free methods. Range-based

methods, which include Time of Arrival (ToA),

Time Difference of Arrival (TDoA), and Angle of

Arrival (AoA), rely on the measurement of distances

or angles between nodes to estimate positions. These

methods often require additional hardware and can

be affected by the aforementioned issues of acoustic

communication . On the other hand, range-free

methods, which infer location based on network

connectivity and proximity, offer a less hardware-

intensive solution but typically with lower accuracy .

Emerging trends in UWSN localization focus on

overcoming the limitations of existing algorithms

through the integration of machine learning tech-

niques, the use of auxiliary information such as

ocean current models, and the development of hy-

brid methods combining the strengths of range-

based and range-free approaches Moreover, the po-

tential for integrating UWSNs with other types of

networks, such as terrestrial and satellite networks,

presents opportunities for creating a more intercon-

nected and comprehensive monitoring and data col-

lection system.

This review paper aims to provide a thorough ex-

amination of the state-of-the-art in localization algo-

rithms for underwater sensor networks. By navi-

gating through the complexities of underwater

communication, addressing the environmental chal-

lenges, and exploring innovative solutions, this pa-

per seeks to offer insights into the advancements and

remaining hurdles in the field of UWSN localiza-

tion. Through a comprehensive analysis of existing

literature and current research trends, this review

will contribute to a deeper understanding of the crit-

ical role of localization in enhancing the capabilities

and applications of underwater sensor networks..

CLASSIFICATION OF

LOCALIZATION

ALGORITHMS

Localization algorithms in underwater sensor net-

works (UWSNs) can be classified based on their

methodologies, which encompass a variety of tech-

niques for determining the sensor nodes locations in

the underwater environment. Depending on the

method for estimating position, localization strate-

gies belong to the category of range-based or range-

free schemes. The method of range-based localiza-

tion’s position estimate consists of two steps. First,

measure the angle or separation between the nodes

in the range phase of localization. The sensor node’s

approximate location is ascertained using the meas-

ured values during the localization phase, which

follows the range phase. Methods listed below is

used to measure range .These algorithms leverage

different principles and measurements such as time-

of-arrival TOA, TDOA, AOA, received signal

strength indicator (RSSI) to infer the sensor nodes’

spatial coordinates . This section presents an over-

view of each category and a classification of locali-

zation algorithms in UWSNs.

2.1 Time-of-Arrival Based Algorithms

TOA algorithms measure the time it takes for acous-

tic signals to travel between the sensor nodes to

ascertain their distances. By employing synchro-

nized clocks and pre cise timing measurements,

these algorithms determine the time-of-arrival of

signals at different nodes and use this information to

calculate the distances. Sequential methods iterative-

ly refine the position estimate based on sequential

distance measuments.MLE approaches estimate

node positions by maximizing the likelihood func-

tion based on observed TOA measurements and

assumed statistical models[25].

2.2 Time-Difference-of-Arrival Based

Algorithms

TDOA based algorithms estimate the differences in

arrival times of signals between

pairs of sensor nodes. By comparing the time differ-

ences at multiple reference nodes, these algorithms

infer the difference in distances between nodes and

triangulate the target node’s location. Common

TDOA based algorithms include two-way ranging

(TWR), multilateration, and non-linear least squares

(NLLS). TWR involves bidirectional communica-

tion between nodes in order to gauge the round-trip

time of acoustic signals. Multilateration methods

determine the location of the target node by utilizing

TDOA data from several reference nodes. NLLS

methods iteratively refine position estimates by min-

imizing the difference between observed and pre-

dicted TDOA values[25] .

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258

2.3 Received Signal Strength Indicator

Based Algorithms

RSSI-based algorithms use the received signal inten-

sity of acoustic broadcasts to determine the distances

between sensor nodes. By correlating signal strength

measurements with distance attenuation models,

these algorithms infer the distances between nodes.

Examples of RSSI based algorithms include trilater-

tion, fingerprinting, and machine learning ap-

proaches. Based on established reference node loca-

tions and distance estimates from RSSI data, trilater-

ation determines node positions. Fingerprinting

methods create a database of signal strength patterns

at known locations and match observed RSSI meas-

urements to determine node positions. Machine

learning algorithms utilize RSSI data to train models

for predicting node positions based on observed

signal characteristics [25].

2.4 Angle-of-Arrival Based Algorithms

AOA based algorithms estimate the angles at which

acoustic signals arrive at sensor nodes relative to

known reference directions. By measuring the arri-

val angles from multiple reference nodes, these algo-

rithms triangulate where the target node is located.

AOA based algorithms include techniques such as

Acoustic Doppler Shift (ADS) and Angle-of-Arrival

estimation. ADS methods exploit the Doppler effect

induced by the motion of the target node to estimate

arrival angles. Using array processing algorithms,

AOA estimation methods determine the angles of

arrival using spatial beamforming and signal pro-

cessing [25].

2.5 Hybrid and Collaborative

Localization Approaches

Hybrid and collaborative localization approaches

combine multiple localization techniques to improve

accuracy, robustness, and scalability. These ap-

proaches leverage complementary strengths of dif-

ferent techniques for localization to get around the

restrictions of individual techniques. Examples of

hybrid and collaborative localization approaches

include sensor fusion, cooperative localization,

adaptive algorithms. Sensor fusion integrates meas-

urements from multiple sources, like TOA, TDOA,

RSSI, and AOA, to increase the precision of locali-

zation. Cooperative localization schemes involve

collaboration between sensing nodes to exchange

data and enhance localization performance. Adap-

tive algorithms dynamically select and combine

localization methods based on environmental condi-

tions, network dynamics, and application require-

ments [27][16].

CHALLENGES IN

UNDERWATER

LOCALIZATION

Underwater localization faces a unique set of diffi-

culties that are mostly caused by the characteristics

of the aquatic environment and the technical limita-

tions of underwater communication technologies.

Underwater localization faces a unique set of diffi-

culties that are largely caused by the characteristics

of the aquatic environment and the technical limita-

tions of underwater communication technologies.

Underwater communication systems have several

difficulties, including restricted bandwidth, in-

creased energy consumption, longer propagation

delay times, End-End Delays (E-ED), three-

dimensional topology, media access control, routing,

resource optimization, and power limitations[26].

Since There exist certain differences between sensor

environments on land and underwater, we have opt-

ed for acoustic waves over radio signals:

Implementation: Although sensor networks on land

are widely dispersed, those underwater are thought

to be deployed less frequently because of the related

costs and difficulties.

Strength: Because acoustic underwater communica-

tions operate over longer distances and require more

sophisticated signal processing at the receivers to

account for channel imperfections, compared to

radio communications on land, they use more ener-

gy.

Recollection: While the storage capacity of terres-

trial sensor nodes is quite restricted, under-water

sensors may require the ability to perform some data

caching due to the possibility of intermittent under-

water channel connectivity.

Correlation in Space: Although there is often corre-

lation between the results from terrestrial sensors,

underwater networks are less likely to experience

this because of the greater distances between the

sensors [2].

Review on Localization Algorithm in Underwater Sensor Network

259

Table 1: Comparison of Electromagnetic(EM), Acoustic, and Optical Waves in Underwater Environment.

Properties EM Acoustic Waves Optical Waves

Frequency

Bandwidth

Effective range

Nominal speed(m/s)

Antenna size

kHz

1 m

1,500

0.1 m

MHz

10 m

33,333,333

0.5

–10

10-15 MHz

10-100 m

33,333,333

0.1 m

Acoustic communication, the predominant method

for underwater sensors due to its better propagation

characteristics in water compared to electromagnetic

and optical signals1, introduces significant challeng-

es including signal attenuation, multi-path propaga-

tion, and delay variances. Signal attenuation occurs

as the acoustic signal loses its energy over distance

and due to absorption by the water, which limits the

range and reliability of communication. Multi-path

propagation, where the ocean’s surface and bottom

reflect signals, leads to multiple replicas of the sig-

nal that will arrive at the receiver at different times,

causing signal distortion and making it difficult to

accurately determine the TOA and, consequently,

the distance between nodes. Furthermore, the pro-

cess of localization is made more difficult by the

water currents that cause the mobility of sensor

nodes, which constantly change the topology of the

sensor network. If not sufficiently considered,this

mobility can result in large localization mistakes,

necessitating techniques that can adjust to changes in

the node positions over time. These difficulties are

made worse by the energy limitations of underwater

sensors. Given the difficulty and expense of replac-

ing or recharging batteries in underwater environ-

ments, energy efficiency becomes a critical consid-

eration in the design of localization algorithms, ne-

cessitating methods that minimize communication

and computational overheads. The harsh underwater

environment itself—characterized by varying tem-

perature, pressure, and salinity—can affect sensor

operation and signal propagation. These surrounding

conditions may change the speed of sound through

water, influencing how accurate distance readings

are based on acoustic signal propagation time. Inno-

vative strategies are needed to address these issues

to localization that can cope with the dynamic and

harsh conditions of underwater environments, in-

cluding robust signal processing techniques, adap-

tive algorithms capable of responding to changes in

network topology and environmental conditions, and

energy-efficient designs that extend the operational

lifespan of the sensor nodes[28].

LITERATURE SURVEY ON

LOCALIZATION ALGORITHM

OF UWSNS

This section provides a brief explanation of the rele-

vant survey on localization algorithms, which offers

a wide range of strategies to address the difficulties

of localization in dynamic and heterogeneous envi-

ronments. These strategies take into account varia-

bles like mobility, time synchronization, localization

accuracy, and the particulars of the underwater me-

dium.

4.1 Localization Based on Mobility

Constraints Beacon

[11] The two-dimensional localization method,

MCB-2D, and the three-dimensional localization

algorithm, MCB-3D, are two of the node location

algorithms that the author suggested depending on

mobile restricted beacons. The technique is not re-

quired to know the beacon node’s precise location;

instead, through the geometric relationship between

the anchor’s position and the moving radius of the

beacon node, the unknown node can be located.

According to the experiments result , the method

improves localization accuracy and decreases the

rate of network node placement errors.

4.2 Mobility Prediction and Particle

Swarm Optimization Algorithms

To solve the problems of longer localization times,

higher energy consumption, and lower beacon node

distribution density in UWSNs, the author [32] pro-

posed a PSO method based on range to find beacon

nodes and predict unknown node locations depends

on the mobility of an underwater object’s spatial

correlation. The algorithm takes node mobility pat-

terns into account. Through simulation findings, it

was able to achieve better coverage rate and higher

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260

localization accuracy in comparison to additional

widely used localization methods applied in practice.

Since the mobility prediction-based approach, large

localization errors could result from these predic-

tions being inaccurate. Predicting underwater sensor

node movement accurately can be difficult because

of a number of variables, including turbulence, cur-

rents, and the erratic behavior of marine life.

4.3 A Predictive Localization

Algorithm Based on Improved

Backtracking Search Optimization

and GWO

[29] Predictive localization algorithm (MGP), based

on enhanced backtracking search optimization and

gray wolf optimizer, is made available for UWSNs

by the author. When compared to other existing

algorithms, MGP produces better localization re-

sults. It suggested the usage of the predictive locali-

zation algorithm (MGP) to improve time of node

convergence and position accuracy for enhanced

node positioning and UWSNs by introducing a two-

part localization procedure using the gray wolf op-

timizer (GWO) and modified backtracking search

optimization (MBSA). Simulations result demon-

strates that the MGP technique performs better rela-

tive to other algorithms in terms of localization out-

comes such as SLMP, MCL-MP, and MP-PSO. But

the study doesn’t go into great detail about the pos-

sible difficulties or disadvantages of putting the

suggested predictive localization method into prac-

tice.

4.4 Static Localization Using Nelder–

Mead Algorithm for Smart Cities

In this paper the author[14] employs virtual nodes

and the Nelder-Mead algorithm for static underwater

sensor network localization in order to overcome

obstacles such as communication constraints and

water conditions, improving coverage without syn-

chronization overhead. One shortcoming of this

work is that it skips over how the properties of the

underwater acoustic channel impact the performance

of the localization techniques

4.5 A Computationally Efficient Target

Localization Algorithm

Block principal pivoting-based localization (BPPL),

a computationally effective technique that demon-

strated competitive accurate location and computa-

tional complexity under a variety of conditions, was

proposed by the author[20].The author’s computa-

tionally efficient approach, BPPL, studied the locali-

zation problem in the ANCLS framework after this

algorithm’s linearization procedure and outper-

formed state-of-the-art techniques in numerous sce-

narios both with regard to competitive computation-

al complexity and localization accuracy. After divid-

ing potential solutions into two groups, it employed

variable exchange to select the optimal choice given

the constraints. It provided the target location meth-

od known as block principal pivoting-based localiza-

tion (BPPL). Simulations show that BPPL provides

competitive computing complexity and localization

accuracy in comparison to cutting-edge techniques

in a range of scenarios.

4.6 Silent Positioning in Underwater

Acoustic Sensor Networks (UASNs)

The time difference between arrivals measured lo-

cally at the sensor and the four anchor nodes the

author suggested as part of the UPS (Underwater

Positioning System). [4] . The purpose of trilatera-

tion is to infer the 3-D sensor position by summing

these range discrepancies over several beacon inter-

vals. UPS offers location privacy for sensors and

underwater vehicles whose whereabouts must be

ascertained, and it doesn’t require time synchroniza-

tion. It use a modified ultrawide and Saleh-

Valenzuela model to simulate the underwater acous-

tic channels in order to examine the UPS’s perfor-

mance. Each path cluster’s arrival, as well as the

paths inside it, have double Poisson distributions, the

multipath channel gain, on the other hand, has a

Rician distribution. The outcomes show that UPS is

a successful underwater vehicle/sensor self-

positioning technique.

Review on Localization Algorithm in Underwater Sensor Network

261

Table 2: Localization Algorithms for UWSNs.

Algorithms

Method

(Range based

or Range free)

Technique Time Sync Mobility Inferences

Asynchronous localiza-

tion with Mobile Pre-

diction [36]

Energy Harvesting Hy-

brid Acoustic-Optical

Underwater Wireless

Sensor Networks Locali-

zation [23]

Both

Hybrid

Yes

Enhanced Localization Accura-

cy, Reduction in Localization

error, Time Compensation for

Node Mobility

Estimate the final sensor node

locations accurately Energy

harvesting capability within the

nodes of sensors assist in solv-

ing the energy consumption

issue.

Deep Sea TDOA Lo-

calization Method

Based on Improved

OMP Algorithm[13]

Two-Phase Time Syn-

chronization Free Lo-

calization [18]

Received signal

strength based

localization in

inhomogeneous

underwater

medium[22]

Range Based

Both

Range Based

TDOA

RSSI

Yes

localization effectiveness in

comparison to alternative tech-

niques in a multipath interfer-

ence environment and have

higher accuracy and stability in

time delay estimation

Provide Localization Accuracy

Robust under various underwa-

ter conditions Challenges in this

algorithm are acoustic interfer-

ence and signal attenuation

RSS can be influenced by

propagation effects like absorp-

tion, scattering, refraction can

lead to fluctuations in RSS

measurements but improved

Navigation Accuracy and RSS-

based localization is cost-

effective than GPS

Doppler shift

and modified

genetic algorithm [6]

MANCl:A Multi

Anchor Nodes

collabarative

Localization

Algortihm [34]

AdaDelta Gradient

Descent Algorithm[35]

Both

Range Based

TOA ,TDOA

Hybrid

TDOA

Yes

May or

May not

Yes

Provide accurate node

localization ,Limited Range,

Handle large-scale USWNs,

Require precise the sensor

node calibration and acoustic

equipment

Better localization covergae

Better Energy Cosumption

Reduce localization Error

Reduce Ranging Interference

faster convergence and better

optimization performance. Its

adaptive learning rate mecha-

nism helps in efficient conver-

gence during optimization and

it rely on initial parameter

values for conver

ence

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262

SEAL: Self Adaptive

AUV based

Localization [21]

Joint localization

and tracking

for autonomous

underwater

vehicle[30]

Virtual Node Assisted

Localization

Algorith

[17]

Both

Range Based

RSSI

RSSI and

TOA

Hybrid

Yes (When

TOA

used)

Yes

Accuracy of localization is

found using AUV and Sensor

nodes are dispersed

throughout a wide area with

significant gaps

Use of AUV leads to safer

operations, reduced risk of

collisions, and enhanced

success rates of sensor de-

ployments

Efficient UWSNs localization

Reduce Communication Error

High localization coverage error

4.7 Cluster Based Localization Scheme

with Backup Node

[24] In order to increase energy on initial parameter

values for convergence efficiency and extend net-

work lifetime in an underwater environment, the

author suggests a cluster-based localization tech-

nique. In addition, a clustering protocol comprising

cluster heads, anchor nodes, and the purpose of add-

ing backup nodes is to optimize energy consumption

and promote information transfer. The objective of

this plan is to increase network longevity in sub-

merged situations and enhance energy efficiency.

Over time, it might have difficulties preserving the

dynamic character of cluster heads and related

nodes, which could have an effect about the net-

work’s stability table.

4.8 Iterative Localization Technique

for UWSNs

In order for Geo-routing to be executed successfully

and produce meaningful location-aware data, medi-

um access and routing protocols must be optimized.

Localization plays a critical part in this process. This

study [31] examines comparison between localiza-

tion with and without reference nodes in UWSNs via

the author’s investigation. In order for Geo-routing

to be executed successfully and produce meaningful

location-aware data, medium access and routing

protocols must be optimized.

Localization plays a critical part in this process.

While multipath interference and water currents

pose obstacles, the localization of non localized

sensor nodes is essential for many underwater com-

munication applications.

4.9 An Intelligent Agent-Based

Localization System in UWSNs

The author [5] proposed an intelligent scan model

technique for the localization system under UWSN.

The information used in this procedure comes from

unmanned floating vehicles (MULEs) that have

enough storage capacity and rechargeable energy to

determine the precise position of the sensor nodes

for the purpose of properly identifying sense data

through Data MULE (mobile ubiquitous LAN ex-

tensions).The suggested approach makes use of un-

manned floating vehicles that are outfitted with en-

ergy, storage, and data transmission capabilities

Information is gathered via underwater sensor nodes

and locate them using sophisticated localization

algorithms. This method increases the efficiency and

accuracy of localization of UWSNs, allowing for

more accurate sensed data identification and applica-

tion.

The severe channel characteristics of UWSNs

make precise localization difficult to achieve. Even

though sound waves can be used for long-distance

communication, accurate position estimations need

the inclusion of systems that can detect changes in

direction and velocity. Furthermore, large data rates

across shorter distances are possible using optical

waves. By lowering contention and increasing data

rates, a hybrid approach using, for example, optical

waves for intra-cluster communication and acoustic

waves for inter-cluster communication can improve

performance. Time synchronization is still difficult

to achieve, though, as current methods need constant

communication between nodes and may use a lot of

energy. To create effective and energy-saving syn-

chronization techniques for UWSNs, more study is

required.

Review on Localization Algorithm in Underwater Sensor Network

263

CONCLUSION AND FUTURE

WORK

This review highlights the critical importance of

localization algorithms in optimizing the functionali-

ty of UWSNs across diverse applications, from oce-

anic research to environmental monitoring. It out-

lines the numerous challenges inherent in UWSN

localization, stemming from underwater environ-

mental factors and technical constraints of acoustic

communication. Despite these hurdles, advance-

ments in localization techniques, including range

based and innovative range-free methods, signify

significant progress in addressing these obstacles.

The integration of machine learning and environ-

mental data promises enhanced precision, robust-

ness, and energy efficiency in localization solutions.

The review not only underscores current challenges

but also paves the way for future research directions,

advocating for adaptive, scalable, and sustainable

solutions tailored to the complexities of underwater

environments. It calls for extensive real-world ex-

perimental validations and advocates for the explo-

ration of hybrid approaches to further enhance accu-

racy, robustness, and scalability. Ultimately, inter-

disciplinary collaboration and technological ad-

vancements are driving forward the possibilities in

underwater sensing, facilitating exploration, moni-

toring, and sustainable management of aquatic eco-

systems. Localization in UWSNs faces challenges

due to harsh channel conditions we can use hybrid

approach combining acoustic and optical waves can

enhance performance. However, achieving efficient

time synchronization remains a challenge it is neces-

sitating to further research for energy-conserving

methods.

REFERENCES

1. Felemban E, Shaikh FK, Qureshi UM, Sheikh AA,

Qaisar SB. Underwater Sensor Net work Applica-

tions: A Comprehensive Survey. International Journal

of Distributed Sensor Networks. 2015;11(11).

2. Akyildiz, I.F., Pompili, D., Melodia, T.: Underwater

acoustic sensor networks: research challenges. Ad Hoc

Networks 3(3), 257–279(2005). https://doi.org/

10.1016/j.adhoc.2005.01.004, https://www.science di-

rect.com/science/article/pii/S1570870505000168

3. Awan, K.M., Shah, P.A., Iqbal, K., Gillani, S., Ahmad,

W., Nam, Y., et al.: Underwater wireless sensor net-

works: A review of recent issues and challenges.

Wireless Communications and Mobile Computing

2019 (2019)

4. Cheng, X., Shu, H., Liang, Q., Du, D.H.C.: Silent posi-

tioning in underwater acoustic sensor networks. IEEE

Transactions on vehicular technology 57(3), 1756–

1766 (2008)

5. Dass, A.K., Das, S., Pattanaik, S.R.: An intelligent

agent-based localization syste in underwater wireless

sensor networks. In: Constraint Decision-Making Sys-

tems in Engineering, pp. 135–155. IGI Global (2023)

6. Datta, A., Dasgupta, M.: On accurate localization of

sensor nodes in underwater sensor networks: A dop-

pler shift and modified genetic algorithm based locali-

zation technique. Evolutionary Intelligence 14(1),

119–131 (2021)

7. Erol-Kantarci, M., Mouftah, H.T., Oktug, S.: A survey

of architectures and localization techniques for under-

water acoustic sensor networks. IEEE communications

surveys & tutorials 13(3), 487–502 (2011)

8. Freitag, L., Grund, M., Singh, S., Partan, J., Koski, P.,

Ball, K.: The whoi micromodem: An acoustic com-

munications and navigation system for multiple plat-

forms.pp.1086–1092Vol.2(10 2005). https://doi.org/

10. 1109/OCEANS.2005.1639901

9. Gang, Q., Muhammad, A., Khan, Z.U., Khan, M.S.,

Ahmed, F., Ahmad, J.: Machine learning-based pre-

diction of node localization accuracy in iiot-based mi-

UWSN and design of a td coil for omnidirectional

communication. Sustainability 14(15),9683 (2022)

10. Grbčić, L., Lučin, I., Kranjčević, L., Družeta, S.: A

machine learning-based algorithm for water network

contamination source localization. Sensors 20(9),

2613(2020)

11. Guo, Y., Han, Q., Kang, X.: Underwater sensor net-

works localization based on mobility-constrained bea-

con. Wireless Networks 26, 2585–2594 (2020)

12. Islam, T., Lee, Y.K.: A cluster based localization

scheme with partition handling for mobile underwater

acoustic sensor networks. Sensors19(5)(2019).

https://doi.org/10.3390/s19051039,https://www.mdpi.c

om/1424-8220/19/5/1039

13. Jiang, F., Zhang, Z., Esmaeili Najafabadi, H.: Deep sea

tdoa localization method based on improved omp al-

gorithm. IEEE Access 7, 168151–168161 (2019).

https://doi.org/10.1109/ACCESS.2019.2954330

14. Kulandaivel, M., Natarajan, A., Chandrasekaran, B.P.,

Selvaraj, A.: Static localization for underwater acous-

tics sensor networks using nelder–mead algorithm for

smart cities. Computational Intelligence 37(4), 1691–

1705 (2021)

15. Lanbo, L., Shengli, Z., Jun-Hong, C.: Prospects and

problems of wireless communication for underwater

sensor networks. Wireless Communications and Mo-

bile Computing 8(8), 977–994 (2008)

16. Lin, Y., Tao, H., Tu, Y., Liu, T.: A node self-

localization algorithm with a mobile anchor node in

underwater acoustic sensor networks. Ieee Access 7,

43773–43780 (2019)

17. Liu, C., Wang, X., Luo, H., Liu, Y., Guo, Z.: Va:

Virtual node assisted localization algorithm for un-

derwater acoustic sensor networks. IEEE Access 7,

IC3Com 2024 - International Conference on Cognitive & Cloud Computing

264

86717–86729(2019). https://doi.org/10.1109/

ACCESS. 2019.2925938

18. Luo, J., Fan, L.: A two-phase time synchronization-

free localization algorithm for underwater sensor net-

works. Sensors 17(4) (2017). https://doi.org/

10.3390/s17040726, https://www.mdpi.com/1424-

8220/ 17/4/726

19. Ma, L., Qiao, G., Yang, J.: Underwater Acoustic

Communication, pp. 1–8. Springer Singapore, Singa-

pore (2020). https://doi.org/10.1007/978-981-10-6963-

5_288-1,

20. Mei, X., Wu, H., Han, D., Chen, X., Xian, J., Han, B.:

A computationally efficient target localization algo-

rithm in underwater wireless sensor networks. In:

2023 8th International Conf. on Computer and Com-

munication Systems (ICCCS). pp. 181–186.IEEE

(2023)

21. Ojha, T., Misra, S., Obaidat, M.S.: Seal: Self-adaptive

auv-based localization for sparsely deployed underwa-

ter sensor networks. Computer Communications 154,

204–215 (2020)

22. Poursheikhali, S., Zamiri-Jafarian, H.: Received signal

strength based localization inhomogeneous underwater

medium. Signal Processing 154, 45–56 (2019)

23. Saeed, N., Celik, A., Al-Naffouri, T.Y., Alouini, M.S.:

Energy harvesting hybridacoustic-optical underwater

wireless sensor networks localization. Sensors 18(1),

51(2017)

24. Sahana, S., Singh, K.: Cluster based localization

scheme with backup node in underwater wireless

sensor network. Wireless Personal Communications

110(4), 1693–1706(2020)

25. Su, X., Ullah, I., Liu, X., Choi, D., et al.: A review of

underwater localization techniques,algorithms, and

challenges. Journal of Sensors 2020 (2020)

26. Teekaraman, Y., Sthapit, P., Choe, M., Kim, K.: Ener-

gy analysis on localiz tion free routing protocols in

uwsns. International Journal of Computational Intelli-

gence Systems 12(2), 1526–1536 (2019)

27. Tsai, P.H., Tsai, R.G., Wang, S.S., et al.: Hybrid local-

ization approach for underwater sensor networks.

Journal of Sensors 2017 (2017)

28. Tuna, G., Gungor, V.C.: A survey on deployment

techniques, localization algorithms, and research chal-

lenges for underwater acousticsensor networks. Inter-

national Journal of Communication Systems 30(17),

e3350 (2017). https://doi.org/10.1002/dac.3350,

https://onlinelibrary.wiley.com/doi/abs/10.1002/dac.33

50, e3350 IJCS-16-0556.R1

29. Xu, B., Liu, H., Liu, B.: A predictive localization

algorithm for underwater wireless sensor networks

based on improved backtracking search optimization

and gray wolf optimizer. In: 2022 41st Chinese Con-

trol Conference (CCC). pp. 5116–5121. IEEE(2022)

30. Yan, J., Li, X., Luo, X., Gong, Y., Guan, X.: Joint

localisation and tracking for autonomous underwater

vehicle: a reinforcement learningbased approach. IET

Control Theory & Applications 13(17), 2856–2865

(2019). https://doi.org/https://doi.org/10.1049/iet-

cta.2018.6122, https://ietresearch.onlinelibrary.

wiley.com/doi/abs/10.1049/iet-cta.2018.6122

31. Yogeshwary, B., et al.: Iterative localization technique

for underwater wireless sensor networks. In: 2022

IEEE North Karnataka Subsection Flagship Interna-

tional Conference (NKCon). pp. 1–8. IEEE (2022)

32. Zhang, Y., Liang, J., Jiang, S., Chen, W.: A localiza-

tion method for underwater wireless sensor networks

based on mobility prediction and particle swarm opti-

mization algorithms. Sensors 16(2), 212 (2016)

33. Zhou, Z., Peng, Z., Cui, J.H., Shi, Z., Bagtzoglou, A.:

Scalable localization with mobility prediction for un-

derwater sensor networks. IEEE Transactions on Mo-

bile Computing 10(3), 335–348 (2010)

34. Guangjie Han, Chenyu Zhang, Tongqing Liu, and Lei

Shu. Mancl: a multi-anchor nodes collaborative locali-

zation algorithm for underwater acoustic sensor net-

works. Wireless Communications and Mobile Compu-

ting,16(6):682–702,2016.18

35. Ziyu Zhou, Xingyu Zhou, Guozhen Xing, Zhigang Jin,

Ye Chen, and Qiul-ing Yang. Localization of under-

water sensor networks for ranging interference based

on the adadelta gradient descent algorithm. 2023.18

36. Jing Yan, Xiaoning Zhang, Xiaoyuan Luo, Yiyin

Wang, Cailian Chen, and Xinping Guan. Asynchro-

nous localization with mobility prediction for un der-

water acoustic sensor networks. IEEE Transactions on

Vehicular Technology, 67(3):2543–2556, 2017.18

Review on Localization Algorithm in Underwater Sensor Network

265