UNDERWATER OBJECT DETECTION AND TRACKING

USING ELECTROMAGNETIC WAVES

PRESENTED BY

MUSBIHA BINTE WALI

STUDENT ID: 0906022

THESIS SUPERVISOR

DR MD. FARHAD HOSSAIN

DEPARTMENT OF ELECTRICAL AND ELECTRONIC ENGINEERING

BANGLADESH UNIVERSITY OF ENGINEERING AND TECHNOLOGY

DHAKA – 1000

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Outline •  Overview •  Motivation •  Background •  Related Works •  My Work •  The Proposition •  Overall Localization Scheme •  The Proposed Architectures •  Performance Metric •  Simulations, Results and Discussions •  Conclusions •  Summary of Contributions •  Future Research •  References

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Overview •  70% of the Earth’s surface covered by water, a resourceful domain.

•  Active research for over a decade to explore it. •  Sensor technologies’ acceptance: Underwater wireless sensor networks (UWSNs) being considered a tangible, low-cost solution. •  Purposes served by the UWSNs: environmental monitoring, tactical surveillance, search and rescue missions, gathering of oceanographic data, marine archaeology, mine reconnaissance, disaster prevention, oceanography and many other aquatic applications. •  Available transmission media for underwater environment: electromagnetic (EM), acoustics, optical. The main shortcoming of EM: the high absorption of in water, especially in seawater. Limited bandwidth (0 b/s to 20 kb/s) and impact on marine life are the problems of acoustics. Optical wave’s requirement of line-of-sight (LOS) limits its use in underwater environment. •  UWSNs have, till now, almost exclusively been implemented using acoustic systems. Acoustics is proven technology to be the best engineering solution. •  Re-evaluation of EM based communication underwater.

4

Motivation •  Slow transmission by acoustics,

increasing number of fast moving underwater vehicles and autonomous weapons used worldwide,

urgency for an alternative faster transmission media like optical or electromagnetic (EM) wave for detection and tracking purposes.

•  Optical waves are impractical for major underwater applications .

•  How to exploit the EM wave’s fast transmission capability in underwater to make real-time high-throughput applications feasible.

•  Electromagnetic (EM) wave based underwater networks have great potential for supporting high-speed data rates in such scenarios because fast detection and transmission; immunity to acoustic noise, turbidity and pressure gradients; reduced impact on marine life and capability of non-LOS (NLOS) communications, which acoustic networks fail to afford, can be materialized by using EM based UWSNs.

•  There needs more attention on underwater intruder localization networks based on a faster media other than acoustics.

•  Therefore, this thesis proposes and investigates three dimensional (3D) cluster-based UWSN architectures for localizing underwater intruders.

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Background •  Wireless Communications

•  Underwater Wireless Communications and Networks

•  UWSNs

•  EM Wave Underwater Propagation Models

•  Localization Methods

•  Underwater Localization

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Wireless communications : • The fastest growing segment of the communications industry. • Wireless Network Challenges: Path Loss, Shadowing and Multipath fading,

Doppler Power Spectrum and Inter Symbol Interferences (ISI)

Mathematical Models of Wireless Channel: • Additive noise channel • Linear filter channel • Linear time-variant (LTV) filter channel

Figure: Additive noise channel.

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Underwater Wireless Communications and Networks: Underwater wave propagation, the challenges are much more acute than that on terrestrial. Comparison of Acoustic, EM And Optical Waves in Seawater Environments

Method Benefits Limitations

EM • short-range wireless communication using EM waves in

seawater has seen certain breakthroughs

• Crosses air/water/seabed boundaries easily

• Prefers shallow water

• Unaffected by turbidity, salinity, and pressure gradients

• Works in non-line-of-sight (NLOS); unaffected by

sediments and aeration

• Immune to acoustic noise

• High bandwidths (up to 100 Mb/s) at very close range

• Susceptible to EMI

• The main shortcoming stays with the high

absorption of EM waves in water, especially in

seawater.

Acoustics • Proven technology to be the best engineering solution in

most long-distance applications.

• Long Range (up to 20 km)

• Strong reflections and attenuation when

transmitting through water/air boundary

• Poor performance in shallow water

• Adversely affected by turbidity, ambient noise,

salinity, and pressure gradients

• Limited bandwidth (0 b/s to 20 kb/s)

• Impact on marine life

Optical • Ultra-high bandwidth (gigabits per second)

• Low cost

• Does not cross water/air boundary easily

• Susceptible to turbidity, particles, and marine

fouling

• Needs line-of-sight

• Requires tight alignment of nodes

• Very short range under water

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Underwater Wireless Communications and Networks: Underwater wave propagation, the challenges are much more acute than that on terrestrial. Comparison of Acoustic, EM And Optical Waves in Seawater Environments

Acoustic EM Optical

Nominal speed(m/s) ∼ 1,500 ∼ 33,333,333 ∼ 33,333,333

Power Loss > 0.1 dB/m/Hz ∼ 28 dB/1km/100MHz ∝ turbidity

Bandwidth ∼ kHz ∼ MHz ∼ 10-150 MHz

Frequency band ∼ kHz ∼ MHz ∼ 1014–1015 Hz

Antenna size ∼ 0.1 m ∼ 0.5 m ∼ 0.1 m

Effective range ∼ km ∼ 10 m ∼ 10-100 m

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Underwater Wireless Sensor Networks (UWSNs)

Typical Sensor Node. Sensor Node for

Underwater Environment.

Sensor Node Receiver Sensitivity

Receiver’s minimum operational sensitivity, Smin = (S/N) min kTo B(NF) Typical values for Smin of some receivers: RWR (Radar Warning Receiver) : -65 dBm Pulse Radar: -94 dBm CW Missile Seeker: -138 dBm

(S/N)min = Minimum SNR needed to process(vice just detect) a signal NF = Noise figure/factor k = Boltzmann's Constant = 1.38 x 10-23 Joule/K To = Absolute temperature of the receiver input (Kelvin) = 290K B = Receiver Bandwidth (Hz)

Clustered Sensor Network.

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EM Wave Underwater Propagation Model

EM waves propagate slower in water than in the air. Because water contains dissolved salts and other matter, it becomes a partial conductor. The higher water’s conductivity, the greater the attenuation of radio signals.

Prec(dBm) = Pt(dBm) + Gt(dB) + Gr(dB) − Lpathloss(dB) For underwter environment,

pathloss, PL = 20×(log10e)×2π× (σ × f × 10−7) dB/m , where where f = transmission frequency (Hz) and σ = conductivity of water (S/m). Sea water has a high salt content, with an average conductivity of 4 Siemens/meter (S/m), fresh water conductivity is typically about 0.01 S/m.

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Different Methods for Target Localization

Localization

Technique

Summary and

Characteristics

Strength and Weakness Usage and Applicability

TOA

TOA Uses distance

information between

FT and MT.

One-way ranging requires perfect

synchronization, while two-way

ranging does not.

More common in cellular

networks.

TDOA

Difference between

TOAs in several FTs are

utilized.

Needs highly precise synchronization

between MTs, while not precise

synchronization between FTs and MTs.

More common in

wireless sensor networks

AOA

Uses the angle

information to

construct the

lines between MT and

FTs and use their

intersection to find MT

location

Requires new hardware (antenna

arrays).

This means additional costs and larger

node sizes.

More appropriate for FTs

rather than MTs due to

large size. Otherwise MT

size has to be able to

accommodate an

antenna array. More

accuracy than RSS

RSS Distance is estimated

based on the

attenuation introduced

by propagation of the

signal

from FT to MT.

RSS Distance is estimated based on the

attenuation introduced by propagation

of the signal from FT to MT. An

accurate propagation model is needed

for reliable distance estimation. It is

low cost due to most RX being able to

estimate RSS. MT mobility and channel

variation may yield large errors.

Since it has low-precision

characteristic, typically

used in applications

which require coarse

estimate

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Underwater Localization

The detection or localization of underwater objects from presents a difficult problem due to various factors such as variations in the operating and environmental conditions, presence of spatially varying clutter, and variations in target shapes, compositions, and orientation.

Submerged Target Localization: Popular Methods

• Cluster based UWSNs using sonar data.

• Underwater Robots: rapidly identify and communicate potential threats.

• Magnetic anomaly detection (MAD) systems: MAD goals include - Underwater Target localization, characterization and Target tracking: Submarine detection, Ships’ wreck detection, Mine detection, UXO detection, Buried drums detection.

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Related Works •  Two dimensional (2D) submarines localization techniques using a distributed sensor network of binary-detection capability.

• 2D two distributed particle filter based object localization and tracking schemes using cluster based UWSNs.

• Using cluster-based UWSNs contributes to robust topology and their aptness in effective energy saving strategies is evident through the works on increasing networklife of UWSNs.

•  More than one works on three-dimensional (3D) underwater target localization and tracking schemes using UWSNs. The ToA of the echo messages coming from the target is used for determining the distance from the sensor to the target. Then trilateration is utilized to calculate target’s position.

Nevertheless, all the aforementioned works are developed based on acoustic networks.

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My Work • Proposes and investigates five different 3D UWSN architectures for detection and localization of underwater intruders.

Each architecture consists of SNs, CHs, sink at water surface and an onshore BS.

•  Localization accuracy: in terms of

normalized mean square error (NMSE) in distance estimation and square-root of mean square error (SRMSE) in direction estimation.

•  Impact of various network parameters, such as node topology, network length and

detection threshold, on the system performance is evaluated and critically analysed

Novelty of this work To the best of our knowledge, there is no complete published work on underwater intruder or target localization networks based on EM communications. So in this aspect, this is the first initiative of it’s kind.

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CH: EM transmitter and receiver. Activation upon Prec ≥ Pth .

gathers local information from SNs and/or other CHs,

forward them to the sink. EM signal power PCH and frequency fCH

SN

CH

Detection zone of an SN

Surface sink

CH in the uppermost layer

CH-to-sink data transfer

Sink to base station

Intruder

Base station (BS)

Overall model of the proposed localization schemes:

SN: Low power sensor package and an omnidirectional EM transmitter.

Pressure sensor, Motion sensor, Metal detector, Passive sonar.

EM signal power PSN and frequency fSN.

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Proposed UWSN Architectures

Architecture A1

𝐷𝐶𝐸 = 1

2∆𝑥2 + ∆𝑦

2 + ∆𝑧2.

𝑥𝑒𝑠𝑡 =1

𝑁 𝑥𝑖

𝑁

𝑖=1

, 𝑦𝑒𝑠𝑡 =1

𝑁 𝑦𝑖

𝑁

𝑖=1

, 𝑧𝑒𝑠𝑡 =1

𝑁 𝑧𝑖

𝑁

𝑖=1

Y

X

Z

∆z

∆x

∆ y

Topology for architecture A1

∆x

∆z

∆y

(1/2) x √(∆x2 + ∆y

2 + ∆z2)

Building block

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Architecture A2 𝑥𝑖𝑛𝑡 =

1

𝑁 𝑥𝑖

𝑁

𝑖=1

,

𝑦𝑖𝑛𝑡 =1

𝑁 𝑦𝑖

𝑁

𝑖=1

,

𝑧𝑖𝑛𝑡 =1

𝑁 𝑧𝑖

𝑁

𝑖=1

Y

X

Z

∆z

∆x

∆ y

Topology for architecture A2

𝑥𝑒𝑠𝑡 =1

𝑁 𝑃𝑚𝑎𝑥 − 𝑃𝑖 𝑥𝑖𝑛𝑡 − (𝑃𝑖𝑛𝑡 − 𝑃𝑖)𝑥𝑖

𝑃𝑚𝑎𝑥 − 𝑃𝑖𝑛𝑡

𝑁

𝑖=1

𝑦𝑒𝑠𝑡 =1

𝑁 𝑃𝑚𝑎𝑥 − 𝑃𝑖 𝑦𝑖𝑛𝑡 − (𝑃𝑖𝑛𝑡 − 𝑃𝑖)𝑦𝑖

𝑃𝑚𝑎𝑥 − 𝑃𝑖𝑛𝑡

𝑁

𝑖=1

𝑧𝑒𝑠𝑡 =1

𝑁 𝑃𝑚𝑎𝑥 − 𝑃𝑖 𝑧𝑖𝑛𝑡 − (𝑃𝑖𝑛𝑡 − 𝑃𝑖)𝑧𝑖

𝑃𝑚𝑎𝑥 − 𝑃𝑖𝑛𝑡

𝑁

𝑖=1

Fine Tuning:

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Architecture A4

Topology for architecture A4

Z

∆z

∆x

∆ y

X

Y

CHs at the regular intervals of 2∆x, 2∆y and ∆z distance along X-, Y- and Z-axis respectively. Each CH is equipped with four directional receivers directed to the four coplanar SNs.

𝐷𝐶𝐸 = 1

2∆𝑥2 + ∆𝑦

2

𝑥𝑒𝑠𝑡 =1

𝑁 𝑥𝑆𝑁,𝑖

𝑁

𝑖=1

,

𝑦𝑒𝑠𝑡 =1

𝑁 𝑦𝑆𝑁,𝑖

𝑁

𝑖=1

,

𝑧𝑒𝑠𝑡 =1

𝑁 𝑧𝑆𝑁,𝑖

𝑁

𝑖=1

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Architecture A3

Topology for architecture A3

CHs at the regular intervals of 2∆x, 2∆y and 2∆z distance along X-, Y- and Z-axis respectively.

𝐷𝐶𝐸 = 1

2∆𝑥2 + ∆𝑦

2 + ∆𝑧2.

∆z

∆x

∆ y

Y

X

Z

𝑥𝑒𝑠𝑡 =1

𝑁 𝑥𝑖

𝑁

𝑖=1

,

𝑦𝑒𝑠𝑡 =1

𝑁 𝑦𝑖

𝑁

𝑖=1

,

𝑧𝑒𝑠𝑡 =1

𝑁 𝑧𝑖

𝑁

𝑖=1

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Architecture A5

Topology for architecture A5

CHs at the regular intervals of 2∆x, 2∆y and 2∆z distance along X-, Y- and Z-axis respectively. Each CH is equipped with eight directional receivers directed to the eight SNs.

𝐷𝐶𝐸 = 1

2∆𝑥2 + ∆𝑦

2 + ∆𝑧2.

∆z

∆x

∆ y

Y

X

Z

𝑥𝑒𝑠𝑡 =1

𝑁 𝑥𝑆𝑁,𝑖

𝑁

𝑖=1

,

𝑦𝑒𝑠𝑡 =1

𝑁 𝑦𝑆𝑁,𝑖

𝑁

𝑖=1

,

𝑧𝑒𝑠𝑡 =1

𝑁 𝑧𝑆𝑁,𝑖

𝑁

𝑖=1

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Performance Metric

𝑟 = (𝑥𝑒𝑠𝑡 − 𝑥)2+(𝑦𝑒𝑠𝑡 − 𝑦)

2+(𝑧𝑒𝑠𝑡 − 𝑧)2

𝑁𝑀𝑆𝐸𝑟 = 𝑟𝑖

2𝑀𝑖=1

𝑀 min(∆𝑥, ∆𝑦 , ∆𝑧)2

where M is the number of Monte-Carlo simulations.

𝜃 = 𝑐𝑜𝑠−1𝑧 − 𝑧𝑒𝑠𝑡𝑟, −90° ≤ 𝜃 ≤ 90°

𝜑 = 𝑡𝑎𝑛−1𝑦𝑒𝑠𝑡 − 𝑦

𝑥𝑒𝑠𝑡 − 𝑥, −180° ≤ 𝜑 ≤ 180°

𝑆𝑅𝑀𝑆𝐸𝜃 =1

𝑀 𝜃𝑖

2

𝑀

𝑖=1

, 𝑆𝑅𝑀𝑆𝐸𝜑=1

𝑀 𝜑𝑖

2

𝑀

𝑖=1

X

Y

Z

Estimated location of an intruder

(r, θ, φ) with respect

to (xest, yest, zest)r

Original system coordinates

Reference point shifted to (xest, yest, zest)

(xest, yest, zest)

(0, 0, 0)

Error in Estimation for a Static Intruder

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Performance Metric

𝑟𝑎𝑐𝑡𝑢𝑎𝑙 = 𝑥1 − 𝑥22 + 𝑦1 − 𝑦2

2 + 𝑧1 − 𝑧22

𝜃 = 𝑡𝑎𝑛−1𝑧𝑒𝑠𝑡1 − 𝑧𝑒𝑠𝑡2𝑥𝑒𝑠𝑡1 − 𝑥𝑒𝑠𝑡2

− 𝑡𝑎𝑛−1𝑧1 − 𝑧2𝑥1 − 𝑥2

, −180° ≤ 𝜃 ≤ 180°

∅ = 𝑡𝑎𝑛−1𝑦𝑒𝑠𝑡1 − 𝑦𝑒𝑠𝑡2𝑥𝑒𝑠𝑡1 − 𝑥𝑒𝑠𝑡2

− 𝑡𝑎𝑛−1𝑦1 − 𝑦2𝑥1 − 𝑥2

, −180° ≤ 𝜑 ≤ 180°

Error in Estimation for a Moving Intruder

𝑟 = (𝑥𝑒𝑠𝑡1 − 𝑥𝑒𝑠𝑡2)2+(𝑦𝑒𝑠𝑡1 − 𝑦𝑒𝑠𝑡2)

2+(𝑧𝑒𝑠𝑡1 − 𝑧𝑒𝑠𝑡2)2

X

Y

Z

(xest1, yest1, zest1)

(0, 0, 0)

(xest2, yest2, zest2)

At time t = 0 At time t = t

Intruder Travelling in the System

(x1, y1, z1)

(x2, y2, z2)

𝑁𝑀𝑆𝐸𝑟 = (𝑟𝑖 − 𝑟𝑎𝑐𝑡𝑢𝑎𝑙,𝑖)

2

𝑀(𝑟𝑎𝑐𝑡𝑢𝑎𝑙,𝑖)2

𝑀

𝑖=1

, 𝑆𝑅𝑀𝑆𝐸𝜃 =1

𝑀 𝜃𝑖

2

𝑀

𝑖=1

, 𝑆𝑅𝑀𝑆𝐸𝜑=1

𝑀 𝜑𝑖

2

𝑀

𝑖=1

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Simulation Setup

• 1000 independent simulations • dimension Dx = Dy = Dz is considered

• path-loss 20×(log10e)×2π× (σ × f × 10−7) dB/m , where where f = transmission frequency (Hz) and σ = conductivity of water (S/m). Typically σ = 0.01 S/m for fresh water and σ = 4 S/m for salty water environment. The second one is considered. • fSN = 6 kHz and fCH = 3 kHz • transmit power PSN = 100 mW and PCH = 1000 mW • network dimension Dx×Dy×Dz = 400×400×400 m3 and ∆x = ∆y = ∆z = 20 m • detection threshold Pth = -60 dBm

CH

per

SN

Each

CH

has

4

dir

ecti

on

al a

nte

nn

as

Each

CH

has

8

dir

ecti

on

al a

nte

nn

as

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Results

0 5 10 15 20 25 30 350

0.2

0.4

0.6

0.8

1

1.2

1.4

A1

A2

A3

A4

A5

CDF of estimation Errors

Distance, r (meter)

Pr

(Estim

atio

n E

rro

r

r)

A1

A2

A3

A4

A5

Absolute distance r

CH

per

SN

Each

CH

has

4

dir

ecti

on

al a

nte

nn

as

Each

CH

has

8

dir

ecti

on

al a

nte

nn

as

-100 -50 0 50 1000

0.2

0.4

0.6

0.8

1

A1

A2

A3

A4

A5

Angle, (degree)

Pr

(Po

lar

An

gle

)

A1

A2

A3

A4

A5

10 12

0.57

0.58

0.59

0.6

Polar angle θ

-100 -50 0 50 1000

0.2

0.4

0.6

0.8

1

A1

A2

A3

A4

A5

CDF of Azimuthal angle (phi)

Angle, (degree)

Pr

(Azim

uth

An

gle

)

A1

A2

A3

A4

A5

-5 0 5

0.48

0.5

0.52

0.54

A1

A3

A4

A5

Azimuthal angle φ 25

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Results

CH

per

SN

Each

CH

has

4

dir

ecti

on

al a

nte

nn

as

Each

CH

has

8

dir

ecti

on

al a

nte

nn

as

1 2 3 4 50

0.2

0.4

0.6

0.8

1

1.2

1.4

NM

SE

of E

stim

ate

d D

ista

nce

(m

2)

A3

A4 A5

A2

A1

1 2 3 4 50

10

20

30

40

50

60

70

SR

MS

E o

f A

ng

le (

De

gre

e)

Polar Angle () Azimuthal Angle ()

A1A5A4A3A2

Average

distance and direction estimation errors

in detecting intruders

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Results

CH

per

SN

Each

CH

has

4

dir

ecti

on

al a

nte

nn

as

Each

CH

has

8

dir

ecti

on

al a

nte

nn

as

400 500 600 700 800 900 1000 1100 1200 13000

0.5

1

1.5

2

2.5

3

A1

A2

A3

A4

A5

Length of the sides of the 3-D networks (m)

NM

SE

in

Estim

ate

d D

ista

nce

(m

2)

A1

A2

A3

A4

A5

400 500 600 700 800 900 10000

0.5

1

1.5 A1

A2

A3

A4

A5

Length of the sides of the 3-D network (m)

NM

SE

in E

stim

ate

d D

ista

nce

(m

2)

A1

A2

A3

A4

A5

(a) Pth = -90 dBm

(b) Pth = -60 dBm

400 450 500 550 600 6500.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

A1

A2

A4

Length of the sides of the 3-D networks (m)

NM

SE

in

Estim

ate

d D

ista

nce

(m

2)

A1

A2

A3

A4

A5

(c) Pth = -30 dBm

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Results for a Travelling Intruder

CH

per

SN

Each

CH

has

4

dir

ecti

on

al a

nte

nn

as

Each

CH

has

8

dir

ecti

on

al a

nte

nn

as

28

1 2 3 4 50

0.1

0.2

0.3

0.4

0.5

0.6

0.7

NM

SE

r of E

stim

ate

d T

rave

lled

Dis

tan

ce

A1

A2

A4 A5

A3

1 2 3 4 50

5

10

15

20

SR

MS

E o

f A

ng

le (

de

gre

e)

Polar Angle

Azimuthal Angle A1 A2

A3

A4

A5

NMSE and SRMSE in distance estimation and

travel direction estimation

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Performance in Intruder Detection with shadowing

CH

per

SN

Each

CH

has

4

dir

ecti

on

al a

nte

nn

as

Each

CH

has

8

dir

ecti

on

al a

nte

nn

as

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for SN-CH communications, signal power received by the CH is

Prec (dBm)= PSN (dBm) –Pathloss (dB/m) × Distance + X (dB)

Where shadowing, X ~ N(0 , SD)

0 5 10 15 20 25 30 35 400

0.01

0.02

0.03

0.04

0.05

0.06

Shadowing SD

Pr

(No

De

tectio

n)

for

Ne

two

rk L

en

gth

60

0m

A1

A2

A3

A4

A5

0 5 10 15 20 25 30 35 400

0.2

0.4

0.6

0.8

1

Shadowing SD

Pr

(No

De

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n)

for

Ne

two

rk L

en

gth

12

00

m

A1

A2

A3

A4

A5

Dx=Dy=Dz=600m Dx=Dy=Dz=1200m

My

Wo

rk

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Summary of Contributions •  studied wireless communications, underwater wireless communications, target localization, underwater target localization, underwater EM wave propagation and finally proposed five different EM wave based 3D UWSN architectures for the detection and localization of underwater intruders into a surveillance area. •  The proposed architectures differ in topology and the principle of localization. Performance has been evaluated in terms of NMSE of estimated distance as well as SRMSE of polar and azimuthal angles. •  Simulation results have demonstrated a great impact of network topology on the localization performance. •  The utilization of information of received EM signal power and the location of SNs in location estimation can substantially improve the localization accuracy. This applies even in presence of underwater shadowing. •  The integration of directional receivers in the CHs can achieve better accuracy even with deploying fewer CHs in the networks. Overall, the results have indicated that underwater target localization and tracking of the movement of the target using fast transmission media EM wave are achievable.

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Future Research • More complex network scenario including mobility,

underwater ambient noises and multi-path fading. • Improving the localization capability further by

integrating denoising filters and channel estimation, and using time-of-arrival (ToA) and angle-of-arrival (AoA) information.

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References [1] Andrea Goldsmith, “Wireless Communications” [2] J. G. Proakis and M. Salehi, “Communication Systems Engineering” [3] S. Al-Dharrab, M. Uysal and T. M. Duman, “Cooperative Underwater Acoustic Communications,” IEEE Communications Magazine, vol. 51, no. 7, pp. 146-153, July 2013. [4] R. Headrick and L. Freitag, “Growth of Underwater Communication Technology in the U.S. Navy,” IEEE Communications Magazine, vol. 47, no. 1, pp. 80-82, Jan 2009. [5] J. Partana, J. Kurosea, and B. N. Levinea, “A Survey of Practical Issues in Underwater Networks,” ACM Mobile Computing and Communications Review, vol. 11, no. 4, pp. 23–33, 2007. [6] S. Arnon and D. Kedar, “Non-Line-Of-Sight Underwater Optical Wireless Communication Network,” Journal of Optical Society America, vol. 26, pp. 530–539, Mar 2009. [7] X. Che, I. Wells, G. Dickers, P. Kear and X. Gong, “Re-evaluation of RF Electromagnetic Communication in Underwater Sensor Networks," IEEE Communications Magazine, vol. 48, no. 12, pp. 143-151, Dec 2010. [8] S. Zhou and P. Willett, “Submarine Location Estimation via a Network of Detection-only Sensors,” IEEE Transactions on Signal Processing, vol. 55, no. 6, pp. 3104–3115, June 2007. [9] Y. Huang, W. Liang, H.-B. Yu, and Y. Xiao, “Target Tracking Based on a Distributed Particle Filter,” Wireless Communications and Mobile Computing, vol. 8, no. 8, pp. 1023–1033, Oct. 2008. [10] G. Isbitiren and O. B. Akan, “Three-Dimensional Underwater Target Tracking with Acoustic Sensor Networks,” IEEE Transaction on Vehicular Technology, vol. 60, no. 8, pp. 3897–3906, Oct. 2011. [11] Z. Madadi, G. V. Anand and A. B. Premkumar, “3-D Source Localization in Shallow Ocean with Non-Gaussian Noise using a Linear Array of Acoustic Vector Sensors,” Proc. IEEE International Conference on Information Science, Signal Processing and their Applications (ISSPA), Montreal, QC, Canada, pp.1353-1358, July 2012. [12] W. Cheng, A. Y. Teymorian, L. Ma, X. Cheng, X. Lu and Z. Lu, “Underwater Localization in Sparse 3D Acoustic Sensor Networks,” Proc. IEEE International Conference on Computer Communications (INFOCOM), Phoenix, AZ, USA, pp. 13-18, Apr 2008. [13] N. M. A. Latiff, C. C. Tsimenidis and B. S. Sharif, “Energy-Aware Clustering for Wireless Sensor Networks using Particle Swarm Optimization," Proc. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), pp. 1-5, Sep 2007. [14] M. Younis, M. Youssef, and K. Arisha, “Energy-Aware Management for Cluster-based Sensor Networks,” Computer Networks, vol. 43, no. 5, pp. 649-668, Dec 2003.

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THANK YOU