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Muhamad
Felemban, BasemShihada, and
KamranJamshaidDepartment of Computer Science, CEMSE Division,
KAUST, Saudi Arabia
Optimal Node Placement in Underwater
Wireless Network
1
Presentation Outline
Introduction and Motivation
Objective
Network Model
Underwater Communication
Problem Formulation
Results
Simulation Setup and Results
Conclusion
2
Introduction
Most of the Earth is covered by water
Underwater operations are difficult
Monitoring tasks:
Habitat monitoring
Data sampling
Critical tasks:
Oil spill, Mexico Gulf 2010
3
Motivation
AUV Limitations:
Off-line configuration
Non real-time monitoring
Limited Bandwidth and high propagation delays
Use Underwater Wireless Sensor Network UWSN to
over come theses limitations
But
High cost deployment
Large power consumption
Limited hardware
4
Paper’s Objective
5
Find the optimal distance between two nodes such
that
Attains maximum coverage and connectivity
Minimizes transmission loss between nodes
Find an optimal node placement strategy to support
AUV’s operations such that
Minimum number of nodes is used for a given volume
Maximum coverage volume for certain number of nodes
Network Model
Surface Gateways (SG): EM and acoustic transceivers
Relay Nodes (RN): homogenous transceivers
Uniform transmission power
Each node forms a communication sphere of
radius r
Two nodes are connected if inter-distance is
less than or equal r
Nodes are statically placed and maintain
their positions
Ocean is divided horizontally into regions
based on the depth
Propagation characteristic is different
in each region
6
r
RN
SG
Network Model
7
Find a space-filling polyhedron that approximates the
communication sphere
The best polyhedron to approximate a sphere has a
large volumetric quotient
Truncated Octahedron (TO) has
volumetric quotient of 0.68
Node placement strategy is to
tessellate TOs of radius R using
where
Underwater Communication
SNR is computed using the passive sonar equation
[Urick]
Transmission Loss δ
Two factors
Energy spreading
K = 15
Wave absorption
α is computed using Ainslie and McColm model
[Ainslie&McColm]
Temperature, frequency, depth, salinity, and acidity
8
Underwater Communication
Absorption coefficient α
Increases with frequency
Decreases with depth
9
Problem Formulation
Problem P given: k,f, d, T, Rmax, V, and N
Minimize
Subject to
10
Results
11
Transmission loss of deep water at 10000 m depth
Results
There exists a range of frequencies with longer
transmission distance, because of the reduction in
ambient noise
As depth increases, higher frequencies can be used
for larger transmission distance
High BER can tolerate larger frequencies and further
transmission distance
Higher power increases transmission range
BPSK and QPSK perform better than 16-QAM
Small bit/symbol is better in low data-rate networks
12
Results
Maximum transmission range at different depths with Ptx= 100
W13
Results
Maximum transmission range with different transmission power at depth of
10000 m14
Results
Maximum transmission range with different BER at depth of
10000 m15
Results
Maximum transmission range with different modulation schemes at depth of
10000 m16
Simulation Setup
NS-3 simulator with UAN framework
Contributions to UAN framework
Added new propagation models
Added passive sonar equation to calculate SNR
Modified MAC AlOHA to work with UDP client and server
application
PER of 90% if received SNR ≥ SNRth
17
Simulation Results
Maximum transmission range to maintain cut-off threshold of 19.47 at depthof
7500 m18
Conclusions
Higher frequencies provide more channel capacity
but more susceptible to transmission loss
Optimal operating frequency is around 40 KHz in shallow
water, and 100 KHz in deep water
Low symbol modulation is more suitable for UWSN
BPSK and QPSK
19
References
20
[Urick] R. Urick, “Principles of underwater sound,”
New York, 1983.
[Ainslie&McColm] M. Ainslie and J. McColm, “A
simplified formula for viscous and chemical
absorption in sea water,” Journal of the Acoustical
Society of America, vol. 103, no. 3, pp. 1671–
1672, 1998.
Questions and Discussion
21
Conclusions