In underwater wireless sensor network (UWSN) size estimation signal length possesses a great significance. Node estimation using conventional protocol based techniques in underwater wireless sensor network (UWSN) poses a great challenge due to unique characteristics of underwater network. Under water communication is difficult due to factors like multi-path propagation, time variations of the channel, small available bandwidth and strong signal attenuation, especially over long ranges. In underwater communication there are low data rates compared to terrestrial communication, since underwater communication uses acoustic waves instead of electromagnetic waves. An alternative approach of node estimation based on statistical signal processing using cross-correlation of Gaussian signals received at three sensors is applied for this purpose. Cross-correlation based estimation technique is best suited for this kind of environmental network. As signal- length is an energy-related term so the greater the signal-length, the more energy is required to perform the estimation.

1. Thesis Title
Impact of Signal Length in Cross-
Correlation Based Underwater Network
Size Estimation
December 26, 2015
Presented by Supervised by
Samir Ahmed Shah Ariful Hoque Chowdhury
Roll No: 104018 Assistant Professor
Dept. of ETE, RUET.
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2. Contents
• Introduction
• Importance of node estimation
• Importance of signal length
• Underwater environment
• Literature review
• Impact of signal length in node estimation
• Corresponding works
• Comparison
• Future work
• Conclusion
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3. Introduction
• Node - communication endpoint, terminal equipment.
• Sensor- receiving node, capable of performing some
processing, gathering sensory information and communicating
with other connected nodes.
• Cross-correlation- a measure of similarity between
two waveforms
• Underwater wireless acoustic sensor network (UWASN)
• Signal length – Energy related term
• TS case – triangular sensors case, sensors placed in triangular
shape
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4. Importance of node estimation
• To ensure proper network operation
• Successful data collection
• Network maintenance
• To maintain communication quality
• Background noise calculation
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5. Importance of Signal Length
• Signal length possesses a very important role in size
estimation of underwater wireless sensor network (UWSN)
• The greater the signal length the greater energy is required
for estimation
• Ideally the signal length is infinity (we consider 106
samples)
• It plays a great role in estimating number of nodes
• Accurate node estimation is being observed and discussed
in this thesis
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6. Underwater environment
• Long propagation delay
• High path loss
• Strong background noise
• Non-negligible capture effect
• Multipath signal propagation
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7. Cross-correlation based node
estimation using two sensors [1]
• Basic theory: cross-correlation of two Gaussian signals
results a delta.
• Estimation parameter: ratio of standard deviation to
the mean, R of the cross-correlation function (CCF).
• Low protocol complexity
• Delay insensitive
• Not affected by capture effect
• Less time required
• Applicable to any environment network
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8. Cross-correlation based node
estimation using two sensors [1]
Figure. Distribution of underwater network nodes and sensors.
December 26, 2015
0 Distance between sensors, dDBS
0
y-axis
z-axis
0
x-axis
D
D
D
Distribution of nodes and sensors
Nodes
Sensors
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9. Cross-correlation based node
estimation using two sensors [1]
Figure. Bins, b in the cross-correlation process.
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-1.0 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1.0
0
20
40
60
80
100
Distance (m)
CoefficientvalueofCCF
Bins, b
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10. Cross-correlation based node
estimation using two sensors [1]
• Estimation parameter, R :
𝑅 =
𝜎
𝜇
=
𝑁×
1
𝑏
× 1−
1
𝑏
𝑁×
1
𝑏
=
𝑏−1
𝑁
so, 𝑁 =
𝑏−1
𝑅2
where b is the number of bins, which is twice the number of samples
between the sensors (NSBS), m minus one and can be expressed as:
𝑏 =
2×𝑑 𝐷𝐵𝑆×𝑆 𝑅
𝑆 𝑝
̶̶ 1
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11. Cross-correlation based node
estimation using two sensors [1]
Figure. R versus N for b = 19 with dDBS = 0.5m and SR = 30kSa/s.
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0 20 40 60 80 100
0
1
2
3
4
5
6
7
8
Number of nodes, N
RofCCF
Theoretical
Simulated
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12. Cross-correlation based node estimation
using three sensors (SL) case[2]
Figure. Distribution of underwater network nodes for sensors in line (SL) case
with N transmitting nodes (left) and only one node N1 (right).
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0
0
0
y-axis
z-axis
x-axis
D
D
D
Sensors
Node
H3H1 H2
N1
d11
d12
d13
dDBS12
0
0
y-axis
z-axis
0
x-axis
D
D
D
Distribution of nodes and sensors
Sensors
Nodes
dDBS23
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13. Cross-correlation based node estimation
using three sensors (TS) case [3]
Figure. Distribution of underwater network nodes for triangular sensors (TS)
case with N transmitting nodes (left) and only one node N1 (right).
December 26, 2015
d11
d12
d13
dDBS12
dDBS23
0
Nodes
0
y-axis
z-axis
0
x-axis
D
D
D
Distribution of nodes and sensors
Sensors
0
z-axis
D
0 0
y-axis
x-axis
D
D
Sensors
Node
H3
N1
H1
H2
dDBS31
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14. Block diagram for TS case
December 26, 2015
d11
d12
d13
dDBS12
dDBS23
(t)Src1
(t)Src2
(t)Src3
C12(τ)
C23(τ)
σ12
σ23
μ23
μ12
R12
R23
Raverage
3CCF
⁞
⁞
⁞
Ratio
σ31 / μ31
Average
Ratio
σ12 / μ12
Ratio
σ23 / μ23
Mean
Mean
Standard deviation
Mean
Standard deviation
Sensors
Gaussian
signals Composite
Gaussian
signals
Cross-correlation
Cross-correlation
Nodes
N1
N2
N3
NN
H3
H2
H1
Cross-correlation
Standard deviation
C31(τ) μ31
σ31
R31
dDBS31
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15. Cross-correlation based node estimation
using three sensors (TS) case [3]
For TS case, estimation parameter, 𝑅average
3CCF can be expressed as:
𝑅average
3CCF =
𝑅12 + 𝑅23 + 𝑅31
3
=
𝑏12 − 1
𝑁
+
𝑏23 − 1
𝑁
+
𝑏31 − 1
𝑁
3
For efficient estimation,b12=b23=b31=b,
so, 𝑁𝑒𝑠𝑡 = 𝑏−1
(𝑅average
3CCF )2
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16. Cross-correlation based node estimation
using three sensors[]
Figure: versus N for SL case (left) and versus N for TS case
(right) with b = 19. [dDBS = 0.5m and SR = 30kSa/s]
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0 20 40 60 80 100
0
1
2
3
4
5
6
7
8
Number of nodes, N
R
2CCF
average
ofCCFs
Theoretical
Simulated
0 20 40 60 80 100
0
1
2
3
4
5
6
7
8
Number of nodes, N
R
3CCF
average
ofCCFs
Theoretical
Simulated
2CCF
averageR 3CCF
averageR
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17. December 26, 2015
Effect of signal length in estimation
process, (SL case) [4]
10
2
10
3
10
4
10
5
10
6
0
5
10
15
20
25
30
35
Signal length in Number of samples (Ns
)
Numberofnodes,N
Nest
vs Ns
plot for SL case
Nest
vs Ns
plot for Two sensor case
Exact 32 Nodes
Figure. Estimated N versus Ns plot (x-log, y-normal scale) for two & three
sensor (SL case) method with fixed value of b = 119 using exact 32 nodes
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18. December 26, 2015
Impact of signal length in cross-
correlation based estimation (TS case)
• The channel is considered as ideal
• Receivers are assumed to be ideal
• No multipath effect is considered
• No Doppler shift is considered
• Network dimension — 3D spherical
• Transmitted Signal — White Gaussian
• Signal power — Equal received powers from all nodes
Some initial assumption
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19. Nominal simulation parameters
• Dimension of the sphere, D = 2000m
• Speed of acoustic wave propagation, SP = 1500m/s
• Signal length, Ns = 106 samples (varied for comparison)
• Absorption coefficient, a = 1
• Dispersion factor, k = 1.5
• Distance between equidistance sensors = 1m (can be varied)
Estimation parameter
𝑁𝑒𝑠𝑡 = 𝑏−1
(𝑅average
3CCF )2
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20. Corresponding Result
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10
2
10
3
10
4
10
5
10
6
10
0
10
1
10
2
signal Length, Ns
numberofnodes,N
N vs Ns plot for exact 32 nodes
Estimated N vs Ns plot for b=119
Estimated N vs Ns plot for b=39
Estimated N vs Ns plot for b=19
Figure. Estimated N versus Ns plot (x-log, y-log scale) three sensor (TS case)
method with value of b = 19, b=39 and b=119 using exact 32 nodes
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21. Corresponding Result
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10
2
10
3
10
4
10
5
10
6
10
0
10
1
10
2
signalLength, Ns
numberofnodes,N
Estimated N vs Ns plot for 64 nodes,b=39
N vs Ns plot for exact 64 nodes
Estimated N vs Ns plot for 32 nodes,b=39
N vs Ns plot for exact 32 nodes
Figure. Estimated N versus Ns plot (x-log, y-log scale) three sensor (TS case)
method with fixed value of b = 39 using 32 nodes and 64 nodes.
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22. Comparison between TS case and SL case
December 26, 2015
10
2
10
3
10
4
10
5
10
6
10
0
10
1
10
2
signalLength, Ns
numberofnodes,N
N vs Ns plot for exact 64 nodes
Estimated N vs Ns plot for TS case,b=19
Estimated N vs Ns plot for SL case,b=19
Figure. Estimated N versus Ns plot (x-log, y-log scale) three sensor (TS and SL
case) method with fixed value of b = 19 using exact 64 nodes
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23. Future work
• Estimation with unequal distances between the sensors
• Estimation with non-uniform distribution of nodes
• Estimation with different shape of network
• Estimation with random placement of the sensors
• Estimation with variable propagation delay
• Use of Non-Gaussian signals for estimation
• Estimation with M number of sensors
• This thesis consider only ERP case, so ETP, RTRP cases
requires further work
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24. Conclusion
• Using three sensors method, TS case, we can estimate the
number of nodes easily with reduced signal length for which
the required energy will be less than the SL case, three sensors
method
• In this thesis we use smaller signal length than two sensor
technique and provide better performance in estimation
process
• TS case, three sensors techniques provide better performance
than any other techniques in small area
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25. References
[1] M. S. Anower, M. R. Frater, and M. J. Ryan, ―Estimation by cross-correlation of the number of
nodes in underwater networks,‖ In Proceedings of Australasian Telecommunication Networks
and Applications Conference (ATNAC), 10–12 November, 2009, pp. 1–6. doi:
10.1109/ATNAC.2009.5464716.
[2] S. A. H. Chowdhury, M. S. Anower, and J. E. Giti (2014), ―A signal processing approach of
underwater network node estimation,‖ In Proc. International Conference on Electrical
Engineering and Information Communication Technology (ICEEICT) 2014, Dhaka, 10−12
April, 2014.
[3] S. A. H. Chowdhury, M. S. Anower, and J. E. Giti (2014), ―Effect of sensor number and location
in cross-correlation based node estimation technique for underwater communications network,‖ in
Proceedings of 3rd International Conference on Informatics, Electronics & Vision (ICIEV 2014),
23–24 May, 2014, Dhaka, Bangladesh
[4] M.A. Hossen, S.A.H. Chowdhury, M. S. Anower (2015), ―Effect of signal length in cross-
correlation based underwater network size estimation‖ Paper id 528_ICEEICT 2015
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26. Thank you
December 26, 2015
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