Survey Of Temperature Variation Effect On ...

SETIT 2009 5th International Conference: Sciences of Electronic, Technologies of Information and Telecommunications March 22-26, 2009 – TUNISIA

Survey Of Temperature Variation Effect On Underwater Acoustic Wireless Transmission Ghada ZAÏBI *, Nejah NASRI *, Abdennaceur KACHOURI * and Mounir SAMET * *

Laboratory of Electronics and Technologies of Information (LETI) National School of Engineers of Sfax B.P.W, 3038 Sfax, Tunisia [email protected] [email protected] [email protected] [email protected]

Abstract: In acoustic channel it is known that low available bandwidth, highly varying multipath, large propagation delays, noise and physical channel properties variation, in addition to the power consumption restrict the efficiency of underwater wireless acoustic systems. The physical characteristics of Mediterranean channel such as depth, salinity and especially temperature influence the acoustic signal attenuation, the SNR level, error ratio and the signal bandwidth. It seems to be crucial to introduce these parameters in the underwater channel model of the underwater wireless communication network. The goal of this paper is to highlight the impact of the temperature variation at shallow sea on the acoustic signal attenuation and error ratio and explain the relationship between temperature and SNR as well as the optimal frequency of the transmitted signal. Key words: optimal frequency, SNR, temperature, underwater acoustic channel. as temperature, salinity and depth variation [SOZ 00], [WON 05], [COP 82]. This article characterizes the underwater channel and presents its physical characteristics survey as well as its parameters variation effect on the acoustic signal attenuation. We establish the relationship between SNR and temperature values and finally we evaluate temperature changes effect on image quality.

INTRODUCTION In spite of the use of electromagnetic wave, especially radio wave in the mobile radio communications, its propagation in underwater environment is limited in few meters. They are rapidly attenuated and require an important transmission power as well as large antennae. On the other hand, acoustic communications showed the best performances compared to the electromagnetic waves. Thus, underwater wireless communications are established by acoustic wave but remain far from an ideal communication. The transmission of a reliable underwater acoustic signal with the least distortions and the minimum emission power is attracting increasing interest from researchers considering the unfavourable conditions of the shallow underwater environment, as well as the problems encountered when providing the system with energy [AKY 05]. Several alternatives try to improve signal’s quality (signal to noise ratio, inter-symbols interferences (ISI)) and reduce power consumption of the underwater channel by modelling a suitable underwater transmission system neglecting the physical specifications of the underwater channel such

1. Description of underwater channel The underwater channel is an intermittent channel. It is characterized by its limited bandwidth, long propagation delays and frequency selectivity This Disturbance and characteristics of the underwater channel are represented by three blocks. In fact, the noise of the underwater is modelled by the AWGN channel. The Rayleigh channel represents the multipath delay, fading and Doppler frequency shift. Moreover, adding an attenuation block is necessary to model transmission losses. Variables f, d, p are respectively frequency, distance and physical characteristics (figure 1).

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SETIT2009 Table 1. Average values of temperature (wintersummer) and salinity of the Mediterranean

Figure 1. Underwater channel model. 1.1. Physical characteristics of the underwater channel In underwater acoustic communication, the well known of the underwater channel will improve the modelling of the underwater acoustic system. The characteristics of the ocean or sea water such as salinity, temperature, density, and current velocity do not vary smoothly with depth, but in a discontinuous fashion. They remain almost constant within certain layers and change rapidly in passing from one layer to another. The thickness of these layers varies from tens of centimetres to tens of meters.

1.1.2. Attenuation of the acoustic wave in marine environment The sound propagation in sea water leads to acoustic signal attenuation. This attenuation is due to two effects: geometrical considerations like diffusion and non geometrical effect like absorption.

In fact, this discontinuity of water layers has a deep effect on the transmission of an acoustic signal by increasing or decreasing the attenuation value.

The signal attenuation is given by the following formula (1):

1.1.1. Mediterranean sea The Mediterranean Sea is the crossroad of the three continents: Europe, Asia and Africa. It is a deep sea. Its average depth is about 1500 meters and its maximum depth is 5121 meters (narrow trench in the south of the Cape Matapan (Greece)). Mediterranean sea-beds form a chaotic zone. They are characterized by variable depths (siculo-Tunisian threshold (400m); Gibraltar (less than 100m); the Turkish straits threshold (less than 50m)) [SIM 02].

A( x )  x

k

x( f ) 10 10

(1)

Where k represents the spreading factor (k = 1 for cylindrical spreading, k = 1.5 is the practical value and k = 2 for spherical spreading) and β(f) is the absorption coefficient in dB/Km (for x is the distance between 2 nodes on Km) .

The temperature and the salinity of Mediterranean water (from Western to Eastern Mediterranean) undergo fluctuations which are influenced by the currents and the depths [ERI 65]. Indeed, water surface temperature varies between 10 and 30 °C, and is stabilized in-depth in an average value of 13 °C. These values are different from surface Atlantic temperatures which reach 4 °C and even less.

The absorption coefficient is chosen for frequencies between 3 KHz and 0.5 MHz, according to Marsh and Schulkin empirical formula [STO 06], as:



 S.A.f .f 2 B.f 2   1  6.54.10 4 P (f )  8.68.103  2 T 2   f f f T   T

The rate of salinity of the Mediterranean varies between 36 ‰ (in the west) and 39 ‰ (in the east) [BET 05] and the average rate is close to 37,5 ‰. Its value oscillates around 36 ‰ close to the Gibraltar Straits where water marries by the currents with those of the Atlantic and it is between 38 and 39 ‰ at the Gulf of Gabes (minor Syrte).



(2)

For f in KHz, A = 2.34.10-6, B = 3.38.10-6, S is the salinity (‰), P is the hydrostatic pressure [kg/cm²], and fT = 21.9.106-1520/(T+273) is the relaxation frequency (kHz) with T is the temperature between 0 and 30 °C. It takes into account the variation of salinity, depth and temperature which characterise absorption due to the MgSO4 relaxation. It depends also on the shear viscosity and volume viscosity in the frequency band 100 Hz -100 Khz.

The average values of the temperature (wintersummer) and salinity measured at various depths (m) for several places of the Mediterranean Sea is illustrated by the following table [ZEN 02].

Both expressions (1) and (2) of signal attenuation and absorption coefficient are used to extract how physical sea water characteristics attenuate the propagated signal.

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SETIT2009 compensating its effect.

2. Sea water characteristics impact on the Signal attenuation

For the other characteristics, their impact is less important than temperature especially for the Mediterranean Sea because it’s not as deep as the Atlantic Ocean, for example. The attenuation difference between salinity extremes is about 5.10 -2 ‰ at 200 m depth, 1 Km distance, average temperature and 10 KHz frequency.

In this subsection, the focus was on the temperature variation especially at shallow sea and its impact on the acoustic signal attenuation. Indeed, the research showed the parameters which increase the temperature influence on signal attenuation when propagated in shallow water. Table 2(a) shows that for two nodes separated by 1Km distance and placed at 200 m depth, with an average salinity, attenuation variation is about 0.46 dB for 10 KHz signal frequency at the extreme Mediterranean sea temperature values.

In the next subsection, the underwater channel characteristics chosen are 10 KHz as frequency, 200m as depth, an average salinity (37.5 ‰) and temperature (13 °C).

Table 2(a). Attenuation variations for different temperatures at 1 Km and 10 KHz.

3. Relationship between the environment temperature and SNR 3.1. Ambient noise The ambient noise is the background noise of the ocean due to four causes: turbulence, shipping activities, waves and thermal noise [STO 06], [LUR 98]. It can be described by Gaussian statistics and a continuous power spectral density (p.s.d). The following formulae present the p.s.d of the four noise sources in dB re µ Pa per Hz as a frequency function in KHz:

If the distance between the two nodes increases, example 5 Km (table 2(b)), then the temperature influence is a linear function and the attenuation difference reaches 2.3 dB at the same frequency.

10 log(N t (f ))  17  30(log(f ))

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10 log(N s (f ))  40  20(s  0.5)  26 log(f ) 

Table 2(b). Attenuation variations for different temperatures at 5 Km and 10 KHz.

[60 log(f  0.03)]

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10 log(N w (f ))  50  7.5w 1 2  20 log(f )  [40 log(f  0.4)]

(5)

10 log(N a (f ))  15  20 log(f )

(6)

In the second expression, shipping activity factor s varies between 0 and 1. The wind speed is w in m/s. The total spectral density p.s.d of the ambient noise is the combination of its four components:

In addition, if we use high signal frequencies, for example 50 KHz (table 2(c)) then the variation of attenuation is about 7.65 dB.

N(f )  N t (f )  N s (f )  N w (f )  N a (f )

Table 2(c). Attenuation variations for different temperature at 1 Km and 50 KHz.

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3.2. Temperature variation and the SNR By taking into account transmission loss, SNR(x, f, T) will be given by:

SNR(x, f, T) =

P A(x, f, T)N(f) Δ(f )

(8)

Where ∆f is the receiver noise bandwidth (a narrow band around the frequency f).

So, for high signal frequencies and huge distance, temperature variations effect on signal attenuation in shallow water, becomes more and more important and must be taken into account when modelling a transceiver by including it in the channel model and

The factor 1/A(x, f, T)N(f) is illustrated in figure 2 and represent the frequency dependent part of the SNR. For each transmission distance x, there exist an -3-

SETIT2009 optimal frequency fo for which the maximum SNR is obtained. Figure 3 shows the optimal frequency as a function of the distance for T=13°C. We notice that optimal frequency varies between 4 and 51 KHz for a distance variation from 1 to 100 Km. We can set then the transmission bandwidth around the optimal frequency and we adjust the transmission power to achieve the desired SNR level.

geographic coverage is greater than the unpartitioned link-layer coverage of all nodes [PAR 06]. 1/A*N en fonction de la fréquence -60 -80 T=30°C -100 T=4°C

T=13°C

-120

1/A*N en fonction de la fréquence

1/AN(dB)

0 -20

-140 -160

5Km -40

-180

10Km

1/AN(dB)

-60

-200

50Km

-80

-220

100Km -100

-240

0

2

4

6

8 10 12 fréquence(KHz)

-120

14

16

18

20

Figure 4. Frequency dependent part of the SNR, 1/A(x, f, T)N(f) factor at different temperatures for x=50 Km.

-140 -160

0.0

2

4

6

8

10 12 fréquence(KHz)

14

16

18

20

fo(x,T) en fonction de la température 46

Figure 2. Frequency dependent part of the SNR, 1/A(x, f, T)N(f) factor at different distances for T=13 °C, k=1.5, moderate shipping activity(s=0.5) and w=0.

44

fréquence optimale(KHz)

fo(x,T) en fonction de la distance 60

T=13°C

fréquence optimale(KHz)

50

X=2Km

42 40 38 36 34 32

40

30 30

28 20

15 température(°C)

10

Figure 5. Optimal frequency VS temperature for x=2 Km.

0

0

5

10

20

25

30

fo(x,T) en fonction de la température 1

10

20

30

40

50 60 distance(Km)

70

80

90

50

100

x=5Km x=4Km x=3Km x=2Km

45 fréquence optimale(KHz)

Figure 3. Optimal frequency VS distance for T=13°C. Temperature fluctuation of the sea surface layers creates an important optimal frequency variation (figure 4) from 28 to 46 KHz for a distance equal to 2 Km (figure 5). If we increase the distance between two nodes (figures 4 and 6), the optimal frequency fo decreases and the temperature variation effect becomes less important. In fact, for x=5 Km fo varies from 15 to 28 KHz and for x=50 Km it varies from 4 to 9 KHz. We notice that surface layers temperature variation effect on the optimal frequency, so on the SNR, is less important than the distance variation effect. So that, these results will be useful for single hop and multi-hops networks, and less important for DTN (disruption-tolerent network) network, where the

40 35 30 25 20 15

0

5

10

15 température(°C)

20

25

30

Figure 6. Optimal frequency VS temperature for x=2 Km, 3Km, 4Km, 5Km.

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SETIT2009 three specific temperatures (0 °C for arctic area, 13°C and 30°C for Mediterranean sea). We choose 2 different distances between the transmitter and the receiver. We find that the influence of the temperature on MSE and PSNR grows up significantly with distance. For X=3 Km, the highest sea temperatures cause the lowest MSE value. From the inverse relation between the MSE and PSNR, this translates to a high value of PSNR. It means that the ratio of Signal to Noise (image to error) is more significant and fewer errors are produced. We choose to increase the distance (X=5 Km) for better seeing the received image degradation according to the medium temperature (table 4). Indeed, the error increase and we obtain a large variation of MSE and PSNR values for the different temperatures. These results confirm the conclusions in section 2 and 3.

4. Image quality and temperature variation The temperature variation effect is proven by transmitting a data through the underwater channel. In fact, we transmit an 80x80 RGB color image (Lena), which is the composition of three gray scale images. We used a digital binary modulation (BPSK) and an LMS (Least mean square) equalizer to compensate the ISI caused by the rough channel. We change the water temperature, fixe the distance at 3 Km, and we compute the difference between the transmitted and the received images, which can be seen in figure (7).

Table 3. MSE and PSNR at different temperatures and X=3 Km. MSE_R

MSE_G

MSE_B

Psnr_R

Psnr_G

Psnr_B

1297.9

2083.3

4633

16.99

14.94

11.472

T=13 1055.9

1628.7

2475.6

17.89

16.01

14.194

T=30 944,34

1575,4

1885.6

18.38

16.15

15.376

T=0

7(a)

7(b)

Table 4. MSE and PSNR at different temperatures and X=5 Km.

7(c)

7(d)

Figure 7. (a) Original image, (b) received image T=0°C, (c) received image T=13°C, (d) received image T=30°C. We used two of the error metrics to compare the difference between the transmitted and the received image, for different temperatures: the Mean Square Error (MSE) and the Peak Signal to Noise Ratio (PSNR) given by the following formulas:

MSE 

1 M1N1  [PS (x, y)  Pr (x, y)] 2 M * N x 0 y 0

 255 2   PSNR  10 log10    MSE 

MSE_R

MSE_G

MSE_B

Psnr_R

Psnr_G Psnr_B

T=0

2786.4

9685.2

13487

13.68

8.2697

6.8318

T=13

1446.5

3477.8

6160

16.52

12.718

10.235

T=30

1310.5

2114.2

4735.8

16.95

14.879

11.377

5. Conclusion In this article the focus was on the temperature variation effect, at different frequencies and nodes distances, on acoustic signal attenuation, and its impact on SNR ratio, optimal frequency and signal quality. First, we ensured that the temperature influence is a linear function of the distance between two nodes. Otherwise, minimizing this distance will decrease its negative impact on the acoustic signal.

(9)

(10)

For frequencies higher than 50 KHz the temperature variation becomes very important and must be taken into account when modelling a transceiver and include it in the channel model.

With: PS (x, y): the pixel value at the position (x, y) in the original image. Pr (x, y): the pixel value at the position (x, y) in the received image. M and N are the dimensions of the image.

Secondly, we studied the relationship between SNR Ratio and temperature variation and we extracted optimal frequencies values which allowed the maximum SNR ratio.

Table 3 and 4 present the three MSE and PSNR values corresponding to Red, green and blue color for

When the temperature of the surface layers varies -5-

SETIT2009 (from 0 to 30C) we notice an almost linear optimal frequency increase especially with an important distance between two nodes (5 Km). In addition, we noticed that if we increase the distance between two nodes, the optimal frequency fo decreases and the temperature variation effect becomes less important. So, this parameter will not very useful in DTN networks but crucial in single and multi-hops network. Finally, we evaluate temperature effect on image transmission quality in a harsh underwater environment, and we confirmed that low temperature in shallow water causes less errors and better image quality. Future research should focus on using these results to fix the signal bandwidth and the channel capacity according to the SNR threshold needed for a specific underwater wireless transmission system, so it will help us to optimize our transceiver.

REFERENCES [AKY 05] I. F. Akyildiz, D. Pompili and T. Melodia, Underwater acoustic sensor networks: research challenges, Ad Hoc Networks 3, (2005) 257–279.

[BET 05] J. P. Béthoux, Cycles Bio-Geochimiques en Méditerranée en relation avec le climat et l’environnement, évolutions présentes et passées, Observatoire Océanologique, France, 2005.

[COP 82] A. B. Coppens and all, Fundamentals of acoustics, third edition, John Wiley and sons, 1982.

[ERI 65] Erimesco, La mer et l’atmosphère des côtes marocaines, Bulletin de l’ISPM,, n° 13, pp. 3-19, 1965. [LUR 98] X. Lurton, Acoustique sous-marine Présentation et applications, Moscow, Russia, Edition IFREMER, 1998. [PAR 06] J. Partan, J. Kurose1, and B. N. Levine, A Survey of Practical Issues in Underwater Networks, WUWNet’06, September 25, 2006, Los Angeles, California, USA.

[SIM 02] M. Simmonds, L. Nunny, Cetacean Habitat Loss and Degradation in the Mediterranean Sea, Accobams secretariat, Monaco, February 2002, section 7, 23 p.

[SOZ 00] E.M. Sozer, M. stojanovic, J.G Proakis, Underwater acoustic network, IEEE journal of oceanic engineering, vol.25, No.1, January 2000. [STO 06] M. Stojanovic, On the Relationship Between Capacity and Distance in an Underwater, International Conference on Mobile Computing and Networking Proceedings of the 1st ACM international workshop on Underwater networks, 2006.

[WON 05] S.K.H. Wong, Underwater acoustic simulator for communication, Rev.8, pp. 9-10, 2005. [ZEN 02] A. Zenetos, I. Siokou-Frangou,O. GotsisSkretas, Europe's biodiversity.Seas around Europe S, Environmental issue report, (European Environment Agency) Copenhagen 2002).

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