A study of the performance of vector based forwarding in underwater acoustic sensor network Chetan Ragpot, Nassirah Laloo and Mohammad Sameer Sunhaloo
Raja K. Subramanian
School of Innovative Technologies and Engineering University of Technology Mauritius La Tour Koenig, Mauritius. E-mail:
[email protected],
[email protected],
[email protected]
E-mail:
[email protected]
Abstract — In this paper, we investigate the performance of the vector based forwarding protocol in shallow and deep water in terms of propagation delay and signal to noise ratio. We determine whether water is shallow or deep by varying the underwater propagation speed at different depth. We evaluate vector based forwarding in shallow and deep water through simulation using Aqua-Sim on ns-2. We have observed that vector based forwarding performs better in deep water than in shallow water. Keywords: Underwater Acoustic Sensor Network, Vector Based Forwarding, Propagation Speed, Propagation Delay.
1.0 Introduction Wireless Sensor Network (WSN) in aqueous medium also known as Underwater Acoustic Sensor Network (UASN) is distinctive due to its surrounding environment. This area of study is attracting the interest of many researchers and has enabled a broad range of applications including information collection, assisted monitoring, mine reconnaissance, equipment monitoring, disaster prevention, under ocean exploration and environmental monitoring [6]. WSN in aqueous medium has the ability to explore the underwater environment in details [6]. To ensure maximum efficiency, a good communication system as well as an effective routing protocol is needed. This will enable the underwater devices to communicate precisely. Underwater propagation speed varies with temperature, salinity and depth. By varying the underwater propagation speed at different depths, two scenarios may be examined accurately namely: shallow and deep water. In both shallow and deep water, different ambient noise levels and different spreading factors may be applied and analyzed to determine the efficiency of specific routing protocols. This paper is organized as follows. In Section 2, we present a general 3D Architecture and a 3D architecture with Autonomous Underwater Vehicles (AUV). Vector based forwarding (VBF), which is an efficient and robust underwater routing protocol is described in Section 3 and in the same section we introduce our concept. Finally in Sections 4 to 6 we describe the implementation and results of the simulation performed followed by a conclusion in section 7.
2.0 Three-Dimensional Architecture We consider a general 3D model and a 3D model with AUV [1]. In the general 3D model, the sensor nodes are anchored at different depths and are equipped with a floating buoy, which can be inflated by a pump. The sensors can be regulated by adjusting the length of the wire that connects the sensor to the anchor. In the other 3D model, which we have considered, some nodes act as AUV, which consist of underwater sensors.
3.0 Vector Based Forwarding The VBF as proposed by Xie et al. [8] is considered as the base routing protocol for robust, scalable, and energy efficient routing in underwater acoustic network [5, 7].
Figure 1: Vector based forwarding model. In Figure 1, we illustrate the vector based forwarding protocol model. The sensor nodes are distributed in 3D. The nodes are equipped with devices that enable them to measure the distance and signal’s angle of arrival. Node S0 is considered as the source and node S1 is the sink. When S0 wants to send data packets to sink S1 , it −−−→ first establishes a routing vector (S0 S1 ) as shown in the figure. W is the threshold distance from the routing vector, which makes a cylinder pipe with central axis S1 S0 and radius W . Node S0 broadcasts the packet with S1 as target. Upon receiving the packet, the nodes calculate their corresponding distance from the routing vector. If the node is within the range W from the routing vector, then the packet is forwarded to the next node, otherwise the packet is discarded. In VBF, participating nodes are mainly those which are within the routing pipe depicted in Figure 1. However when the sensor nodes are densely deployed, VBF may involve too many nodes in data forwarding thus increasing energy consumption. Effective node selection in VBF may be achieved by considering the self adaptation algorithm proposed by Xie in [7]. The most desirable nodes are selected as forwarders based on the value of Tadaptation given by Tadaptation =
√
α × Tdelay +
R−d v0
(1)
where α is the desirableness factor, R is the transmission range, d is the distance between the selected forwarder node and the next forwarder node, and v0 is the propagation speed of acoustic signals in water. The purpose of the delay in (1) is to distinguish the importance of the nodes in the transmission range of a forwarder. In VBF, Tdelay is set large enough. VBF has been proposed as an efficient routing protocol for aqueous medium [8] and it has been used for protocols efficiency comparison [5, 7]. To the best of our knowledge, no study has been done to investigate the performance of VBF in shallow and deep waters. Also, as far as we are aware, VBF has been considered with an average speed of 1500 ms−1 . In this paper we consider varying propagation speed based on the assumption that it will provide better accuracy [2]. The VBF routing protocol is considered with varying propagation speed in order to judge its performance in shallow and deep water. We consider the work of Coppens [3], where the ocean speed c is considered with varying pressure (depth), salinity and temperature according to the following equation: c = 1449.05 + 45.7t − 5.21t2 + 0.23t3 + (1.333 − 0.126t + 0.009t2 )(s − 35) + (16.23 + 0.253t)z + (0.213 − 0.1t)z 2 + (0.016 + 0.0002(s − 35))(s − 35)tz, ◦
(2)
where z is the depth in meters, s is the salinity in parts per thousand and t = T /10 C. We note that (2) is valid for 0◦ C ≤ T ≤ 35◦ C, 0 ≤ S ≤ 45 parts per thousand, and 0 ≤ z ≤ 4000 m where T is given by 2.0, z > 4500; 2.0 + 0.00057142(4500 − z), 1000 < z < 4500; 4.0 + 0.016(1000 − z), z > 750; T = 8.0 + 0.028(750 − z), z > 250; 22.0, otherwise. For further details about VBF, please refer to [7, 8].
4.0 Simulation The simulations were built using the underwater package Aqua-Sim of ns-2. While supporting 3D deployment, AquaSim allows effective simulation of acoustic signal attenuation and packet collisions in underwater sensor networks. Basic information about the ocean environment and the underwater channel are provided through an Otcl script. Modules uw-common, uw-mac and uw-routing, found in Aqua-Sim are modified to support the simulation. The simulations described in this paper make use of the 3D network architecture with randomly deployed sensor nodes. The VBF routing protocol is used with one data source and one sink. The LinkQuest UWM 2000 [9] is taken as a reference for the sensor nodes with the parameters given in Table 1: Bit Rate Energy consumption for sending mode Energy consumption for receiving mode Energy consumption for idle mode
10 kbps 2W 0.8 mW 0.2 mW
Table 1: Parameters for LinkQuest UWM 2000
The size of the data packet and large control packet for VBF is set to 50 Bytes. The size of the small control packet for VBF is set to 50 Bytes. The pipe radius in VBF is set to 20 m. Performance Metrics We now describe the performance metrics [4], which have been used evaluate the performance of VBF. Propagation delay is taken to be the total time delay in second to send a number of packets from the source to the destination through VBF routing protocol. Signal to Noise Ratio (SNR) is taken as the ratio of the total power transmitted and the total noise in the network to send a number of packets from the source to the destination through VBF.
5.0 Implementation For our simulation, shallow water consists of depth less than 200 m and cylinder spreading. Deep water consists of depth greater or equal to 200 m and spherical spreading. Shallow Water For the case of shallow water, we consider a cube of length 100 m. Using Coppens equation with a temperature of 22◦ C, a salinity of 36.5 parts per thousand and a depth of 10-100 m, a range of 1526.99 to 1528.33 is obtained for the underwater propagation speed. Now, we consider a source node, transmitting 50 packets to a AUV sink. Both the source and the sink are placed at the same level as shown in Figure 2 and they are tested by varying the depth by changing the locations of the source and the sink simultaneously. We increased the depth from 10 m to 100 m, thus causing transmission at different propagation speeds.
Figure 2: Source and sink are placed at the same level. Next, we consider the case where the source is placed at the bottom and the sink is considered as a boat floating at the sea surface as shown in Figure 3.
Figure 3: Source is placed at the bottom and the sink is at the top.
The depth considered is less than 100 m. The source node transmits 50 packets at a varying frequency of 500 Hz to 25000 Hz. SNR is calculated with varying frequency in both shallow and deep water. Within the frequency range 500 Hz − 25000 Hz, shipping noise, caused by water vehicle, is not considered because only a frequency level of below 500 Hz affects the shipping noise. Within the range 500 Hz − 25000 Hz only wind speed is considered as it affects the ambient noise. The wind speed is set to 2ms−1 . We note that in shallow water the spreading attenuation is cylindrical. Deep Water For deep water, we consider the cuboid of dimension 500 × 500 × 1000 m3 . Using the Coppens equation with varying temperature in the range 22.0◦ C − 7.2◦ C), salinity in the range 36.5 − 34.8 parts per thousand and depth in the range 200 − 1000 m, a range of 1531.84 ms−1 to 1482.75 ms−1 is obtained for the underwater propagation speed. Again, we consider the case where the source and the sink are at the same level. We set the transmission range to 100 m. The source is set at (10, 10, z) and the sink at (450, 450, z), where z is varied according to depth, thus making the value of the propagation speed to vary. Next, we consider a source sensor node and a AUV. Both of them are placed in deep sea as shown in Figure 4. In the case of deep water the propagation speed is considered as 1507.83 ms−1 . The frequency varies from 500 Hz to 25000 Hz.
Figure 4: Source and sink are placed deep in the sea.
6.0 Results
In this section, we present the results which we have obtained from the simulations carried out. It follows from Coppens equation that in shallow water when the depth increases from 10 m to 100 m, the propagation speed also increases. Thus, keeping the same routing path and the same distance travel by the 50 packets from the source to the destination, the propagation delay decreases with increase in depth in shallow water as shown in Figure 5.
45.827
45.826
Propagation delay (s)
45.825
45.824
45.823
45.822
45.821
45.82 1528.6
1528.8
1529
1529.2 1529.4 1529.6 Propagation speed (m/s)
1529.8
1530
1530.2
Figure 5: Propagation delay in shallow water. Again from Coppens equation, it is clear that underwater propagation speed decreases with the increase of depth from 200 m to 1000 m. Hence in deep water environment, Figure 6 shows that the propagation delay increases when the depth is increased.
Propagation delay (s)
46.05 46 45.95 45.9 45.85 45.8 1480
1485
1490
1495
1500 1505 1510 1515 Propagation speed (m/s)
1520
1525
1530
1535
Figure 6: Propagation delay in deep water. Attenuation is the decrease of the signal strength. It depends on the distance and the spreading factor. Figure 7 shows the attenuation loss using VBF routing in deep and shallow water. The routing path distance and the spreading factor have been used to obtain the attenuation. The figure also shows that with an increase in frequency, the attenuation increases in both shallow and deep water. But the attenuation loss in deep water is much higher than in shallow water by about 27 dB. Shallow water
Deep water
18.25
36.05
18.2
36
18.15
35.95
18.1 dB Re µ pa
dB Re µ pa
35.9 18.05
18
35.85
35.8 17.95 35.75
17.9
35.7
17.85
17.8
0
5
10 15 Frequency (kHz)
20
25
35.65
0
5
10 15 Frequency (kHz)
20
Figure 7: Attenuation in shallow and deep water.
25
Ambient noise also known as background noise is the loss due to its environment. In Figure 8, we show the ambient noise level in deep and shallow water. 60 Shallow water Deep water dB Re µ pa
50
40
30
20
0
5
10
15 Frequency (kHz)
20
25
Figure 8: Ambient noise in shallow and deep water. Total attenuation is the combined loss of the ambient noise and the attenuation due to path loss. Despite the increase of the path loss with the increase of frequency, the total attenuation of the signal decreases with the increase of frequency in both shallow and deep water as shown in Figure 9. However total attenuation in deep water is much higher than in shallow water when shipping noise is ignore and wind speed is 2 ms−1 . 85 Shallow water Deep water
80
dB Re µ pa
75 70 65 60 55 50
0
5
10
15 Frequency (kHz)
20
25
Figure 9: Total attenuation in shallow and deep water. From Figure 10, we observe that SNR in shallow water is much higher than in deep water. This is due to the higher attenuation loss in deep water than in shallow water. With an increase in frequency, the SNR also increases in both shallow and deep water. 1.8 Shallow water Deep water
1.7 1.6 1.5 1.4 1.3 1.2 1.1 1
0
5
10
15 Frequency (kHz)
20
25
Figure 10: Signal to noise ratio is shallow and deep water.
7.0 Conclusion Based on our results, we conclude that although ambient noise in shallow water is higher than in deep water, the vector based forwarding routing protocol performs better in shallow water than in deep water. This is due to the attenuation of the signal which is much higher in deep water than in shallow water. Also pressure is higher in deep water than shallow water and this causes a rapid decrease in signal strength in deep water as compared to shallow water.
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