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- 1. Optimal Node Placement in Underwater Wireless Network Muhamad Felemban, BasemShihada, and KamranJamshaid Department of Computer Science, CEMSE Division, KAUST, Saudi Arabia 1
- 2. Presentation Outline Introduction and Motivation Objective Network Model Underwater Communication Problem Formulation Results Simulation Setup and Results Conclusion 2
- 3. 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
- 4. 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
- 5. Paper’s Objective 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 5
- 6. 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 r SG Ocean is divided horizontally into regions RN based on the depth Propagation characteristic is different in each region 6
- 7. Network Model 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 7
- 8. 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
- 9. Underwater Communication Absorption coefficient α Increases with frequency Decreases with depth 9
- 10. Problem Formulation Problem P given: k,f, d, T, Rmax, V, and N Minimize Subject to 10
- 11. Results Transmission loss of deep water at 10000 m depth 11
- 12. 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
- 13. Results Maximum transmission range at different depths with Ptx= 100 13 W
- 14. Results Maximum transmission range with different transmission power at depth of 10000 m 14
- 15. Results Maximum transmission range with different BER at depth of 15 10000 m
- 16. Results Maximum transmission range with different modulation schemes at depth of 10000 m 16
- 17. 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
- 18. Simulation Results Maximum transmission range to maintain cut-off threshold of 19.47 at depthof 7500 m 18
- 19. 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
- 20. References [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. 20
- 21. Conclusions Questions and Discussion 21
Hinweis der Redaktion
- My presentation outline is as follows. I will start with the importance of UWSN applications and its challenges and limitations. Then will show some related work of underwater node placement. Then I will present the problem objective and formulation, followed by discussion about the observed results from the analytical and simultion experiments At the end I will conclude my presentation with some considered future work
- 70% of planet earth is covered by water.High percentage is still unexplored. No cheap and efficient way to conduct underwater operations. AUVs are helpful, but difficult to control in deep water.AUVs are used in scientific tasks like habitat monitoring, data samplingOne of greatest use of AUV’s is the call of MBARI during the oil spill in Mexican Gulf 2010. It dove up to 1,500 m and collected water samples near the oil spill. It provided the researchers with better understanding of the effects on the surrounding enivrnoment
- The disadvantages of using AUVs underwater is that sampled data can be only retrieved when the AUV is back to surface. Some applications need real-time data monitoring and sampling. Another disadvantage is the limited storage. Offline configurationChallenges ExpensiveRequired high powerLimited hardware capability Difficult to deploy
- Transmission loss is caused by two phenomena: 1- energy spreading: as the wave propagates for longer distances it occupies larger surface area. as the surface area increases the energy per unit surface area becomes less and hence low received signal .. Geometric spreading are: spherical and cylidrical.. Modeled by k values of 1 and 2. 1.5 is the practical value2- waves absorptionis frequency dependent. High frequency signals are more vulnerable to loss because of energy transfer to energy. Transmission loss, mainly depends no the distance، operating frequency, and absorption coefficient …. Different models for absorption, the most basic depends only on the frequency. While a more complicated depends on the temperature, salinity, acidity and the depth.
- Ainslie and McColm model is and accurate model that consider temperature, salinity and acidity. G1 represent the abosrptoptin caused by the boric acid, g2 from the magnisuemsulphate. Default values for salinity and acidity is 35 and 8 Figure shows the effect of the frequency and depth
- Given spreading factor, operating frequency, depth, Rmax, volume and number of nodes, we can find out the optimal tranmission range that minimizes TLConstraints are it has to be within the hardware capability of the node operating frequency in the valid range for the absorption model V is greater than certain threshold to assure that rc is not going to zero
- Logarithmic behavior in the transmission loss. Rapidly increase with distance and frequency. Decreases as depth increase
- One observation worth to mention is the optimal frequency because of the noise model behavior. For deep water, frequencues around 100 KHz has less noise than any other and still hold TLth. For shallow is 40 KHz. We found the affect of changing power, BER values, modulation scheme and depth on the optimal frequency
- As depth increases, higher frequency can be used to maintain same TLth
- Increasing the power allow singals to propgate further with same frequencies
- Low BER values have more strict ranges and fs to maintain TLth
- We used NS3. It’s a free available network simulator equipped with many models for all kinds of networks UAN framework is available, but buggy and has very limited functionalities. We modified the framework to better match with our assumptions. Changes: propogation modelPhy chars MAC protocol Sending UDP packets underwater
- Transmission ranges in the simulation approximatly matches the obtained ones from the mathematical model
- In the future, we are aiming to enhance the problem formulation to include channel capacity. In such that the solution provides the best distance and operating frequency to achieve network reliability and high throughput in terms of capacity and propagation delay