Modeling the Underwater Acoustic Channel in ns2

Harris, Albert F.; Zorzi, Michele

doi:10.4108/nstools.2007.2024

Cited by 93 publications

(70 citation statements)

References 12 publications

(21 reference statements)

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“…An experiment similar to the one run by Harris et al [15] was used to determine the accuracy of propagation delay calculated by the simulator. The depth of two AUVs situated 1 km apart was progressively increased, while maintaining a constant relative depth for both AUVs.…”

Section: Simulation Resultsmentioning

confidence: 99%

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A multi-AUV missions simulation framework for the USARSim Robotics Simulator

Sehgal¹,

Cernea²

2010

18th Mediterranean Conference on Control and Automation, MED'10

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Abstract-The maximum potential for underwater exploration rests within the use of multiple Autonomous Underwater Vehicles (AUVs) and tasks involving human diver-AUV coordination. Such tasks require dependable underwater wireless communications, which normally utilize the rapidly varying acoustic channel. Rigorous testing of underwater acoustic communication systems is necessary to develop a dependable network, however, the high offshore testing costs make this difficult. Though simulators could aid the development of such AUV communication systems, the few existing simulators focus upon simulating a single vehicle and, as such, do not provide tools for simulating underwater communication systems. In this paper we present an underwater communications simulation framework designed for the Unified System for Automation and Robotics Simulator (USARSim). This simulation tool is capable of modeling networked communications between, or with, AUVs by accurately characterizing the underwater acoustic channel. Details on this simulation framework are provided along with some results obtained during development of this tool.

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Section: Simulation Resultsmentioning

confidence: 99%

“…The work described in [6], [15] establishes the relationship between capacity and distance, while the authors of [12] show the dependence of capacity on depth and temperature as well.…”

Section: Channel Capacitymentioning

confidence: 99%

A multi-AUV missions simulation framework for the USARSim Robotics Simulator

Sehgal¹,

Cernea²

2010

18th Mediterranean Conference on Control and Automation, MED'10

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“…It is well known that SNR of an emitted underwater signal at the receiver is given by (Yang and B. Liu, 2009;Harris and M. Zorzi, 2007;Brekhovskikh and Lysanov, L.1982) (, ) ( )…”

Section: Signal To Noise Ratiomentioning

confidence: 99%

“…The following formulas give the power spectral density of the four noise components (Yang and Liu, 2009;Liu, 2010;Harris and Zorzi, 2007;Webb, 1992):…”

Section: Noisementioning

confidence: 99%

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Energy Efficient Communication for Underwater Wireless Sensors Networks

Babiker¹,

Zakaria²

2012

Energy Efficiency in Communications and Networks

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Performance analysis of distributed underwater wireless acoustic sensor networks systems in the presence of internal solitons

Mandal

Misra

Dash

et al. 2014

Int J Communication

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Summary In this paper, we have analyzed the performance of distributed Underwater Wireless Acoustic Sensor Networks (UWASNs) in the presence of internal solitons in the ocean. Internal waves commonly occur in a layered oceanic environment having differential medium density. So, in a layered shallow oceanic region, the inclusion of the effect of internal solitons on the performance of the network is important. Based on various observations, it is proved that nonlinear internal waves, that is, solitons are one of the major scatterers of underwater sound. If sensor nodes are deployed in such type of environment, internode communication is affected because of the interaction of wireless acoustic signal with these solitons, as a result of which network performance is greatly affected. We have evaluated the performance of UWASNs in the 3‐D deployment scenario of nodes, in which source nodes are deployed in the ocean floor. In this paper, four performance metrics, namely, Signal‐to‐interference‐plus‐noise‐ratio (SINR), bit error rate (BER), Delay (DELAY), and energy consumption are introduced to assess the performance of UWASNs. Simulation studies show that in the presence of internal solitons, SINR decreases by approximately 10%, BER increases by 17%, delay increases by 0.24%, and energy consumption per node increases by 53.05%, approximately. Copyright © 2014 John Wiley & Sons, Ltd.

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Modeling the Underwater Acoustic Channel in ns2

Cited by 93 publications

References 12 publications

A multi-AUV missions simulation framework for the USARSim Robotics Simulator

A multi-AUV missions simulation framework for the USARSim Robotics Simulator

Energy Efficient Communication for Underwater Wireless Sensors Networks

Performance analysis of distributed underwater wireless acoustic sensor networks systems in the presence of internal solitons

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