Analyzing the behavior of acoustic link models in underwater wireless sensor networks

Llor, Jesús; Torres, Esteban; Garrido, Pablo; Malumbres, Manuel P.

doi:10.1145/1641913.1641915

Cited by 19 publications

(13 citation statements)

References 7 publications

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“…Although this model is more accurate than the above model, it requires intensive computations. Therefore a large amount of time is required to obtain the propagation loss [22] [30]. That is not feasible for simulation tools and it might not be feasible also to be programmed in actual sensor nodes due to their constrained computational resources.…”

Section: O Monterey-miami Parabolic Equation (Mmpe) Modelmentioning

confidence: 99%

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State-of-the-art of the Physical Layer in Underwater Wireless Sensor Networks

Al-Salti¹,

Alzeidi²

2019

IJWMN

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With the current technology revolution, underwater wireless sensor networks (UWSNs) find several applications such as disaster prevention, water quality monitoring, military surveillance and fish farming. Nevertheless, this kind of networks faces a number of challenges induced by the nature of the underwater environment and its influence on the network physical media. Therefore, the ultimate objective of this paper is to lay down the key aspects of the physical layer of the underwater sensor networks (UWSNs). It discusses issues related to the characteristics and challenges of the underwater communication channel, differences between terrestrial wireless sensor networks and UWSNs, and acoustic propagation models in underwater. The paper also surveys some of the underwater acoustic modems. This study is essential to better understand the challenges of designing UWSNs and alleviate their effects.

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Section: O Monterey-miami Parabolic Equation (Mmpe) Modelmentioning

confidence: 99%

“…o High and variable propagation delay: The speed of an acoustic signal in water ranges from 1450 to 1540 m/s [22]. A typical acoustic signal speed is about 1500 m/s and depends on water properties including temperature, salinity and depth.…”

mentioning

confidence: 99%

State-of-the-art of the Physical Layer in Underwater Wireless Sensor Networks

Al-Salti¹,

Alzeidi²

2019

IJWMN

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“…The high error prone nature of underwater wireless links is another major issue and a challenge in UWSNs [5]. The use of poor link quality in the process of transmitting the data packets leads to increased data packet loss [47,48]. Thus, it requires retransmitting the data packets again.…”

Section: Future Workmentioning

confidence: 99%

A reliable energy-efficient pressure-based routing protocol for underwater wireless sensor network

et al. 2017

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Underwater wireless sensor networks (UWSNs) are similar to the terrestrial sensor networks. Nevertheless, there are different characteristics among them such as low battery power, limited bandwidth and high variable propagation delay. One of the common major problems in UWSNs is determining an efficient and reliable routing between the source node and the destination node. Therefore, researchers tend to design efficient protocols with consideration of the different characteristics of underwater communication. Furthermore, many routing protocols have been proposed and these protocols may be classified as location-based and location-free routing protocols. Pressure-based routing protocols are a subcategory of the location-free routing protocols. This paper focuses on reviewing the pressure-based routing protocols that may further be classified into non-void avoidance protocols and void avoidance protocols. Moreover, non-void avoidance protocols have been classified into single factor based and multi factor based routing protocols. Finally, this paper provides a comparison between these protocols based on their features, performance and simulation parameters and the paper concludes with some future works on which further study can be conducted.

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“…It does physical and data-link layer emulation. Other software emulation works include Nautilus, of Masiero et al [16], a channel model with neighbor set calculation, collisions and propagation delays, of Cnar and Orencik [17], the World Ocean Simulation System (WOSS) library [18], a model developed by King [19] for OMNeT++ [20], and a model for the OPNET environment by Llor et al [21], [22].…”

Section: Related Workmentioning

confidence: 99%

Simulation of underwater communications with a colored noise approximation and mobility

Barbeau

Blouin

Cervera

et al. 2015

2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE)

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Abstract-We study the software simulation of underwater physical communications, namely, underwater acoustic waves and (de)modulation of underwater acoustic digital data signals. We take into account the mobility of nodes, such as in underwater sensor networks. We also consider the integration with protocol layers above the physical layer, i.e., the link and network layers. In this context, mobility is relevant because there are underwater vehicles and environmental conditions causing displacements of sensors. Attenuation is sensitive to transmitter-receiver separation distances. Because of mobility, distances change. Our solution is based on the work of Borrowski (2010). The physical layer is modeled as Matlab functions. As a function of distance and frequency, our physical layer model takes into account attenuation and noise and their effects on a phase-shift keyed signal. We use OMNeT++ to model link and network layer protocols. The Matlab functions and OMNet++ models are linked together. While Matlab does particularly well with signal processing, OMNeT++ addresses better the protocols placed in the link layer and above.

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Analyzing the behavior of acoustic link models in underwater wireless sensor networks

Cited by 19 publications

References 7 publications

State-of-the-art of the Physical Layer in Underwater Wireless Sensor Networks

State-of-the-art of the Physical Layer in Underwater Wireless Sensor Networks

A reliable energy-efficient pressure-based routing protocol for underwater wireless sensor network

Simulation of underwater communications with a colored noise approximation and mobility

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