Adaptive Triangular Deployment of Underwater Wireless Acoustic Sensor Network considering the Underwater Environment

Kim, Woojoong; Moon, Hyun Soo; Yoon, Young Joong

doi:10.1155/2019/6941907

Cited by 16 publications

(11 citation statements)

References 11 publications

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“…The technique assesses ideal holding time and also utilizes various routing parameters such as localization‐free signal‐to‐noise ratio (LSNR), signal quality index (SQI), and energy cost function (ECF), to tackle flooding, path loss, and broadcast dormancy. Kim et al 10 suggested an adaptive triangular deployment procedure regulating node dissemination depends on the disparity in transmission activities in an underwater atmosphere.…”

Section: Related Workmentioning

confidence: 99%

Dynamic topology control algorithm for node deployment in mobile underwater wireless sensor networks

Choudhary

Goyal

2022

Concurrency and Computation

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Sensors in underwater wireless sensor networks (UWSNs) can drift up to 3 m/s due to ocean currents, marine organisms, or passing vessels. In existing node deployment and localization techniques, node mobility and network disconnections are not taken into account. This article proposes a dynamic topology control algorithm for node deployment (DTCND) in mobile UWSN. This work aims to monitor the node mobility to predict nodes' location for ensuring coverage and connectivity. The sensor nodes are deployed randomly at different depths. The anchor nodes observe the signal quality index, energy drain rate, and node density at every time interval and detect node disconnections based on the variations in these observed metrics. After receiving the beacon messages from the anchor nodes, the courier nodes incline to move toward the target region for satisfying the coverage and connectivity constraints. Simulation results show that the proposed algorithm attains 5% higher connectivity when compared to energy-efficient localization algorithm (EELA) and 9% higher connectivity when compared to adaptive triangular deployment algorithm (ATDA). The residual energy is also higher by 3% and 18% when compared to EELA and ATDA, respectively.The deployment cost and delay of DTCND also decrease when compared to EELA and ATDA, which results into efficient data collection.

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Section: Related Workmentioning

confidence: 99%

Dynamic topology control algorithm for node deployment in mobile underwater wireless sensor networks

Choudhary

Goyal

2022

Concurrency and Computation

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“…Moreover, the VODA increases the network coverage percentage than the other deployment scheme. Woojoong Kim et al 20 developed an adaptive triangular deployment of UASN in underwater environment. Based on the performance of surface model, the distances between the nodes were predicted.…”

Section: Related Workmentioning

confidence: 99%

Sea lion optimization algorithm based node deployment strategy in underwater acoustic sensor network

Gola

Chaurasia²,

Gupta

et al. 2021

Int J Communication

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Summary In the ocean, huge number of sensor nodes (SNs) are located to transfer the information between other nodes using the Underwater Acoustic Sensor Network (UASN) framework. An underwater acoustic communication technique is utilized by this UASN to exchange the information. Because of environmental conditions and adverse channel, the SNs in UASN may have link breakages. Likewise, maximum target coverage rate for SN deployment is considered as another issue. So it is very essential to create a strong communication system in underwater together with the different kind of variations in ocean environment. As a result, the system will perform better data transmission with the severely fluctuating underwater communication conditions. In this paper, a latest optimization algorithm named as Sea Lion Optimization (SLO) procedure is proposed to discover the optimal location for SN in underwater communication. This algorithm optimally places the acoustic SNs based on the maximum connectivity rate by finding the targeted optimal position. The Matlab tool is utilized for implementation purpose, and the different kinds of parameters like connectivity rate, coverage rate, and delay are taken to evaluate the performance of proposed methodology. Moreover, the existing methods like deployment scheme, Connected Dominating set based depth computation Approach (CDA) approach, and distributive approach are taken to contrast the performance of proposed methodology. When compared to the previous algorithms, our proposed methodology achieves 95% connectivity ratio for varying number of acoustic SNs.

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“…Most terrestrial WSNs adopt two-dimensional (2D) sensor deployment models, and little attention has been paid to threedimensional (3D) sensor deployment models. In recent years, there has been an increase in the WSN application in 3D real-world scenarios due to the development of related technologies and the urgent demands of various industries, such as military monitoring, industrial area monitoring, air pollution monitoring, alpine and underwater monitoring, and emergency response and disaster relief [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Lattice‐Based 3‐Dimensional Wireless Sensor Deployment

et al. 2021

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With the wide application of wireless sensor networks (WSNs) in real space, there are numerous studies on 3D sensor deployments. In this paper, the k -connectivity theoretical model of fixed and random nodes in regular lattice-based deployment was proposed to study the coverage and connectivity of sensor networks with regular lattice in 3D space. The full connectivity range and cost of the deployment with sensor nodes fixed in the centers of four regular lattices were quantitatively analyzed. The optimal single lattice coverage model and the ratio of the communication range to the sensing range r c / r s were investigated when the deployment of random nodes satisfied the k -connectivity requirements for full coverage. In addition, based on the actual sensing model, the coverage, communication link quality, and reliability of different lattice-based deployment models were determined in this study.

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Adaptive Triangular Deployment of Underwater Wireless Acoustic Sensor Network considering the Underwater Environment

Cited by 16 publications

References 11 publications

Dynamic topology control algorithm for node deployment in mobile underwater wireless sensor networks

Dynamic topology control algorithm for node deployment in mobile underwater wireless sensor networks

Sea lion optimization algorithm based node deployment strategy in underwater acoustic sensor network

Lattice‐Based 3‐Dimensional Wireless Sensor Deployment

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