Performance and Improvement Analysis of the Underwater WSN Using a Diverse Routing Protocol Approach

Sathish, K.; Ravikumar, C. V.; Srinivasulu, Asadi; Rajesh, A.; Oyerinde, Olutayo O.

doi:10.1155/2022/9418392

Cited by 20 publications

(8 citation statements)

References 36 publications

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“…This circumstance occurs during the process of underwater acoustic wave propagation. The authors developed a brand-new underwater acoustic localization technique based on energy (EB) for such conditions [60]. The algorithm's concept may be broken down into two phases.…”

Section: Range Based Algorithmsmentioning

confidence: 99%

Review of Localization and Clustering in USV and AUV for Underwater Wireless Sensor Networks

Sathish

Venkata

Anbazhagan

et al. 2023

Telecom

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Oceanographic data collection, disaster prevention, aided navigation, critical observation sub-missions, contaminant screening, and seaward scanning are just a few of the submissions that use underwater sensor hubs. Unmanned submerged vehicles (USVs) or autonomous acoustic underwater vehicles (AUVs) through sensors would similarly be able to explore unique underwater resources and gather data when utilized in conjunction with integrated screen operations. The most advanced technological method of oceanic observation is wireless information routing beneath the ocean or generally underwater. Water bottoms are typically observed using oceanographic sensors that collect data at certain ocean zones. Most research on UWSNs focuses on physical levels, even though the localization level, such as guiding processes, is a more recent zone. Analyzing the presenting metrics of the current direction conventions for UWSNs is crucial for considering additional enhancements in a procedure employing underwater wireless sensor networks for locating sensors (UWSNs). Due to their severely constrained propagation, radio frequency (RF) transmissions are inappropriate for underwater environments. This makes it difficult to maintain network connectivity and localization. This provided a plan for employing adequate reliability and improved communication and is used to locate the node exactly using a variety of methods. In order to minimize inaccuracies, specific techniques are utilized to calculate the distance to the destination. It has a variety of qualities, such as limited bandwidth, high latency, low energy, and a high error probability. Both nodes enable technical professionals stationed on land to communicate data from the chosen oceanic zones rapidly. This study investigates the significance, uses, network architecture, requirements, and difficulties of undersea sensors.

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Section: Range Based Algorithmsmentioning

confidence: 99%

Review of Localization and Clustering in USV and AUV for Underwater Wireless Sensor Networks

Sathish

Venkata

Anbazhagan

et al. 2023

Telecom

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“…Traditional localization methods usually yield erroneous results due to the intricacy of underwater surroundings. The proposed method takes advantage of the temporal patterns inherent in proximity-based data to significantly improve the precision with which targets can be located in underwater environments [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Enhancing underwater target localization through proximity-driven recurrent neural networks

Kumar,

Chinthaginjala,

et al. 2024

Heliyon

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“…The breadth and depth of research on smart oceans and transparent oceans for ocean development and utilization have been continuously strengthened in recent years. By utilizing the features of broad coverage, fade resistance, and minimal required switching provided by the distributed antenna systems (DAS) [ 1 , 2 ], underwater sensor nodes collect and transmit data through point-to-point and multipoint-to-multipoint communication to construct a distributed underwater sensor network for the effective transmission of information [ 3 – 6 ].…”

Section: Introductionmentioning

confidence: 99%

Spatial diversity processing mechanism based on the distributed underwater acoustic communication system

Zhou,

Zhang,

et al. 2024

PLoS ONE

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To address the problem of unreliable single-link underwater acoustic communication caused by large signal delays and strong multipath effects in shallow water environments, this paper proposes a distributed underwater acoustic diversity communication system (DUA-DCS). DUA-DCS employs a maneuverable distributed cross-medium buoy network to form multiple distributed, non-coherent, and parallel communication links. In the uplink, a receiving diversity processing mechanism of joint decision feedback equalizer embedded phase-locked loop and maximum signal-to-interference ratio combining (DFE-PLL-MSIRC) is proposed to achieve waveform-level diversity combining of underwater signals. A phase-locked loop module is embedded in each branch of the decision feedback equalizer to eliminate the residual frequency and phase errors after Doppler compensation. Meanwhile, the combining coefficients are determined based on the maximum signal-to-interference ratio criterion, taking into account the residual inter-symbol interference after equalization, resulting in efficient and accurate computation. Additionally, the combined decision values are fed back to the feedback filters in each branch to ensure more accurate feedback output. Simulation and lake experiment results demonstrate that, compared to the single-link communication system, DFE-PLL-MSIRC can achieve a diversity gain of more than 5.2 dB and obtain about 3 dB more diversity gain than the comparison algorithm. And the BER of DFE-PLL-MSIRC can be reduced by at least one order of magnitude, which is lower by at least 0.6 order of magnitude compared to the comparison algorithm. In the downlink, a transmitting diversity processing mechanism of complex orthogonal space-time block coding (COSTBC) is proposed. By utilizing a newly designed generalized complex orthogonal transmission matrix, complete transmission diversity can be achieved at the coding rate of 3/4. Compared to the single-link communication system, the system can achieve a diversity gain of more than 6 dB.

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Performance and Improvement Analysis of the Underwater WSN Using a Diverse Routing Protocol Approach

Cited by 20 publications

References 36 publications

Review of Localization and Clustering in USV and AUV for Underwater Wireless Sensor Networks

Review of Localization and Clustering in USV and AUV for Underwater Wireless Sensor Networks

Enhancing underwater target localization through proximity-driven recurrent neural networks

Spatial diversity processing mechanism based on the distributed underwater acoustic communication system

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