General Characteristics of the Underwater Environment

Björnö, L.; Buckingham, Michael J.

doi:10.1016/b978-0-12-811240-3.00001-1

Cited by 6 publications

(3 citation statements)

References 117 publications

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“…Different parameters influence the function of acoustic channels, such as water depth, temperature, and salinity. Figure 2 [41]] demonstrates the relationship between water depth and acoustic speed using thermoclines. Equation (1) [42] shows the acoustic signal speed:…”

Section: Network Modelmentioning

confidence: 99%

An Effective Data-Collection Scheme with AUV Path Planning in Underwater Wireless Sensor Networks

Khan

Wang

Anwar

et al. 2022

Wireless Communications and Mobile Computing

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Data collection in underwater wireless sensor networks (UWSNs) using autonomous underwater vehicles (AUVs) is a more robust solution than traditional approaches, instead of transmitting data from each node to a destination node. However, the design of delay-aware and energy-efficient path planning for AUVs is one of the most crucial problems in collecting data for UWSNs. To reduce network delay and increase network lifetime, we proposed a novel reliable AUV-based data-collection routing protocol for UWSNs. The proposed protocol employs a route planning mechanism to collect data using AUVs. The sink node directs AUVs for data collection from sensor nodes to reduce energy consumption. First, sensor nodes are organized into clusters for better scalability, and then, these clusters are arranged into groups to assign an AUV to each group. Second, the traveling path for each AUV is crafted based on the Markov decision process (MDP) for the reliable collection of data. The simulation results affirm the effectiveness and efficiency of the proposed technique in terms of throughput, energy efficiency, delay, and reliability.

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Section: Network Modelmentioning

confidence: 99%

An Effective Data-Collection Scheme with AUV Path Planning in Underwater Wireless Sensor Networks

Khan

Wang

Anwar

et al. 2022

Wireless Communications and Mobile Computing

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show abstract

“…But in this scheme of multi-hop, problem of energy consumption cannot be solved in UWSNs, so the scheme of data accumulation which involves AUV is proposed. For 3D UWSN having kenned deployment information, AUV simply needs to build a least probability neighborhood coverage set for data latency [16].…”

Section: Related Workmentioning

confidence: 99%

Underwater Networked Wireless Sensor Data Collection for Computational Intelligence Techniques: Issues, Challenges, and Approaches

et al. 2020

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Underwater wireless sensor networks (UWSNs) is emerging as an advance terminology for monitoring and controlling the underwater aquatic life. This technology determines the undiscovered resources present in the water through computational intelligence (CI) techniques. CI here pertains to the capability of a system to acquire a specific task from data or experimental surveillance below the water. In today's time data is considered as the identity for everything that exists in nature, whether that data is related to human beings, machines or any type of device like internet of underwater things (IoUT). The collected data should be correct, complete and fulfill the requirements of a particular task to be done. Underwater data collection is very tough because of sensors mobility due to water drift 3 meters/sec, crest and trough. A lot of packet drop also exists due to underwater conditions that hurdles the data collection process. Various techniques already exists for efficient collection of data below the water but these are not properly classified. This manuscript has summarized the concept of data collection in UWSN along with its classification based on routing. Also, a short discussion about existence of CORONA below the water along with water purification is carried out. Furthermore, some data routing approaches are also analyzed on the basis of quality of service parameters and the current challenges to be tackled during data collection are also discussed. INDEX TERMS Acoustic sensor network, coronavirus (COVID-19), computational intelligence, routing, underwater sensor network DIVYA ANAND received the Ph.D. degree in computer science and engineering and Masters of Technology in information security from the Lovely Professional University. She has expertise in Teaching, Entrepreneurship and Research and Development. She is currently an Assistant Professor with the Lovely Professional University. She has published over 20 conferences and journal articles. Her research interests include networks security, bioinformatics, machine learning, gene identification, big data analytics and computational models.

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“…The depth of the SOFAR channel (i.e. its depth axis) is geographically dependant: in mid-latitudes depths of 1000-1200 m are common 2,3 ; while in the Arctic summer depths as shallow as 100-200 m are reported 4 . Anthropogenic exploitation of this channel has included locating downed aviators (using a depth sensitive charge) 2 and long distance communication 5 .…”

Section: Introductionmentioning

confidence: 99%

Effects of Mesoscale Eddies on Sound Channels

PERKS,

PARKES,

SIGGERS

2024

International Conference on Underwater Acoustics 2024

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General Characteristics of the Underwater Environment

Cited by 6 publications

References 117 publications

An Effective Data-Collection Scheme with AUV Path Planning in Underwater Wireless Sensor Networks

An Effective Data-Collection Scheme with AUV Path Planning in Underwater Wireless Sensor Networks

Underwater Networked Wireless Sensor Data Collection for Computational Intelligence Techniques: Issues, Challenges, and Approaches

Effects of Mesoscale Eddies on Sound Channels

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