Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

176
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
176

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

X-Ray Image-Based Real-Time COVID-19 Diagnosis Using Deep Neural Networks (CXR-DNNs).

Journal of imaging·2024
Same author

A Novel System to Increase Yield of Manufacturing Test of an RF Transceiver through Application of Machine Learning.

Sensors (Basel, Switzerland)·2023
Same author

Energy-Efficient Routing Protocol for Selecting Relay Nodes in Underwater Sensor Networks Based on Fuzzy Analytical Hierarchy Process.

Sensors (Basel, Switzerland)·2022
Same author

Coded-GFDM for Reliable Communication in Underwater Acoustic Channels.

Sensors (Basel, Switzerland)·2022
Same author

Underwater Vehicle Positioning by Correntropy-Based Fuzzy Multi-Sensor Fusion.

Sensors (Basel, Switzerland)·2021
Same author

Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter.

Sensors (Basel, Switzerland)·2021
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Oct 30, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

877

Energy-Efficient Packet Forwarding Scheme Based on Fuzzy Decision-Making in Underwater Sensor Networks.

Jitander Kumar Pabani1,2, Miguel-Ángel Luque-Nieto1, Waheeduddin Hyder3

  • 1Institute of Oceanic Engineering Research, University of Malaga, 29010 Málaga, Spain.

Sensors (Basel, Switzerland)
|July 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a fuzzy logic protocol to improve energy efficiency in Underwater Wireless Sensor Networks (UWSNs). The new method optimizes data routing, reducing energy loss and enhancing overall system performance.

Keywords:
fuzzy logicpacket forwarding probabilityunderwater sensor networks

More Related Videos

A Silicon-tipped Fiber-optic Sensing Platform with High Resolution and Fast Response
09:03

A Silicon-tipped Fiber-optic Sensing Platform with High Resolution and Fast Response

Published on: January 7, 2019

7.4K
Wireless Electrophysiological Recording of Neurons by Movable Tetrodes in Freely Swimming Fish
10:14

Wireless Electrophysiological Recording of Neurons by Movable Tetrodes in Freely Swimming Fish

Published on: November 26, 2019

9.0K

Related Experiment Videos

Last Updated: Oct 30, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

877
A Silicon-tipped Fiber-optic Sensing Platform with High Resolution and Fast Response
09:03

A Silicon-tipped Fiber-optic Sensing Platform with High Resolution and Fast Response

Published on: January 7, 2019

7.4K
Wireless Electrophysiological Recording of Neurons by Movable Tetrodes in Freely Swimming Fish
10:14

Wireless Electrophysiological Recording of Neurons by Movable Tetrodes in Freely Swimming Fish

Published on: November 26, 2019

9.0K

Area of Science:

  • Marine engineering
  • Computer science
  • Network protocols

Background:

  • Underwater Wireless Sensor Networks (UWSNs) face significant challenges, particularly in energy consumption and efficient data routing.
  • Existing protocols often suffer from node battery drainage, packet loss, and suboptimal node selection for data forwarding, impacting system performance.

Purpose of the Study:

  • To propose an energy-efficient packet forwarding scheme for UWSNs using fuzzy logic.
  • To address critical issues of energy loss and inefficient routing in underwater sensor networks.

Main Methods:

  • A novel fuzzy logic-based protocol is developed, utilizing metrics such as hop count, neighbor count, and Received Signal Strength Indicator (RSSI) in a 3D architecture.
  • The protocol's performance is evaluated under varying transmission ranges (adaptive and non-adaptive) and scalable numbers of nodes.

Main Results:

  • The proposed fuzzy logic protocol demonstrates superior performance compared to existing techniques in terms of energy consumption and hop count.
  • Simulation results validate the effectiveness of the energy-efficient packet forwarding scheme.

Conclusions:

  • The fuzzy logic-based approach offers a significant improvement in energy efficiency for UWSNs.
  • This protocol effectively mitigates common issues like node shutdown and packet loss, enhancing overall network performance and reliability.