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

Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

194
The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
194
Maximum Power Transfer01:16

Maximum Power Transfer

468
Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
468
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

200
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
200
Network Function of a Circuit01:25

Network Function of a Circuit

415
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
415
Short-distance Transport of Resources02:12

Short-distance Transport of Resources

16.6K
Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.
16.6K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

808
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
808

You might also read

Related Articles

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

Sort by
Same author

A Comparative Evaluation of Super-Resolution Methods for Spectral Images Using Pretrained RGB Models.

Sensors (Basel, Switzerland)·2026
Same author

Development of a Multispectral Image Database in Visible-Near-Infrared for Demosaicking and Machine Learning Applications.

Journal of imaging·2026
Same author

Wireless Sensor Network Deployment: Architecture, Objectives, and Methodologies.

Sensors (Basel, Switzerland)·2025
Same author

Characterizing the bulk angular distribution of metal flakes in pigmented coating systems leveraging RTI.

Optics express·2025
Same author

Correction: Zossou et al. Radiomics-Based Classification of Tumor and Healthy Liver on Computed Tomography Images. <i>Cancers</i> 2024, <i>16</i>, 1158.

Cancers·2025
Same author

Automatic Segmentation of Plants and Weeds in Wide-Band Multispectral Imaging (WMI).

Journal of imaging·2025
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: Sep 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

675

Area Coverage Maximization under Connectivity Constraint in Wireless Sensor Networks.

Frantz Tossa1,2, Wahabou Abdou3, Keivan Ansari4

  • 1ImViA Laboratory, University of Bourgogne Franche-Comté, 21000 Dijon, France.

Sensors (Basel, Switzerland)
|March 10, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a genetic algorithm for optimizing wireless sensor network (WSN) deployment. The GAFACM algorithm maximizes area coverage and ensures sensor node connectivity for efficient data collection.

Keywords:
area coverageconnectivitygenetic algorithmsensors deploymentwireless sensor networks

More Related Videos

In Situ Soil Moisture Sensors in Undisturbed Soils
08:20

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

6.7K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.4K

Related Experiment Videos

Last Updated: Sep 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

675
In Situ Soil Moisture Sensors in Undisturbed Soils
08:20

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

6.7K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.4K

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless sensor networks (WSNs) are crucial for data collection from regions of interest (ROI).
  • Ensuring maximum area coverage and node connectivity is a primary challenge in WSN design.
  • Effective sensor deployment is vital for reliable data transmission to base stations.

Purpose of the Study:

  • To address the dual problem of maximizing coverage and connectivity in WSNs.
  • To develop an algorithm for optimal sensor node placement in any defined area.
  • To ensure reliable data collection through guaranteed network connectivity.

Main Methods:

  • Utilized a genetic algorithm (GAFACM) for sensor node deployment.
  • Formulated a mathematical model and objective function for coverage and connectivity.
  • Tested the algorithm on various area shapes (regular and irregular).

Main Results:

  • GAFACM effectively maximizes area coverage for a given number of sensors.
  • The algorithm identifies optimal sensor positions for enhanced coverage.
  • Guaranteed connectivity between sensor nodes was achieved.

Conclusions:

  • The GAFACM algorithm provides an effective solution for WSN deployment challenges.
  • It successfully balances maximum area coverage with essential network connectivity.
  • This approach is applicable to diverse regions of interest and sensor network configurations.