Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Two-Dimensional Force System: Problem Solving

Two-Dimensional Force System: Problem Solving

Solving problems related to two-dimensional force systems is an essential aspect of mechanics and engineering. By applying the principles of vector analysis and force equilibrium, one can determine the effect of multiple forces acting on an object in a two-dimensional space.
The first step to solving a two-dimensional force system problem is to draw a free-body diagram of the object under consideration. This diagram helps identify all the external forces acting on the object, including their...

Two-Dimensional Force System

Two-Dimensional Force System

A two-dimensional system in mechanical engineering involves the analysis of motion and forces in a plane. A two-dimensional force vector can be resolved into its components as:

Electrostatic Boundary Conditions

Electrostatic Boundary Conditions

Consider an external electric field propagating through a homogeneous medium. When the electric field crosses the surface boundary of the medium, it undergoes a discontinuity. The electric field can be resolved into normal and tangential components. The amount by which the field changes at any boundary is given by the difference between the field components above and below the surface boundary.
The surface integral of an electric field is given by Gauss's law in integral form and is related to...

State Space Representation

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...

Three-Dimensional Force System:Problem Solving

Three-Dimensional Force System:Problem Solving

A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...

Linear Approximation in Time Domain

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...

You might also read

Related Articles

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

Sort by

Same author

Measurement of internal magnetic flux density distribution in air-core toroidal transformer under high-frequency excitation.

The Review of scientific instruments·2020

Same author

Consensus-based distribution of power packets and decentralized control for routing.

Chaos (Woodbury, N.Y.)·2020

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 7, 2025

Finite Element Modelling of a Cellular Electric Microenvironment

Finite Element Modelling of a Cellular Electric Microenvironment

Published on: May 18, 2021

Two-dimensional swarm formation in time-invariant external potential: Modeling, analysis, and control.

Yanran Wang¹, Takashi Hikihara¹

¹Department of Electrical Engineering, Kyoto University, Kyoto 615-8510, Japan.

Chaos (Woodbury, N.Y.)

|October 2, 2020

Summary

This study introduces a novel energy-efficient protocol for Wireless Sensor Networks (WSNs) by mimicking natural cluster formation. It eliminates data transmission to the Base Station, significantly conserving energy.

More Related Videos

Quantifying Bacterial Surface Swarming Motility on Inducer Gradient Plates

Quantifying Bacterial Surface Swarming Motility on Inducer Gradient Plates

Published on: January 5, 2022

Bioparticle Microarrays for Chemotactic and Molecular Analysis of Human Neutrophil Swarming in vitro

Bioparticle Microarrays for Chemotactic and Molecular Analysis of Human Neutrophil Swarming in vitro

Published on: February 16, 2020

Related Experiment Videos

Last Updated: Dec 7, 2025

Finite Element Modelling of a Cellular Electric Microenvironment

Finite Element Modelling of a Cellular Electric Microenvironment

Published on: May 18, 2021

Quantifying Bacterial Surface Swarming Motility on Inducer Gradient Plates

Quantifying Bacterial Surface Swarming Motility on Inducer Gradient Plates

Published on: January 5, 2022

Bioparticle Microarrays for Chemotactic and Molecular Analysis of Human Neutrophil Swarming in vitro

Bioparticle Microarrays for Chemotactic and Molecular Analysis of Human Neutrophil Swarming in vitro

Published on: February 16, 2020

Area of Science:

Computer Science
Network Engineering
Robotics

Background:

Cluster formation is a natural phenomenon observed across various organisms.
Wireless Sensor Networks (WSNs) require energy-efficient protocols for extended operation.
Existing WSN protocols often incur significant energy costs due to data transmission.

Purpose of the Study:

To develop a new energy-efficient protocol for Wireless Sensor Networks (WSNs).
To investigate sensor cluster formation in response to an external, time-invariant energy potential.
To eliminate the need for data transmission to a Base Station, thereby conserving energy.

Main Methods:

Defining swarm formation topology based on sensor behavior.
Estimating the curvature of an external potential manifold by analyzing swarm formation changes over time.
Introducing a dynamic formation control algorithm to maintain swarm topology within the potential field.

Main Results:

Demonstrated a novel approach to energy conservation in WSNs.
Successfully modeled sensor cluster dynamics under external energy potentials.
Developed a control algorithm for stable swarm formation.

Conclusions:

The proposed method offers a significant advancement in energy efficiency for Wireless Sensor Networks.
Mimicking natural cluster formation provides a viable strategy for WSN protocol design.
Eliminating direct data transmission to a Base Station is key to conserving network energy.