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

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

693
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
693
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

712
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...
712
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

158
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.
158
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.6K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.6K
Mesh Analysis01:20

Mesh Analysis

827
Mesh analysis is a valuable method for simplifying circuit analysis using mesh currents as key circuit variables. Unlike nodal analysis, which focuses on determining unknown voltages, mesh analysis applies Kirchhoff's voltage law (KVL) to find unknown currents within a circuit. This method is particularly convenient in reducing the number of simultaneous equations that need to be solved.
A fundamental concept in mesh analysis is the definition of meshes and mesh currents. A mesh is a closed...
827
Ampere's Law: Problem-Solving01:31

Ampere's Law: Problem-Solving

3.7K
Ampere's law states that for any closed looped path, the line integral of the magnetic field along the path equals the vacuum permeability times the current enclosed in the loop. If the fingers of the right hand curl along the direction of the integration path, the current in the direction of the thumb is considered positive. The current opposite to the thumb direction is considered negative.
Specific steps need to be considered while calculating the symmetric magnetic field distribution...
3.7K

You might also read

Related Articles

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

Sort by
Same author

Direct-to-Satellite IoT Slotted Aloha Systems with Multiple Satellites and Unequal Erasure Probabilities.

Sensors (Basel, Switzerland)·2021
Same author

UAV Path Optimization for Precision Agriculture Wireless Sensor Networks.

Sensors (Basel, Switzerland)·2020
Same author

ICENET: An Information Centric Protocol for Big Data Wireless Sensor Networks.

Sensors (Basel, Switzerland)·2019
Same author

SDN Architecture for 6LoWPAN Wireless Sensor Networks.

Sensors (Basel, Switzerland)·2018
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: Aug 17, 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

634

Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning.

Marcos Alberto Mochinski1,2, Marina Luísa de Souza Carrasco Vieira1,2, Mauricio Biczkowski3

  • 1Centro de P&I em Sistemas Elétricos Inteligentes (CISEI) - Smart Grid Research Center, Escola Politécnica, Pontifícia Universidade Católica do Paraná (PUCPR), Curitiba 80215-901, Brazil.

Sensors (Basel, Switzerland)
|December 11, 2022
PubMed
Summary
This summary is machine-generated.

We developed AIDA, an AI-driven method for smart grid network planning. It efficiently positions communication devices, ensuring high connectivity with fewer routers and gateways, even in complex terrains.

Keywords:
AMI network planningkey device positioningsmart grid communication network

More Related Videos

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.1K
Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band
06:43

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band

Published on: May 2, 2018

7.1K

Related Experiment Videos

Last Updated: Aug 17, 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

634
Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.1K
Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band
06:43

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band

Published on: May 2, 2018

7.1K

Area of Science:

  • Smart Grid Communications
  • Network Planning and Optimization
  • Heuristic Algorithms

Background:

  • Positioning key devices (routers, gateways) in smart grids is NP-Hard due to exponential topology growth.
  • Terrain profiles significantly impact communication losses between smart meters and devices.
  • Existing methods often lack consideration for real-world large-scale scenarios and detailed topography.

Purpose of the Study:

  • To introduce an efficient heuristic method, AIDA (AI-driven AMI network planning with DA-based information and a link-specific propagation model), for smart grid communication network planning.
  • To balance complexity and efficiency in device placement, eliminating the need for empirical terrain characterization.
  • To propose a method for positioning communication devices that optimizes network coverage versus the number of devices.

Main Methods:

  • Utilized a Minimum Spanning Tree approach for simplified multihop analysis based on link-received power.
  • Employed a link-specific propagation model independent of empirical terrain classification.
  • Integrated distribution automation (DA) device information into the planning process.

Main Results:

  • AIDA demonstrated efficient planning for large-scale Advanced Metering Infrastructure (AMI) networks.
  • The method achieved high-quality connectivity with a reduced number of communication devices across four real-world scenarios (over 230,000 smart meters).
  • AIDA's terrain-based model required fewer router positions compared to the Erceg-SUI Path Loss model for equivalent smart meter coverage.

Conclusions:

  • AIDA offers an efficient and scalable solution for smart grid communication network planning.
  • The heuristic approach effectively reduces complexity and optimizes device placement in large-scale deployments.
  • AIDA provides a practical method for achieving robust smart grid connectivity with minimized infrastructure costs.