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

Network Covalent Solids02:18

Network Covalent Solids

16.1K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.1K
Hybrid Zones02:29

Hybrid Zones

21.8K
Hybrid zones are narrow regions where two closely related species interact, mate, and produce hybrids. Relative to either parent species, hybrids may possess distinct phenotypic or genetic differences that impact their survival and reproductive success. The genetic variances introduced by hybridization influence species diversity and speciation processes within the hybrid zone.
21.8K
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Protein Networks02:26

Protein Networks

2.8K
2.8K
Hybridization of Atomic Orbitals I03:24

Hybridization of Atomic Orbitals I

66.4K
The mathematical expression known as the wave function, ψ, contains information about each orbital and the wavelike properties of electrons in an isolated atom. When atoms are bound together in a molecule, the wave functions combine to produce new mathematical descriptions that have different shapes. This process of combining the wave functions for atomic orbitals is called hybridization and is mathematically accomplished by the linear combination of atomic orbitals. The new orbitals that...
66.4K
Hybridization of Atomic Orbitals II03:35

Hybridization of Atomic Orbitals II

48.4K
sp3d and sp3d 2 Hybridization
48.4K

You might also read

Related Articles

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

Sort by
Same author

Improvement of Wireless Localization Precision Using Chirp Signals.

Sensors (Basel, Switzerland)·2025
Same author

Ambient Backscattering-Enabled SWIPT Relaying System with a Nonlinear Energy Harvesting Model.

Sensors (Basel, Switzerland)·2020
Same author

Inter-Relay Interference Mitigation for Chirp-Based Two-Path Successive Relaying Protocol.

Sensors (Basel, Switzerland)·2019
Same author

An Efficient RSS Localization for Underwater Wireless Sensor Networks.

Sensors (Basel, Switzerland)·2019
Same author

Signal Detection for Ambient Backscatter Communication with OFDM Carriers.

Sensors (Basel, Switzerland)·2019
Same author

A Distance Boundary with Virtual Nodes for the Weighted Centroid Localization Algorithm.

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: Jan 25, 2026

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

1.1K

An Efficient Hybrid RSS-AoA Localization for 3D Wireless Sensor Networks.

Thu L N Nguyen1, Tuan D Vy2, Yoan Shin3

  • 1School of Electronic Engineering, Soongsil University, Seoul 06978, Korea. thunguyen@ssu.ac.kr.

Sensors (Basel, Switzerland)
|May 10, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new 3D localization method for wireless sensor networks (WSNs) using received signal strength and angle-of-arrival. The hybrid approach improves accuracy and efficiency in challenging environments.

Keywords:
angle-of-arrivalhybrid localizationreceived signal strengthsuboptimalweighted least squares estimatewireless sensor networks

More Related Videos

In Vitro Application of a Wireless Sensor in Flexion-Extension Gap Balance of Unicompartmental Knee Arthroplasty
07:33

In Vitro Application of a Wireless Sensor in Flexion-Extension Gap Balance of Unicompartmental Knee Arthroplasty

Published on: May 5, 2023

1.1K
Construction of a Wireless-Enabled Endoscopically Implantable Sensor for pH Monitoring with Zero-Bias Schottky Diode-based Receiver
08:25

Construction of a Wireless-Enabled Endoscopically Implantable Sensor for pH Monitoring with Zero-Bias Schottky Diode-based Receiver

Published on: August 27, 2021

2.9K

Related Experiment Videos

Last Updated: Jan 25, 2026

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

1.1K
In Vitro Application of a Wireless Sensor in Flexion-Extension Gap Balance of Unicompartmental Knee Arthroplasty
07:33

In Vitro Application of a Wireless Sensor in Flexion-Extension Gap Balance of Unicompartmental Knee Arthroplasty

Published on: May 5, 2023

1.1K
Construction of a Wireless-Enabled Endoscopically Implantable Sensor for pH Monitoring with Zero-Bias Schottky Diode-based Receiver
08:25

Construction of a Wireless-Enabled Endoscopically Implantable Sensor for pH Monitoring with Zero-Bias Schottky Diode-based Receiver

Published on: August 27, 2021

2.9K

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Wireless Communication

Background:

  • Wireless sensor networks (WSNs) are crucial for IoT applications like intelligent control and tracking.
  • Existing localization methods in WSNs often struggle with 2D limitations, high computational demands, and measurement inaccuracies.
  • Practical WSN deployments require robust localization in 3D space with unknown channel parameters.

Purpose of the Study:

  • To develop an accurate and efficient 3D localization scheme for randomly deployed WSNs.
  • To address limitations of conventional localization techniques in real-world scenarios.
  • To jointly estimate node location and unknown channel parameters like transmit power and path loss exponent.

Main Methods:

  • A hybrid approach combining received signal strength (RSS) and angle-of-arrival (AOA) for 3D localization.
  • Development of a weighted least squares (WLS) estimator as an approximation to overcome the complexity of maximum-likelihood estimation.
  • Joint estimation of unknown node location, transmit power, and path loss exponent.

Main Results:

  • The proposed hybrid RSS and AOA method demonstrates effectiveness in 3D WSN localization.
  • The WLS estimator provides a computationally tractable solution for joint parameter estimation.
  • Simulation results validate the accuracy and efficiency of the developed localization scheme.

Conclusions:

  • The hybrid RSS/AOA localization method offers a practical solution for 3D WSNs.
  • The WLS approach effectively handles unknown channel characteristics and complex estimation problems.
  • This work contributes to more reliable and efficient localization in wireless sensor network applications.