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

Maximum Power Transfer01:16

Maximum Power Transfer

1.1K
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...
1.1K
Maximum Size of Aggregate01:12

Maximum Size of Aggregate

1.0K
The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
1.0K
Energy Conservation and Bernoulli's Equation01:16

Energy Conservation and Bernoulli's Equation

11.1K
Applying the conservation of energy principle or the work-energy theorem to an incompressible, inviscid fluid in laminar, steady, irrotational flow leads to Bernoulli's equation. It states that the sum of the fluid pressure, potential, and kinetic energy per unit volume is constant along a streamline.
All the terms in the equation have the dimension of energy per unit volume. The kinetic energy per unit volume is called the kinetic energy density, and the potential energy per unit volume is...
11.1K
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.2K
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...
1.2K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

3.1K
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
3.1K
Column Efficiency: Rate Theory01:12

Column Efficiency: Rate Theory

1.2K
The rate theory of chromatography provides quantitative insight into the shapes and widths of elution bands. These bands are based on the random-walk mechanism governing molecular migration within a column. The Gaussian profile of chromatographic bands arises from the cumulative effect of random molecular motions as they progress through the column.
During elution, a solute molecule experiences numerous transitions between stationary and mobile phases, exhibiting irregular residence times in...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Digital Twin Meets Knowledge Graph for Intelligent Manufacturing Processes.

Sensors (Basel, Switzerland)·2024
Same author

Data Fusion of Observability Signals for Assisting Orchestration of Distributed Applications.

Sensors (Basel, Switzerland)·2022
Same author

Design, Development, and Evaluation of 5G-Enabled Vehicular Services: The 5G-HEART Perspective.

Sensors (Basel, Switzerland)·2022
Same author

ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge.

Sensors (Basel, Switzerland)·2022
Same author

Smart Cities of the Future as Cyber Physical Systems: Challenges and Enabling Technologies.

Sensors (Basel, Switzerland)·2021
Same author

Data Offloading in UAV-Assisted Multi-Access Edge Computing Systems: A Resource-Based Pricing and User Risk-Awareness Approach.

Sensors (Basel, Switzerland)·2020

Related Experiment Video

Updated: Apr 5, 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

1.2K

On the Optimization of a Probabilistic Data Aggregation Framework for Energy Efficiency in Wireless Sensor Networks.

Stella Kafetzoglou1, Giorgos Aristomenopoulos2, Symeon Papavassiliou3

  • 1School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), 9 Iroon Polytechniou str., Zografou, 15780 Athens, Greece. skafetzo@netmode.ntua.gr.

Sensors (Basel, Switzerland)
|August 14, 2015
PubMed
Summary

This study optimizes data aggregation in wireless sensor networks (WSNs) for the Internet of Things (IoT). The proposed approach minimizes energy consumption while meeting delay constraints, enhancing smart city applications.

Keywords:
data gathering and aggregationnetwork optimizationsmart citieswireless sensor networks

Related Experiment Videos

Last Updated: Apr 5, 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

1.2K

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • The Internet of Things (IoT) relies on heterogeneous sensors in distributed systems for real-world interactions.
  • Wireless Sensor Networks (WSNs) are crucial for applications like environmental monitoring, healthcare, and smart cities.
  • Data aggregation in WSNs is vital for reducing traffic and conserving energy.

Purpose of the Study:

  • To introduce an optimization approach for probabilistic data aggregation in WSNs.
  • To identify optimal aggregation probability and aggregation period for minimizing energy consumption.
  • To ensure that data aggregation strategies satisfy imposed delay constraints.

Main Methods:

  • Developed an overall optimization approach to enhance existing probabilistic frameworks.
  • Employed primal-dual decomposition to solve the complex optimization problem.
  • Utilized simulations to evaluate the approach's performance.

Main Results:

  • The proposed method effectively minimizes overall energy consumption in WSNs.
  • Optimal aggregation probabilities and periods were identified for energy efficiency.
  • The approach demonstrated operational efficiency across various traffic and topology scenarios.

Conclusions:

  • The optimization approach significantly improves energy conservation in WSNs.
  • It provides a viable solution for efficient data aggregation in IoT environments.
  • The findings support the development of more sustainable and efficient smart city infrastructures.