Jove
Visualize
Contact Us

Related Concept Videos

Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
Microbial Biosensors01:17

Microbial Biosensors

Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...

You might also read

Related Articles

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

Sort by
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
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 Experiment Video

Updated: May 25, 2026

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

Data centric sensor stream reduction for real-time applications in wireless sensor networks.

Andre Luiz Lins Aquino1, Eduardo Freire Nakamura

  • 1Computer Science Department, Federal University of Ouro Preto, Ouro Preto, MG, Brazil.

Sensors (Basel, Switzerland)
|February 4, 2012
PubMed
Summary

This study introduces a data-centric approach for wireless sensor networks to meet real-time deadlines. The strategy optimizes applications and uses sampling algorithms to reduce data without losing representativeness.

Keywords:
data-centric routing algorithmsensor-stream reduction algorithmswireless sensor network

Related Experiment Videos

Last Updated: May 25, 2026

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

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Soft real-time applications in wireless sensor networks face challenges in meeting strict deadlines.
  • Data volume in sensor networks can overwhelm processing capabilities, impacting timeliness.

Purpose of the Study:

  • To present a data-centric strategy for ensuring deadlines in soft real-time wireless sensor network applications.
  • To develop methods for efficient data reduction while maintaining data integrity.

Main Methods:

  • Designed real-time applications for minimal deadline achievement.
  • Developed an analytic model for estimating optimal sample sizes in data reduction.
  • Implemented two stream-based sampling algorithms for data reduction.

Main Results:

  • Simulation results demonstrate the effectiveness of the data-centric strategies.
  • The proposed methods successfully meet application deadlines.
  • Data representativeness was preserved during the data reduction process.

Conclusions:

  • The data-centric strategy is effective for meeting deadlines in soft real-time wireless sensor networks.
  • The developed analytic model and sampling algorithms provide a robust solution for data reduction.
  • This approach ensures timely data delivery without compromising data quality.