Search research articles

Related Concept Videos

Data Collection by Observations

Data Collection by Observations

Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...

You might also read

Related Articles

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

Sort by

Same author

Balancing Communication and Acceleration: Exact One-to-One Optimization for Distributed Multiagent Learning Systems.

IEEE transactions on cybernetics·2026

Same author

VBCNet: A Hybird Network for Human Activity Recognition.

Sensors (Basel, Switzerland)·2024

Same author

MUSIC: Accelerated Convergence for Distributed Optimization With Inexact and Exact Methods.

IEEE transactions on neural networks and learning systems·2024

Same author

A-GSTCN: An Augmented Graph Structural-Temporal Convolution Network for Medication Recommendation Based on Electronic Health Records.

Bioengineering (Basel, Switzerland)·2023

Same author

Residual Learning Diagnosis Detection: An Advanced Residual Learning Diagnosis Detection System for COVID-19 in Industrial Internet of Things.

IEEE transactions on industrial informatics·2023

Same author

Post-Quantum Security: Opportunities and Challenges.

Sensors (Basel, Switzerland)·2023

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

Search research articles

Related Experiment Video

Updated: Apr 18, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

A structure fidelity approach for big data collection in wireless sensor networks.

Mou Wu¹, Liansheng Tan², Naixue Xiong³

¹Department of Computer Science, Central China Normal University, Wuhan 430079, China. mou.wu@163.com.

Sensors (Basel, Switzerland)

|January 23, 2015

Summary

This study introduces a Structure Fidelity Data Collection (SFDC) framework for wireless sensor networks (WSNs). SFDC reduces energy consumption by intelligently scheduling sensor nodes, extending network lifetime while preserving data integrity.

More Related Videos

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

In Situ Soil Moisture Sensors in Undisturbed Soils

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

Related Experiment Videos

Last Updated: Apr 18, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

In Situ Soil Moisture Sensors in Undisturbed Soils

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

Area of Science:

Wireless Sensor Networks (WSNs)
Data Collection and Monitoring
Energy Efficiency in Networks

Background:

Continuous data collection in WSNs, like temperature and humidity monitoring, is crucial but energy-intensive.
Limited energy supply of sensor nodes necessitates energy reduction strategies for extended network lifetime.
Over-deployment of sensor nodes leads to data redundancy and significant energy waste.

Purpose of the Study:

To develop a Structure Fidelity Data Collection (SFDC) framework for WSNs.
To reduce energy consumption by minimizing the number of active sensor nodes.
To maintain low structural distortion and high data fidelity during continuous sensing.

Main Methods:

Leveraging spatial correlations between sensor nodes to optimize data collection.
Implementing a work/sleep scheduling mechanism for nodes based on structural distortion.
Utilizing an image quality assessment approach to quantify and manage structural distortion.
Reducing the number of active nodes while enabling others to enter low-power sleep mode.

Main Results:

Demonstrated significant energy savings through reduced active sensor node count.
Successfully maintained low structural distortion, ensuring high data fidelity.
Validated the effectiveness of the SFDC framework using both synthetic and real-world datasets.
Extended the operational lifetime of wireless sensor networks.

Conclusions:

The SFDC framework offers an effective approach to energy saving in WSNs.
Maintaining structural similarity is key to preserving data fidelity in continuous sensing applications.
SFDC provides a unique perspective on balancing energy efficiency and data integrity in WSNs.