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Related Concept Videos

Data Collection II01:29

Data Collection II

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The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
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Data Collection I01:30

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Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
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Data Collection by Experiments01:13

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Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public...
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Data Collection by Survey01:07

Data Collection by Survey

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The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
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Data Collection by Observations01:08

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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...
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Data Collection III01:05

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The physical assessment examines the patient for objective data that defines the patient's condition, and aids in formulating the nursing care plan. The purpose of physical assessment is a health status appraisal, which includes identifying health problems, and establishing a database for nursing intervention.
The principles to begin the physical assessment include conducting a comprehensive or problem-related history in a quiet, well-lit room, emphasizing privacy and comfort for the...
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Related Experiment Video

Updated: Jan 28, 2026

Construction of a Wireless-Enabled Endoscopically Implantable Sensor for pH Monitoring with Zero-Bias Schottky Diode-based Receiver
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Low-Energy Data Collection in Wireless Sensor Networks Based on Matrix Completion.

Yi Xu1, Guiling Sun2, Tianyu Geng3

  • 1College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300071, China. xuyi@mail.nankai.edu.cn.

Sensors (Basel, Switzerland)
|March 1, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for data collection in wireless sensor networks (WSNs) using rotating random sparse sampling and a fast recovery algorithm. The approach significantly reduces energy consumption and improves data reconstruction accuracy, extending network lifespan.

Keywords:
data collectionmatrix completionsparse samplingwireless sensor networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSNs) face challenges in power consumption for data collection.
  • Existing sparse sensing methods improve accuracy at the cost of time and struggle with low sampling ratios.
  • Communication limits in WSNs hinder efficient data gathering.

Purpose of the Study:

  • To develop a novel, energy-efficient data collection method for WSNs.
  • To enhance data reconstruction accuracy and speed using sparse sampling techniques.
  • To prolong the operational lifetime of WSNs through optimized data collection.

Main Methods:

  • Implementation of a Rotating Random Sparse Sampling (RRSS) method.
  • Development of a Fast Singular Value Thresholding (FSVT) algorithm incorporating the Nesterov technique.
  • Utilizing low sampling ratios with time-varying sampling rates for nodes in sleep mode.

Main Results:

  • The proposed method significantly reduces energy consumption compared to existing schemes.
  • Achieved superior reconstruction accuracy and data collection rates.
  • Demonstrated extended network lifetime through simulations on real-world WSN datasets.

Conclusions:

  • The novel data collection method effectively balances energy efficiency, accuracy, and speed.
  • The RRSS and FSVT algorithms provide a robust solution for data recovery in WSNs.
  • This approach offers a promising strategy for enhancing WSN performance and longevity.