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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 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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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.
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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.
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A High-Efficiency Data Collection Method Based on Maximum Recharging Benefit in Sensor Networks.

Chao Sha1,2, Qi-Wei Wang3,4, Lu Zhang5

  • 1School of Computer Science, Software and Cyberspace Security, Nanjing University of Posts and Telecommunications, Nanjing 210003, China. shac@njupt.edu.cn.

Sensors (Basel, Switzerland)
|September 12, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a Data Collection Maximum Recharging Benefit (DCMRB) method for Wireless Rechargeable Sensor Networks (WRSNs). DCMRB enhances data collection efficiency and network lifespan by optimizing mobile data collector routes and adaptive recharging strategies.

Keywords:
Maximum Recharging Benefitbalance of energy consumptiondata collectionwireless rechargingwireless sensor networks

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Area of Science:

  • Wireless Sensor Networks
  • Energy Harvesting Technologies
  • Mobile Computing

Background:

  • Wireless Rechargeable Sensor Networks (WRSNs) face challenges in data collection time delays and limited network lifetime.
  • Efficient energy management and data retrieval are critical for the performance of WRSNs.
  • Existing methods like ESAOC, GMS-MRB, and FCFS have limitations in balancing these factors.

Purpose of the Study:

  • To propose a high-efficiency data collection method (DCMRB) for WRSNs.
  • To reduce time delays in data collection and extend the overall network lifetime.
  • To address energy replenishment challenges for sensor nodes with varying consumption rates.

Main Methods:

  • Network partitioning using Virtual Scan Line (VSL) based on the minimum number of Mobile Data Collectors (MDCs).
  • Deployment of MDCs and Wireless Charging Vehicles (WCVs) for data collection and energy replenishment within regions.
  • Implementation of an MDC speed adjustment scheme to ensure data integrity and minimize packet loss.
  • Adaptive recharging threshold calculation to prevent premature node death.
  • Development of an adaptive recharging scheme considering WCV battery capacity and energy consumption.

Main Results:

  • DCMRB effectively balances energy consumption across the network.
  • The proposed method significantly enhances data collection efficiency.
  • Network lifetime is demonstrably prolonged compared to ESAOC, GMS-MRB, and FCFS.
  • Reduced packet loss rates and improved data integrity were observed.

Conclusions:

  • DCMRB offers a superior approach to data collection and energy management in WRSNs.
  • The adaptive strategies for MDC speed and WCV recharging are key to improved performance.
  • This method provides a practical solution for extending the operational duration of WRSNs.