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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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A Matching Game-Based Data Collection Algorithm with Mobile Collectors.

Chun Zhang1, Shumin Fei2

  • 1School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

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|March 8, 2020
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Summary
This summary is machine-generated.

This study introduces a matching game algorithm to reduce data delay in wireless sensor networks. The new approach effectively pairs mobile collectors with cluster heads for efficient data collection.

Keywords:
data collectiondata delaymatching gamemobile collectorswireless sensor networks

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Area of Science:

  • Computer Science
  • Network Engineering
  • Algorithm Design

Background:

  • Wireless sensor networks (WSNs) face challenges with limited battery resources, necessitating efficient data collection strategies.
  • Mobile collectors are employed to overcome multi-hop data relay limitations, but minimizing data delay remains a key issue.
  • Cooperative strategies among mobile collectors are crucial for enhancing data collection efficiency in WSNs.

Purpose of the Study:

  • To propose a novel matching game-based algorithm for optimizing data collection in wireless sensor networks.
  • To address the challenge of reducing data delay through the strategic cooperation of mobile collectors.
  • To ensure stable, optimal, and unique matching results for efficient data packet retrieval.

Main Methods:

  • Election of high-level cluster heads within the wireless sensor network.
  • Modeling the data collection problem as a one-to-one matching problem using a matching game theory approach.
  • Establishing benefit matrices based on the preferences of mobile collectors and cluster heads.
  • Implementing a matching algorithm for mobile collectors to select cluster heads for data packet collection.

Main Results:

  • The proposed matching algorithm yields stable, optimal, and unique matching outcomes.
  • Simulation results demonstrate a significant reduction in data delay compared to existing methods.
  • The cooperative strategy effectively leverages mobile collectors for improved network performance.

Conclusions:

  • The matching game-based algorithm offers a superior solution for data collection in WSNs, particularly in reducing data delay.
  • The study validates the effectiveness of game theory in optimizing resource-constrained wireless networks.
  • The proposed method provides a robust framework for enhancing the efficiency of mobile data collection in sensor networks.