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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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An Information-Centric Semantic Data Collection Tree for Wireless Sensor Networks.

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  • 1School of Electronic Engineering, Soongsil University, Room 1104, Huyngam Engineering Building 424, Sangdo-dong, Dongjak-Gu, Seoul 06978, Korea.

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|November 3, 2020
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Summary
This summary is machine-generated.

This study introduces a semantic data collection tree (sDCT) for wireless sensor networks (WSNs) and the Internet of Things (IoT). The sDCT improves energy efficiency and reduces packet transmissions for data collection and queries compared to existing IP-based methods.

Keywords:
IoT service function chainsedge computingfog computinghigh availabilityinternet of thingsnetwork function virtualization

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Area of Science:

  • Computer Science
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) rely on efficient data collection.
  • Current IP-based routing and addressing in WSNs create management overhead and are inefficient for information-centric tasks.
  • Semantic data collection and queries are hindered by traditional network architectures.

Purpose of the Study:

  • To propose a novel semantic data collection tree (sDCT) construction scheme for WSNs.
  • To enhance the efficiency of data collection, queries, and configurations in WSNs.
  • To address the limitations of IP-based approaches in semantic data handling.

Main Methods:

  • Developed a semantic data collection tree (sDCT) rooted at the edge/sink.
  • Implemented the sDCT within the Contiki operating system.
  • Evaluated performance using telosb sensors in various network scenarios.

Main Results:

  • The sDCT demonstrated significant improvements in energy efficiency.
  • Reduced packet transmissions were observed for data collection and query tasks.
  • Outperformed state-of-the-art schemes like 6LoWPAN/RPL and L2RMR.

Conclusions:

  • The proposed sDCT scheme offers a more efficient approach for data collection in WSNs and IoT.
  • sDCT provides substantial benefits in energy conservation and network communication.
  • This method is particularly advantageous for semantic data handling and querying.