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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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Multi-Source Cooperative Data Collection with a Mobile Sink for the Wireless Sensor Network.

Changcai Han1, Jinsheng Yang2

  • 1School of Microelectronics, Tianjin University, Tianjin 300072, China. cchan@tju.edu.cn.

Sensors (Basel, Switzerland)
|October 31, 2017
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Summary

This study introduces cooperative data collection schemes using distributed low-density parity-check codes for wireless sensor networks. The proposed methods enhance bit error rate performance, with two-round cooperation outperforming one-round schemes.

Keywords:
cooperative communicationsdistributed low-density parity-check codesmobile sink nodesparse cooperationwireless sensor networks

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Area of Science:

  • Wireless Sensor Networks
  • Cooperative Communications
  • Error Correction Coding

Background:

  • Efficient data collection from multiple sensor nodes to a mobile sink in wireless sensor networks (WSNs) is a significant challenge.
  • Existing methods often struggle with network density, channel impairments, and mobile sink trajectories.
  • Distributed low-density parity-check (LDPC) codes offer a promising approach for robust data transmission.

Purpose of the Study:

  • To investigate multi-source cooperation integrating distributed LDPC codes for joint data collection in WSNs.
  • To propose and evaluate one-round and two-round cooperative data collection schemes tailored to mobile sink movement.
  • To analyze the impact of sparse cooperation models and distributed processing on system performance.

Main Methods:

  • Development of two sparse cooperation models based on node locations, channel conditions, and sink trajectories.
  • Design of distributed LDPC codes matched to the derived cooperation models and directed graphs.
  • Simulation of one-round and two-round cooperative data collection schemes.

Main Results:

  • The proposed cooperative data collection schemes demonstrate significant improvements in bit error rate (BER) performance.
  • The two-round cooperation scheme consistently outperforms the one-round scheme.
  • Increased participation of source nodes in sparse cooperation further enhances performance.

Conclusions:

  • The integration of distributed LDPC codes with sparse cooperation models provides an effective solution for mobile data collection in WSNs.
  • The two-round scheme offers superior performance, especially for varying sink trajectories and data sizes.
  • The proposed approach achieves low complexity at each source node due to distributed processing and sparse cooperation.