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Related Experiment Video

Updated: Feb 6, 2026

Construction of a Wireless-Enabled Endoscopically Implantable Sensor for pH Monitoring with Zero-Bias Schottky Diode-based Receiver
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Energy-Aware Control of Data Compression and Sensing Rate for Wireless Rechargeable Sensor Networks.

Ikjune Yoon1, Dong Kun Noh2

  • 1Department of Smart Systems Software, Soongsil University, Seoul 06978, Korea. ijyoon@ssu.ac.kr.

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

This study introduces an energy-aware scheme for wireless rechargeable sensor networks. It optimizes data compression and sensing rates to maximize data collection at the sink while minimizing node blackout.

Keywords:
compressionrechargeablesensing rate controlwireless sensor networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Wireless Sensor Networks

Background:

  • Wireless rechargeable sensor nodes enhance data analysis precision with harvested energy.
  • Increased data transmission can lead to energy depletion and blackout in nodes near the sink.

Purpose of the Study:

  • To propose an energy-aware control scheme for data compression and sensing rates.
  • To maximize data collected at the sink while minimizing blackout time in wireless rechargeable sensor networks.

Main Methods:

  • Dominant nodes determine data transmission quotas for descendant nodes using an efficient energy allocation scheme.
  • Nodes select data compression algorithms and sensing rates based on quotas and allocated energy to prevent sink-near node energy exhaustion.

Main Results:

  • The proposed scheme demonstrably collects more data compared to existing methods.
  • It effectively suppresses node blackout, particularly for nodes near the sink.
  • The scheme shows adaptability to varying node densities and energy harvesting conditions.

Conclusions:

  • The energy-aware control scheme successfully balances data collection maximization with energy conservation in wireless rechargeable sensor networks.
  • This approach offers improved performance and adaptability in dynamic network environments.