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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Application of weighted centroid algorithm based on weight correction in node localization of wireless sensor

Wengang Yu1, ChunLing Yu2

  • 1Department of Artificial Intelligence, JiangSu Food & Pharmaceutical Science College, Huai'an, 223001, China. yuwengang2024@163.com.

Scientific Reports
|July 3, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an improved weighted center-of-mass algorithm for wireless sensor networks (WSNs) that enhances node positioning accuracy and reduces energy consumption. The new method effectively minimizes ranging errors, offering a more reliable solution for WSN data monitoring.

Keywords:
Chimpanzee optimization algorithmErrorNodeWeightWeighted centroidWireless sensor network

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Area of Science:

  • Wireless Sensor Networks (WSNs)
  • Localization Algorithms
  • Signal Processing

Background:

  • Node positioning accuracy is critical for WSN data reliability.
  • Traditional center-of-mass algorithms suffer from RSSI errors and poor anchor distribution, leading to inaccuracies.
  • Balancing positioning accuracy and energy efficiency remains a significant challenge in complex WSN environments.

Purpose of the Study:

  • To propose a novel weighted center-of-mass algorithm for WSNs.
  • To improve positioning accuracy while reducing energy consumption.
  • To address limitations of existing RSSI-based localization techniques.

Main Methods:

  • Implemented Gaussian-constant filtering to denoise Received Signal Strength Indicator (RSSI) values.
  • Corrected weight factors by considering anchor node density and communication range.
  • Utilized the chimpanzee optimization algorithm for optimal node localization.

Main Results:

  • The improved algorithm achieved a minimum distance error of less than 1.0 m.
  • Average positioning errors were as low as 0.15 m and 0.14 m, outperforming traditional and other advanced algorithms.
  • Demonstrated over 90% positioning accuracy and at least a 90% reduction in energy consumption compared to other methods.

Conclusions:

  • The proposed weighted center-of-mass algorithm significantly enhances WSN positioning accuracy and energy efficiency.
  • The method effectively mitigates RSSI vulnerabilities and improves upon traditional center-of-mass positioning.
  • Offers a promising low-power, high-accuracy solution for WSN applications.