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Related Experiment Video

Updated: Jan 16, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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An Environment-Adaptive Multi-Channel Ranging Optimization Algorithm Based on a Multi-Objective Evolutionary Model

Xuming Fang1, Zuqin Ji1

  • 1School of Network Security, Jinling Institute of Technology, Nanjing 211100, China.

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|September 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive algorithm for wireless sensor networks (WSNs) to improve ranging accuracy in complex indoor settings. The new method optimizes multi-channel Received Signal Strength Indicator (RSSI) data, overcoming limitations of existing algorithms.

Keywords:
high-precision positioningmulti-channel RSSImulti-objective evolutionwireless sensor networks

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Area of Science:

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

Background:

  • Received Signal Strength Indicator (RSSI) is widely used for WSN ranging due to ease of acquisition and scalability.
  • Traditional RSSI-based WSN algorithms struggle with accuracy in complex indoor environments due to noise and multipath effects.
  • Existing multi-channel ranging algorithms require precise initial parameters or target-reference distances, risking local optima.

Purpose of the Study:

  • To develop an environment-adaptive algorithm for optimizing multi-channel ranging in WSNs.
  • To enhance ranging accuracy by addressing limitations of existing RSSI-based methods in complex indoor scenarios.
  • To achieve globally optimal ranging results without prior parameter or distance calibration.

Main Methods:

  • Proposed an innovative multi-objective evolutionary model integrated with an adaptive extended Kalman filter.
  • Introduced a novel objective function correlating weighted multi-channel RSSI with node distance.
  • Developed an environment-adaptive approach for multi-channel ranging optimization.

Main Results:

  • The proposed algorithm demonstrated significantly higher ranging accuracy compared to existing methods.
  • Achieved globally optimal results, eliminating the need for accurate initial parameter values or target-reference distances.
  • Maintained superior accuracy irrespective of RSSI regularity or the number of propagation paths.

Conclusions:

  • The novel environment-adaptive algorithm offers a robust solution for high-precision WSN ranging in challenging indoor environments.
  • The developed evolutionary model and objective function effectively mitigate multipath effects and noise.
  • This approach advances WSN localization technology by providing more reliable and accurate positioning capabilities.