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Local attraction refers to disturbances in compass readings caused by magnetic influences from nearby objects such as metal fences, buried pipes, vehicles, buildings, power lines, or natural iron ore deposits. Small items like wristwatches, steel tools, or belt buckles can also interfere with the compass by creating local magnetic fields that distort the Earth's natural magnetic field. These distortions lead to inaccurate readings, posing navigation and land surveying challenges.Local...
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The highest and lowest values of a function, relative to a reference axis, are known as extreme values. These include absolute maximum and absolute minimum values, which represent the highest and lowest points the function reaches across its entire domain. Within a restricted portion of the function, the highest and lowest values are referred to as local maximum and local minimum values, respectively.Periodic functions, such as sine and cosine, show extreme values at infinitely many points due...
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GSOS-ELM: An RFID-Based Indoor Localization System Using GSO Method and Semi-Supervised Online Sequential ELM.

Fagui Liu1, Dexiang Zhong2

  • 1School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China. fgliu@scut.edu.cn.

Sensors (Basel, Switzerland)
|June 24, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel RFID positioning algorithm (GSOS-ELM) that enhances accuracy and reduces costs. The algorithm adapts to environmental changes, offering improved indoor localization performance.

Keywords:
Glowworm Swarm Optimization (GSO)indoor localizationradio frequency identification (RFID)semi-supervised online sequential extreme learning machine (SOS-ELM)

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Area of Science:

  • Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Radio Frequency Identification (RFID) is a leading indoor positioning technology due to its non-line-of-sight and non-contact capabilities.
  • Existing RFID positioning algorithms face challenges with tag density, efficiency, and environmental robustness.
  • There is a need for more accurate and adaptable RFID indoor localization solutions.

Purpose of the Study:

  • To develop an improved RFID indoor positioning algorithm that addresses current limitations in accuracy and environmental adaptability.
  • To enhance algorithm efficiency and reduce the reliance on extensive labeled data.
  • To create a robust system capable of continuous environmental perception and adaptation.

Main Methods:

  • Introduction of the Glowworm Swarm Optimization (GSO) fused with semi-supervised online sequential extreme learning machine (SOS-ELM) algorithm, termed GSOS-ELM.
  • GSO algorithm is employed to automatically optimize SOS-ELM regularization weights for diverse initial conditions.
  • Semi-supervised learning reduces the number of required labeled reference tags, lowering system costs.
  • Online learning enables continuous system updates to adapt to environmental changes and interference.

Main Results:

  • The GSOS-ELM algorithm demonstrated superior positioning accuracy compared to existing methods in both simulation and testbed experiments.
  • The system showed significant adaptability to environmental variations and interference.
  • Reduced need for labeled reference tags was confirmed, indicating cost-effectiveness.

Conclusions:

  • The proposed GSOS-ELM algorithm offers a more accurate and robust solution for RFID indoor positioning.
  • The fusion of GSO and SOS-ELM provides an efficient method for optimizing positioning parameters and adapting to dynamic environments.
  • This approach significantly reduces system costs and improves performance in challenging indoor settings.