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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Updated: Oct 25, 2025

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults
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A Small World Graph Approach for an Efficient Indoor Positioning System.

Max Lima1, Leonardo Guimarães2, Eulanda Santos1

  • 1Institute of Computing, Federal University of Amazonas, Manaus 69080-900, Brazil.

Sensors (Basel, Switzerland)
|August 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel graph-based technique for indoor positioning systems (IPS). This method significantly improves scalability and efficiency compared to traditional k-Nearest Neighbors (kNN) algorithms.

Keywords:
hierarchical small world graphsindoor positioning systemsk-nearest neighbors

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Machine Learning

Background:

  • Indoor Positioning Systems (IPS) are crucial for locating mobile devices indoors.
  • The fingerprint technique, using signal strength, is a common IPS method.
  • k-Nearest Neighbors (kNN) offers accuracy but lacks scalability for large datasets.

Purpose of the Study:

  • To develop a scalable and efficient search structure for IPS.
  • To overcome the limitations of traditional kNN in large-scale indoor environments.
  • To enable IPS deployment on resource-constrained devices.

Main Methods:

  • Implemented a novel hierarchical navigable small world graph technique.
  • Developed a synthetic IPS dataset generator for performance evaluation.
  • Created a real-world, large-scale IPS testbed for validation.

Main Results:

  • The graph-based solution demonstrated significant performance gains.
  • Achieved 98% performance improvement over classic k-Nearest Neighbors (kNN).
  • Showed at least 80% improvement compared to Kd-Tree and Ball-Tree variants.

Conclusions:

  • The proposed graph-based approach enhances IPS scalability and efficiency.
  • This technique is suitable for large-scale indoor positioning scenarios.
  • It offers a viable solution for resource-limited devices in IPS applications.