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Machine learning techniques for indoor localization on edge devices
Diego Méndez
, Daniel Crovo
, Diego Avellaneda
Department of Electronic Engineering
SIRP - Intelligent Systems, Robotics and Perception
Universidad Javeriana
Research output
:
Chapter in Book/Report/Conference proceeding
›
Chapter
›
peer-review
3
Scopus citations
Overview
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Keyphrases
Machine Learning Techniques
100%
Indoor Localization
100%
Edge Devices
100%
Internet of Things
50%
Location-based Services
50%
Low Bandwidth
50%
Context-aware Applications
50%
GPS-based
50%
TinyML
50%
Indoor Positioning System
50%
Real Application
50%
Machine Learning Based
50%
Indoor Scenario
50%
Support Machine Learning
50%
Cloud Deployment
50%
Indoor Localization System
50%
Providing Service
50%
Technical Restrictions
50%
Low Latency
50%
Computer Science
Machine Learning Technique
100%
Location-Based Service
50%
Providing Service
50%
context aware application
50%
Real Application
50%
Indoor Scenario
50%
Machine Learning
50%
Learning System
50%
Internet-Of-Things
50%
Global Positioning System
50%
Technical Challenge
50%
INIS
indoors
100%
machine learning
100%
devices
100%
applications
40%
comparative evaluations
20%
implementation
20%
solutions
20%
internet
20%
gps
20%
positioning
20%
clouds
20%