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Indoor localization using Wi-Fi fingerprinting and machine learning.

Python 0.14% C 98.64% Makefile 1.15% Kotlin 0.01% Java 0.01% Swift 0.01% Objective-C 0.01% Dart 0.05%
wifi-localization esp32 machine-learning firebase

indoor-localization-wearable's Introduction

Indoor-Localization-Wearable

This project was developed for educational purposes as a project work in sbe403a Medical Electronics Systems(SBME department, faculty of engineering, Cairo University).

The system was tested on a single floor at the SBME department in Cairo university's campus.

Methodology

The used approach in the development of this system was based on WIFI fingerprints by this was done by collecting WIFI-RSSI data(Received signal strength indication) at different locations to train and select the best machine learning model that can be used to classify or predict the location of an esp32-based wearable device according to the latest RSSI sent from the device to a real time database (firebase). system diagram

Clients:

  • Mobile APP:

QT GUI

  • Desktop APP:

QT GUI

indoor-localization-wearable's People

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adel-elmala avatar eslam69 avatar mahmoud-monem avatar

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