Source code for M.T. Hoang, B. Yuen, X. Dong, T. Lu, R. Westendorp and K. Reddy, “Recurrent Neural Networks for Accurate RSSI Indoor Localization,” IEEE Internet of Things Journal, 2019
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Updated
Mar 23, 2022 - Python
Source code for M.T. Hoang, B. Yuen, X. Dong, T. Lu, R. Westendorp and K. Reddy, “Recurrent Neural Networks for Accurate RSSI Indoor Localization,” IEEE Internet of Things Journal, 2019
Source code for the paper "A Soft Range Limited K-Nearest Neighbors Algorithm for Indoor Localization Enhancement"
Wifi based Indoor Localization system utilizing Wi-Fi Fingerprinting Technique - AI/ML Approach
Dataset for ESP32C3 WiFi FTM RSSI Indoor Localization
Wi-Fi BLE RSSI SQI Localization dataset
An indoor localization system for hospitals using WiFi RSSI Fingerprinting and Machine Learning. Features a Flutter mobile app for navigation and an ML engine for precise room-level positioning.
Android indoor navigation with Wi-Fi fingerprinting, PDR, and graph-based routing.
Indoor positioning research, engineering, deployment, citation resources, datasets, code, and AI-discoverable publication pages by Ahmed Mansour.
Repository containing the code for my Master's thesis in Data Science at the Universitat Oberta de Catalunya (UOC).
Android app uses wifi-fingerprinting and google maps to collect geo location data
Open teaching platform for indoor positioning (WiFi fingerprinting + IMU dead reckoning) built by students on their own building. M5StickC firmware, Python server, Three.js 3D viewer, seven lab handouts. Material in Spanish. MIT + CC BY 4.0.
Android wifi fingerprint locator
Smart Campus Navigation Using Augmented Reality Based on Wi-Fi Fingerprinting (Flutter)
WiFi RSSI indoor localization: KNN/SVM/MLP classifiers + Flask REST API | 91.1% accuracy, 0.19m error | Related to Nature-published WiFi security research
Flask with whereami - Client request nearby WiFi signals to server then machine learning to predict where you are
Production engineering reference for indoor mapping & wayfinding automation: floor-plan parsing, routing graphs, beacon/Wi-Fi positioning, and deployment pipelines — with runnable Python. Built as an offline-capable PWA.
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