Official implementation of the MDPI IJERPH submitted paper: "Improving remote health care with collaborative agents on IoT drone-aided lora network"
The first figure below shows an overview of the proposed Drone-aided system for health analytics in rural areas. The second figure shows screenshots of the protoype and the Drona-Lora communication test experiment.
|-- src // Code
|-- __init__.py/ // main file of client
|-- data_utils.py/ // functins for data retrieval and preprocessing
|-- doing_fl.py/ // Performs Federated Learning
|-- doing_local_central.py/ // seed script for local and central training
|-- spider_plot.py/ // plot individual accuracy on a spider plot
|-- DP_central.py/ // create and train a DP model
|-- DP_central_2.py/ // create and train a DP model 2
|-- model_utils.py/ // functions for model creation and training
|-- plot_utils.py/ // functions for plotting
|-- results // Federated learning results
|-- accuracy/ // Accuracy on HAR datasets under different FL methods
|-- data // Human Activity Recognition datasets
|-- large_scale_HARBox/ // HarBox dataset
|-- imu_dataset/ // IMU dataset
|-- depth_dataset/ // Depth dataset
|-- HARS/ // HARS dataset
|-- HARB4/ // part of HARB4 dataset
|-- uwb_dataset/ // UWB dataset
|-- communication // LoRa libraries
|-- LoRa.py // LoRa class
|-- ReliableLoRa.py // ReliableLoRa class
|-- MainFunctions.py/ // Helper functions for LoRa and ReliableLoRa
|-- Helpers.py // More helper functions for LoRa and ReliableLoRa
|-- GlobalVariables.py // Global variables for LoRa and ReliableLoRa
|-- Examples.py // An example showing how to send/receive a file
|-- README.md
|-- assets // figures used in this README.md