wolfMQTT is a small, fast, portable MQTT client implementation, including support for TLS 1.3.
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Updated
Jul 29, 2024 - C
wolfMQTT is a small, fast, portable MQTT client implementation, including support for TLS 1.3.
Notes on Machine Learning on edge for embedded/sensor/IoT uses
A flexible data historian based on InfluxDB, Grafana, MQTT, and more. Free, open, simple.
Global Sensor Networks
Datalogger for MicroPython and CPython.
esp8266 framework stack for easy configurable applications
Simplest DIY low power, low cost, standalone or web connected IOT sensors network framework using ESP32 or ESP8266 (no third party service required).
A dataset with 40+ hours of sensor data obtained from 10 players in League of Legends
The Home2Ls - Smart Tools for a Private Home
Sensor network to monitor indoor CO2 and VOC levels to improve the air quality. To fight against the COVID-19 pandemic it could be important to raise the awareness that there is a potential correlation between air quality and health.
µSDN: A low-overhead SDN stack and embedded SDN controller for Contiki.
Sensor network middleware in Fortran 2018, for deformation monitoring and geodetic control measurements in the IoT (client and server).
MATLAB code for solving the Euclidean Distance Matrix completion problem.
Randomly Deployed Wireless Sensor Network. UAV data collection. Nodes clustering. Find and build optimal route to collect data. Build network energy model. Calculate energy for data transmittion. Display charge, lifetime, used energy.
Simple Wireless-Sensor-Network Optimization using Genetic-Algorithm.
Smart Emission Data Platform - data harvesting, processing and OGC services (WMS, WFS, SOS, STA) for sensor data
Wired sensor network for IoT with PIC16F1 MCU
Home Climate Control ESP32 based edge device firmware
Code for a scalable data collection from multiple sensors (IMU, infrared camera, EMG, EEG, GSR, HRM, environmental sensors, eye tracker).
Source code of the numerical experiments presented in "Energy-Efficient Edge-Facilitated Wireless Collaborative Computing using Map-Reduce" by Antoine Paris, Hamed Mirghasemi, Ivan Stupia and Luc Vandendorpe (presented at SPAWC19).
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