Displaying 1 to 18 from 18 results

awesome-iot - Awesome IoT

  •    Python

IoTSeeker This scanner will scan a network for specific types of IoT devices to detect if they are using the default, factory set credentials. nShield ★ 66 ⧗ 35 - An Easy and Simple Anti-DDoS solution for VPS,Dedicated Servers and IoT devices based on iptables.

sod - An Embedded Computer Vision & Machine Learning Library (CPU Optimized & IoT Capable)

  •    C

SOD is an embedded, modern cross-platform computer vision and machine learning software library that expose a set of APIs for deep-learning, advanced media analysis & processing including real-time, multi-class object detection and model training on embedded systems with limited computational resource and IoT devices. SOD was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in open source as well commercial products.

Eclipse Hono - Remote service interfaces for connecting large numbers of IoT devices

  •    Java

Eclipse Hono provides uniform (remote) service interfaces for connecting large numbers of IoT devices to a (cloud) back end. It specifically supports scalable and secure data ingestion (telemetry data) as well as command & control type message exchange patterns and provides interfaces for provisioning & managing device identity and access control rules.

Basecamp - An Arduino library to ease the use of the ESP32 in IoT projects

  •    C++

Basecamp - ESP32 library to simplify the basics of IoT projects Originally written by Merlin Schumacher (mls@ct.de) for c't magazin für computer technik Licensed under GPLv3. See LICENSE for details. Exhaustive documentation will provided in the next few weeks. An example can be found inside the example folder.




EdgeML - This repository provides code for machine learning algorithms for edge devices developed at Microsoft Research India

  •    C++

This repository provides code for machine learning algorithms for edge devices developed at Microsoft Research India.Machine learning models for edge devices need to have a small footprint in terms of storage, prediction latency and energy. One example of a ubiquitous real-world application where such models are desirable is resource-scarce devices and sensors in the Internet of Things (IoT) setting. Making real-time predictions locally on IoT devices without connecting to the cloud requires models that fit in a few kilobytes.

manage-control-device-node-red - Create, connect and simulate devices with Watson Data Platform and Node-Red

  •    Javascript

Build an IoT project with a simualted device that sends events for data monitoring on Watson IoT Platform on IBM Cloud. This guide steps you through the process of connecting devices to Watson IoT Platform, monitoring and acting on device data.

iot-workshop - A complete IoT Workshop

  •    Javascript

This workshop will walk you through a few common IoT scenarios like basic user input (a button), output (an LED), a camera, a web service call (Microsoft Cognitive Services Vision API), and finally an IoT ingestion service (Azure IoT Hub). We'll use Node.js as the device language. When it's all finished and working, with the click of a button you'll be able to take a picture, figure out what objects are in the picture, and then record the result in a cloud service.

line-things-starter - The sample codes for LINE Things Developer Trial

  •    C++

These are the sample codes for LINE Things Developer Trial. LINE Things compatible device example firmware and LIFF application code are included.


esp32 - Build your own toolchain to develop, test, build and finally deploy a Golang controller to your ESP32 device

  •    Makefile

Build your own toolchain to develop, test, build and finally deploy a Golang controller to your ESP32 device. The Arduino IDE is easy to use. But I faced problems like maintainability and testability at more complicated IoT projects. I needed to compile and flash the ESP32 before testing my code functionality by doing it 100% manually.

qfactom - kdb+ wrapper library for interacting with the factom client (factomd) and wallet (factom-walletd) applications via the v2 REST APIs

  •    C

qfactom is a kdb+ wrapper library designed to interact with the Factom blockchain via the factomd client (factomd) and wallet (factom-walletd) REST APIs. The library allows records, in the form of kdb+ tables, lists etc, to be easily secured on the Factom blockchain through the use of simple q function. Entries made to Factom chains can serve as a Proof of Existence, enabling cryptographically secure audit trails to be constructed. Data stored on existing Factom chains, like those used to hold Stock market prices, can also be easily extracted and coverted to kdb+ table format, see example. Combining the speed and scalability of kdb+, with the security and scalability of Factom, enables millions of records to be secured quickly and safely.

iot-device-trend-analysis - WARNING: This repository is no longer maintained :warning: This repository will not be updated

  •    Python

This repository will not be updated. The repository will be kept available in read-only mode. In this code pattern, we will setup and create a web application to visualize IoT device data and view trends and stats of device fields across days. The IoT industries are looking for ways to analyze the use of IoT devices, and would like better understand the usage of devices. This code pattern will demonstrate using IBM solutions to read and store IoT device data, and then build an application on top of it. The code pattern uses services offered on IBM Cloud such as IBM Watson IoT Platform and Cloudant DB, in addition to deploying the application to the IBM Cloud. Once setup, the application displays the time-series field data as plots, showing device data trends and statistical analysis.

embeddedinfralib - EmbeddedInfraLib is a set of C++ libraries and headers that provide heap-less, STL like, infrastructure for embedded software development

  •    C++

Description: EmbeddedInfraLib is a set of C++ libraries and headers that provide heap-less, STL like, infrastructure for embedded software development. EmIL can be built by-itself, for example to execute the included micro-tests, or it can be built as part of a larger project. This paragraph describes how to build EmIL by-itself.

iot-device-phone-simulator - A web application which acts as an IoT device when loaded in a smart phone browser

  •    Javascript

This web application once published to IBM Cloud and opened on a smart phone browser acts as an IoT device to publish accelerometer and Gyroscope data to Cloudant NoSQL database. See License.txt for license information.

Basecamp - An Arduino library to ease the use of the ESP32 in IoT projects

  •    C++

Basecamp - ESP32 library to simplify the basics of IoT projects Originally written by Merlin Schumacher (mls@ct.de) for c't magazin für computer technik Licensed under GPLv3. See LICENSE for details. Exhaustive documentation will provided in the next few weeks. An example can be found inside the example folder.

Magic-Leap-Gesture-IoT-Example - Control lights in the physical world from the augmented world using hand gestures

  •    CSharp

Imagine a world where you walk in a smart home, look at a smart light in the room, make a gesture with your hands, and the light turns off or on. Swipe with a hand to change the channel on your TV. Glance at a thermostat and speak your preferred temperature aloud. This isn't the future. This is all possible now with the Magic Leap One and PubNub. This project demonstrates how to control an RGB LED or a power strip with an Arduino using Magic Leap Gestures and PubNub. Learn more about how to build your own smart home with Magic Leap and PubNub from the tutorial.

MLEdgeDeploy - Automatic Over the Air Deployment of Improved Machine Learning Models to IoT Devices for Edge Processing

  •    Objective-C

For IoT devices, processing signals on the same layer that they are collected on is desirable because it lessens network dependency and output latency. Achieving this effect with deterministic types of processing is fairly straightforward, as it is simply a matter of running the formerly network-accessible program on the local hardware. For non-deterministic types of programs, such as those enabled by modern machine learning techniques, there are a few more considerations. Requisite to these techniques is a training process that is both data heavy and compute intensive. This is a significant constraint to consider because most IoT hardware is purpose built for collecting and relaying signals, and, therefore, woefully ill-equipped to handle the intensive training process by which these systems "learn". Once trained, however, such programs shed their dependency on high-end hardware and run perfectly well on minimally equipped systems.

iot-java-sdk - Appdynamics IoT Java SDK to monitor performance of embedded applications on connected devices

  •    Java

This repository contains the AppDynamics IoT Java SDK that can be used in embedded applications to monitor network perfomance, errors, and business metrics. The best way to understand how to use the SDK is to run the sample application.

lib-python - Blynk IoT library for Python and Micropython

  •    Python

This library provides API to connect IoT hardware that supports Micropython/Python to Blynk Cloud and communiate with Blynk apps (iOS and Android). You can send raw and processed sensor data and remotely control anything that is connected to your hardware (relays, motors, servos) from anywhere in the world. With Blynk Library you can connect over 400 hardware models (including ESP8266, ESP32, NodeMCU, all Arduinos, Raspberry Pi, Particle, Texas Instruments, etc.)to the Blynk Cloud. Full list of supported hardware can be found here.






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