Skip to content

    Your cart is empty

    Time to spark some excitement 🛒⚡

Taxes and shipping calculated at checkout
Subtotal €0,00

Gravity WiFi IoT Module

Sold out
SKU TPX00142 Barcode 6959420918324 Show more
Original price €0
Original price €18,93 - Original price €18,93
Original price
Current price €18,93
€18,93 - €18,93
Current price €18,93
VAT included

This WiFi IoT module supports multiple programming platforms, such as MakeCode, Mind+, and Arduino IDE, and can also be used on various popular IoT platforms like Easy IoT, IFFTTT, ThingSpeak, SIoT.

Overview

This WiFi IoT module would be an excellent choice for IoT classroom teaching and smart home projects. It supports multiple programming platforms, such as MakeCode, Mind+, and Arduino IDE, and can also be used on various popular IoT platforms like Easy IoT, IFFTTT, ThingSpeak, SIoT.

Besides that, the module is designed with easy-to-use Gravity interface and employs UART and I2C communication protocols. You can use it to build IoT projects with other mainboards like micro:bit, Arduino, STM32.

Features:

  • Support Arduino IDE programming, MakeCode and Mind+ Graphical Programming
  • I2C and UART Communication
  • Support Maincontroller Arduino, micro:bit
  • Support protocols like MQTT, HTTP, etc. Work well with IoT platforms EasyIoT, IFTTT, ThingSpeak
  • PH2.0-4P Gravity Interface

Tech specs

Power Supply 3.3V~5.5 V
Communication I2C, UART
Wireless Mode IEEE802.11b/g/n
Encryption Type WPA WPA2/WPA2–PSK
WiFi Frequency 2.4GHz
Built-in Protocol TCP/IP Protocol Stack
Dimension 37×32mm/1.46×1.26”
Supported IoT Platforms Easy IoT, IFFTTT, ThingSpeak, SIoT
Supported Programming Platforms Arduino IDE, MakeCode, Mind+

 

Get Inspired

PROJECT HUB
Tiny ML in interactive spaces Arduino X K-WAY Challenge Project
Tiny ML in interactive spaces Arduino X K-WAY Challenge Project
Project Tutorial by fullmakeralchemist

An intelligent device to track moves with responses during an interactive space with mapping, backlight, music and smart sculptures. This project makes use of a machine learning algorithm capable of tracking and detecting moves to identify associated gesture recognition through a microcontroller. Smart sculptures, lighting, music and video projection to trigger with each assigned gesture, creating a powerful AV experience highlighting the incredible potential of TinyML for the performing arts. This allows the corresponding media set Tiny ML in interactive to play when the right move was made because all these elements interact to create a new experience. This allows us to create Interactive installations, these sculptures use a combination of motors, sensors, and other electronics to create an immersive and interactive experience for the viewer. They may include projections, sound, and other sensory elements to create a complete experience.

read more
BLOG
These projects from CMU incorporate the Arduino Nano 33 BLE Sense in clever ways
These projects from CMU incorporate the Arduino Nano 33 BLE Sense in clever ways
May 22, 2023

With an array of onboard sensors, Bluetooth® Low Energy connectivity, and the ability to perform edge AI tasks thanks to its nRF52840 SoC, the Arduino Nano 33 BLE Sense is a great choice for a wide variety of embedded applications. Further demonstrating this point, a group of students from the Introduction to Embedded Deep Learning course at Carnegie Mellon University have published the culmination of their studies through 10 excellent projects that each use the Tiny Machine Learning Kit and Edge Impulse ML platform. Wrist-based human activity recognition Traditional human activity tracking has relied on the use of smartwatches and phones to recognize certain exercises based on IMU data. However, few have achieved both continuous and low-power operation, which is why Omkar Savkur, Nicholas Toldalagi, and Kevin Xie explored training an embedded model on combined accelerometer and microphone data to distinguish between handwashing, brushing one’s teeth, and idling. Their project continuously runs inferencing on incoming data and then displays the action on both a screen and via two LEDs. Categorizing trash with sound In some circumstances, such as smart cities or home recycling, knowing what types of materials are being thrown away can provide a valuable datapoint for waste management systems. Students Jacky Wang and Gordonson Yan created their project, called SBTrashCat, to recognize trash types by the sounds they make when being thrown into a bin. Currently, the model can three different kinds, along with background noise and human voices to eliminate false positives. Distributed edge machine learning The abundance of Internet of Things (IoT) devices has meant an explosion of computational power and the amount of data needing to be processed before it can become useful. Because a single low-cost edge device does not possess enough power on its own for some tasks, Jong-Ik Park, Chad Taylor, and Anudeep Bolimera have designed a system where

read more

Inspired by your shopping trends

  • Arduino MKR WiFi 1010

    The Arduino MKR WiFi 1010 is the easiest point of entry to basic IoT and pico-network application design. Whether you are looking at building a sensor network connected to your office or home route...

  • Mini encapsulated solar cell 2V 0,6W

    A photovoltaic solar panel with extremely small dimensions, ideal for conducting experiments with solar energy.

  • Arduino Nano 33 IoT with headers

    The Arduino Nano 33 IoT is the easiest and cheapest point of entry to enhance existing devices (and creating new ones) to be part of the IoT and designing pico-network applications. Whether you a...

  • Environmental Monitor Bundle

    Measure, read and visualize the temperature, humidity, pressure, light and UV levels. This bundle with accompanying online project shows you how to set-up and read environmental data from the senso...

Compare products

0 of 3 items selected

Select first item to compare

Select second item to compare

Select third item to compare

Compare