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WisGate Edge Lite 2

SKU TPX00098 Barcode 7630049203358 Show more
Original price €0
Original price €273,05 - Original price €273,05
Original price
Current price €273,05
€273,05 - €273,05
Current price €273,05
VAT included

Boost your industrial IoT solutions with a reliable and secure gateway for LoRaWAN® connectivity with deep indoor coverage, even in multi-story buildings.

Overview

The Arduino Pro WisGate Edge Lite 2 powered by RAKwireless™ ensures secure and reliable connectivity for a wide range of professional applications and is suitable for medium-sized to wide area coverage in industrial environments. Its deep coverage indoors makes it ideal for applications in multi-story buildings – such as apartment buildings, multi-level parking lots and factories. 

It offers an intuitive out-of-the-box user experience for easy setup and diagnostics, as well as exhaustive tutorials and technical documentation, and comes with an enclosure designed for efficient cooling and optional DIN rail mounting.

Complementing the MKR and Portenta SOM boards, the Arduino IoT Cloud platform and the other LoRa® components in the Arduino ecosystem, WisGate Edge Lite 2 enables you to create and deploy complete and industrialized IoT/IIoT and I4.0 applications – wrapping both RAKwireless™’s specific expertise and Arduino’s smooth user experience into high-quality solutions that connect your LoRa® devices better than ever, from smart cities to logistics and building automation.

Key benefits include:

  • Secure Ethernet, Wi-Fi or LTE connectivity
  • Rapid setup and diagnostics, backup and data logging thanks to SD card slot
  • Deep coverage indoors, ideal for multi-story buildings
  • Enclosure designed for efficient cooling; it allows for wall and DIN rail mounting (optional)
  • WisGateOS, powered by RAKwireless™, based on open source OpenWRT and fully customizable
  • Ideal to implement private networks directly connected to cloud platforms; compatible with public networks
  • Limited cabling for installation thanks to Power over Ethernet (POE)
  • Comprehensive technical documentation by RAKwireless™
Looking for an outdoor gateway for LoRaWAN® connectivity?

Check out WisGate Edge Pro, designed to bring the same ease-of-use and reliability to your outdoor applications. 

Need Help?
Check the Arduino Forum for questions about the Arduino Language, or how to make your own Projects with Arduino. If you need any help with your product, please get in touch with the official Arduino User Support as explained in our Contact Us page.

Warranty
You can find your board warranty information here.


Tech specs

Processor MT7628, DDR2 RAM 128 MB
32 MB flash memory
LoRa®
  • EU868/US915/AS923/AU915
  • LoRaWAN 1.03 stack
  • LoRa® Packet Forwarder
  • Basic Station
  • Network Server
  • Max Transmit Power 27 dBm
  • Min Receiver Sensitivity -139 dBm
Interfaces
  • Ethernet RJ45 10/10 Mbps
  • USB
  • SD-CARD and SIM slots
Connectivity
  • IEEE 802.11b/g/n Wi-Fi
  • LTE (EMEA)
  • LTE FDD: B1/B3/B7/B8/B20/B28A
  • WCDMA: B1/B8
  • GSM/EDGE: B3/B8
  • LTE (USA)
  • LTE FDD: B2/B4/B5/B12/B13
  • WCDMA: B2/B4/B5
Dimensions / Weight
  • 66x127x36 mm
  • 0.3 kg
Software Features
  • OpenVPN, Ping Watch Dog
  • SSH2
  • NTP
  • Router module NAT
  • Firewall
  • Wi-Fi AP mode
  • DHCP Server/Client
  • MQTT/S Bridging
  • LoRa® Data Logger
  • LoRa® Statistics
  • LoRa® Frame Filtering
Antennas LoRa® with RP-SMA connector
LTE Internal antenna
Wi-Fi Internal antenna
Certifications CE, FCC, ACMA/RCM, UKCA
IP 30 rating

Conformities

The following Declarations of Conformities have been granted for this board:
CE
For any further information about our certifications please visit docs.arduino.cc/certifications

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.

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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

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