Product Features

  • ARM Base Low power processor
  • 4G NBIOT/LTE-CatM1
  • GPS support
  • Isolate Digital Input/Out ports
    • DI x 3
    • DO x2
  • Isolate RS485 Modbus
  • Isolate One-Wire Interface
  • Battery Cell inside
  • NFC EzConfig App
  • AC Power Monitor
    • Current transformer (0-100A)
    • AC Sensing (0-220V)

WT-N630XA

Product Introduction

The WT-N632A wireless sensor node is a versatile device that effectively bridges the gap between traditional wired sensors and cloud servers. With its low power consumption and built-in battery, this sensor node can be effortlessly deployed in a wide range of environments, making it suitable for a variety of scenarios.

This device supports several communication interfaces, including Modbus-RTU, One-Wire, DI/DO, and more, making it compatible with most devices used in industrial and consumer settings.

With the help of the long-distance transmission technology NBIOT/LTE-M, the device collects local data and uploads it to the cloud server, providing an efficient way to monitor and analyze data.

The local sensor information is collected by the device and communicated with the cloud server via the standard communication protocol MQTT. You can easily set device parameters, such as the report rate of sensor data, Network IP, and MQTT sub/pub topics, using our EZCONFIG App (Android Only).

This wireless sensor node can be deployed into any application due to its wireless and low-cost features. Some examples include refrigerator power and health monitoring, cold chain truck tracking, and agricultural weather stations.

Rent-Housing Wireless Meter

The product we offer is an IoT power meter and security application designed to meet the needs of rental-house customers. This system allows customers to remotely monitor and manage their rental properties' power usage and security features through a centralized, user-friendly interface.

Intelligent Aquaculture

That a smart breeding system could be used to manage fish breeding for fish electric symbiosis. This system would use expertise and machine learning to create digital avatars to help breed better quality fish. This system would rely on data from previous breeding experiences to inform breeding decisions. Overall, this system could help improve the efficiency and quality of fish breeding for this specific purpose.