IoT use cases: Air humidity

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IoT for air humidity monitoring

A high level of air humidity is the growing ground for many hazardous problems like mold. So, monitoring air humidity is a great way to optimize indoor conditions or protect people from poor environments and properties and valuables from damage. By implementing our multi-sensors with humidity function (available for LoRaWAN and Z-Wave), you receive updates in real-time on the indoor conditions to set up scenarios to adjust the HVAC system based on readings.

IoT air humidity sensors use cases

Finding the optimal climate conditions is essential in many applications. A few examples of the many use cases within air humidity monitoring:

Monitoring storage conditions for food (wine, cheese, etc.) or valuables (paintings, pictures, clothing)
monitoring to optimize indoor conditions for humans/animals
monitoring any signs of mold-friendly conditions that can damage building structure

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Smart Agriculture

Smart Vertical Farming with Multi-Sensors

How can IoT aid in our contemporary agricultural revolution? Farmers are turning to indoor farming and it needs to be "smart".

Type: Air humidity, Ground moisture, LoRaWAN, Temperature
Application: Indoor climate

Smart Building use cases Smart Home use cases

Indoor Light and Air Quality for Productivity

Type: Air humidity, Lighting, Temperature
Application: Analytics, Indoor climate, PropTech

Smart Ship

Connected Ship

The goal of the Connected Ship project is to develop and implement a horizontal ship digitalization platform to connect the different digital automation systems and IoT sensor networks across different communication standards on board a ship

Type: Air humidity, IoT integration, Lighting, LoRaWAN, Occupancy, Open/Close, Temperature, Video as a sensor, Water Leak detection
Application: Analytics, Asset management, Health, Indoor climate, Predictive maintenance, R&D projects, Security, Sensative customer case

Smart Agriculture

Automatic validation of sensor data

The project will, with the help pf Machine Learning and AI algorithms, identify common sensor problems for the most interesting sensors in agriculture, including sensors for temperature, humidity, precipitation and wind in weather stations, sensors for soil temperature and humidity, etc.