Narrowband IoT (NB-IoT) / CAT.NB1 - WEPTECH elektronik GmbH

What ist Narrowband IoT (NB-IoT)?

Narrowband IoT (NB IoT) / Cat.NB1 was released by 3GPP (Release Version 13) as the new IoT narrowband communication in the LTE-Network and is the response of telecommunication companies to LoRa and SigFox. NB-IoT uses licensed frequency bands and the existing network infrastructure of the telecommunication provider.

With a system dynamic that is higher then 20 dB, Narrworband IoT (NB-IoT) offers high building penetration down to basements. Due to the low energy consumption, the large ranges and low operating costs, the technology is suitable e.g. for smart meter applications.

For questions about the technology Narrworband IoT (NB-IoT) or our products, just contact us.

Advantages
Narrowband IoT - The Smart cellular network

low energy consumption
low operating and acquisition costs
high network coverage and range

deep building penetration
3GPP - industry standard
high capacity - more than 100.000 devices per cell

Application
Narrowband IoT - The future is smart

Everything becomes "smart": water meters, buildings, greenhouses or machines. The Internet of Things enables the collection, evaluation and connection of various data. Narrowband IoT (NB-IoT) therefore uses the existing cellular network to transfer the data.

Smart Metering: Manual collection of water, gas or electricity data will soon be a thing of the past. Metering data is transferred automatically at the end of the month for the digital and transparent service charge statement.

Smart Building: No more mold formation in damp rooms. On time battery replacement in smoke detectors or safe snow clearing of roofs. This is what tomorrow's building management looks like.

Smart Farming: Temperature, humidity, soil moisture or lighting conditions. All relevant information can be collected and evaluated. Look forward to a perfect harvest, while saving resources, money and time.

Smart Maintenance: The key word is predictive maintenance. The smallest irregularities in the operational use can be recognized in advance by machine learning, allowing maintenance or repair before a massive defect occurs.