Combining hardware & software
The tight integration between the hardware and the backend enable a strong overall security in which the backend server is able to provide end-to-end encryption and detect hardware, sensor and/or network intrusion.
As part of the IoT Gatekeeper system we use Kafka, a distributed stream setup which is well suited to distribute data within an organization in a robust and scalable fashion. Below you see an example implementation.
Our Wave NB-IoT device communicates both device status as measurement data over the NB-IoT network to our IoT Gatekeeper Kafka producer. Here the Kafka topics are being attached to the messages and these are placed on the Kafka bus. Any consumer on the Kafka bus which is subscribed to this topic (and has access) is able to consume the message from the bus and process it. Click here to see how it works.
Float offers a Machine Learning consumer called ‘Kit’ on the Kafka stream to enable real-time Machine Learning analysis. This can be used for a variety of use cases: predictive maintenance, fraud detection and security threats. But also customer friendly services as an alert when an elderly has different water usage pattterns which could indicate they need to be checked.
More and more drinking water companies need fine granular pressure data from their water network to:
Improve customer satisfaction
Even out pressure over the day
Detect and locate breakages and leakages
Using Floats Wave NB-IoT pressure sensors enables a drinking water company to add pressure data points at limited total costs of owner. Costs are limited in part because the sensor will be batteryfed, with a battery life of 10+ years.