Trustworthy products and service providers for office security are very expensive. Another option is to follow this article and make your own security system with just a bit of time and hacker spirit. Save a lot of money and no need to trust an external security service company is required.
Visualizing practical, physical event streaming solutions to Industrial IoT customers is quite a challenge. In this recording of a recent Kafka Meetup we went hands-on and showed to the audience how they could build an IIoT model with Kafka on their own.
When you’re manufacturing thousands — or maybe millions — of items, it isn’t possible to perform quality control manually. IIoT sensors can help solve this problem. This post shows how to create a constant event stream of sensor data using our IIoT factory model and how to manually add data anomalies.
Product quality checks are an essential part of industrial manufacturing. To look at each item manually does not scale well, therefore factories outputting large quantities of products use IIoT sensors. We show how to combine Phoenix Contact’s light barrier sensor data with product barcode data to detect anomalies in the manufacturing process.
Conveyor belts are essential components in many factories and the center piece of our IIoT factory model and simulator. In this tutorial we show how you can explain how to simulate factory incidents on a product assembly line.
This article reports on our journey designing and building an IIoT hardware factory model receiving commands via HTTP API and feeding event streaming data from sensors into Kafka. For use case specific scenarios the sensor events and received commands can be recorded, stored and published.
In this tutorial, our Lead Engineer Oliver explains how to programmatically access and use Home IoT devices within small to medium sized offices for custom use cases, such as energy saving, security, disaster detection and fun convenience applications.
In this tutorial, we will build a project in Xapix that will get two data points - vehicle location and speed - in a Kafka event stream. Then based on a condition - is the vehicle travelling faster than 60 miles per hour (MPH) - send a text notification via a Webhook to a browser.
Data is most valuable in the moment when it’s hot. Machine, vehicle or asset transparency is key to delivering better user and customer insights. But how do you enable high value use cases that are based on real-time data?
Digital twins are slowly entering mainstream use. Let’s have a look at three ways in which digital twins can help the automotive industry with saving costs while rolling out innovative digital services.