HiveMQ
Canvas Category Software : Operational Technology : IIoT
HiveMQ’s MQTT broker makes it easy to move data to and from connected devices in an efficient, fast and reliable manner. We make it possible to build connected products that enable new digital businesses.
Assembly Line
A Comparative Analysis of Data Modelling Standards for Smart Manufacturing
In essence, adopting data modeling standards can facilitate seamless data exchange across the entire value chain, enhancing overall efficiency and cooperation among various applications and machines. Crucial to this evolution is semantic modeling, allowing machines to deduce meaning without human intervention. Thus, the concept of information modeling, encapsulating not only data but its meaning, is paramount to facilitating intelligent, autonomous decisions.
The Digital Twin Definition Language (DTDL) language follows JSON syntax but is based on JSON-LD. JSON-LD, or JSON for Linked Data, is a method of encoding Linked Data using JSON. It is a World Wide Web Consortium (W3C) standard that provides a way to enrich your data by contextualizing it with schemas (vocabularies) that you choose. This makes it easy to define complex models and relationships between different parts of a system.
Sparkplug and OPC UA, on the other hand, provide a way to structure data and ensure interoperability. Sparkplug uses MQTT and Protocol Buffers, focusing on SCADA/IIoT solutions and efficient data encoding, while OPC UA provides a more generalized approach, offering industry-specific guidelines through companion specifications.
A Data-Driven Approach to Sustainability in Industry 4.0 Using MQTT
The MQTT protocol is the de-facto standard for IoT messaging. It works following the publish/subscribe (Pub/Sub) pattern. Many manufacturing and industry 4.0 companies use MQTT as it is lightweight, supports bi-directional messaging, can scale to millions of connected devices, works well over unreliable networks, and allows secure communication.
At the Internet of Things World, Berlin event, HiveMQ, SVA, and Splunk demonstrated the complete cycle of a connected car platform. In this small practical demonstration, we showed how combining data movement and communication efforts could accelerate sustainability in the automotive industry and other verticals. The demo exhibited a HiveMQ broker connecting several autonomous racing cars. The data published by these cars were forwarded to Splunk using SVA’s HiveMQ Splunk Extension. Splunk’s Sustainability dashboard visually brought key sustainability metrics like C02 emissions and fuel efficiency to life.
Building Industrial Digital Twins on AWS Using MQTT Sparkplug
Even better, a Sparkplug solution is built around an event-based and publish-subscribe architectural model that uses Report-By-Exception for communication. Meaning that your Digital Twin instances get updated with information only when a change in the dynamic properties is detected. Firstly, this saves computational and network resources such as CPU, memory, power and bandwidth. Secondly, this results in a highly responsive system whereby anomalies picked up by the analytics system can be adjusted in real-time.
Further, due to the underlying MQTT infrastructure, a Sparkplug based Digital Twin solution can scale to support millions of physical assets, which means that you can keep adding more assets with no disruptions. What’s more, MQTT Sparkplug’s definition of an MQTT Session State Management ensures that your Digital twin Solution is always aware of the status of all your physical assets at any given time.