Feature: Industrial
DDS over TSN: Future-proofed architectural framework for industrial automation
By Mark Carrier, Principal Engineer, RTI I
n an age where data is at the core of every decision-making process, “data” and “connectivity” are the base of any modern automation system. On the one hand, they open up a new world
of possibilities like autonomous cars and smart cities, but, on the other, the increasing number of connected devices and the sheer volume of data can burden and even overwhelm networks. Tis dilemma is driving new ways to
store, analyse and process data closer to the source – i.e., edge computing. Edge computing addresses the physical limitations of networks, notably bandwidth, congestion and latency, by decentralising the network. Simply reducing the distance data needs to travel eliminates latency, whilst reducing the load on the network’s bandwidth eliminates congestion. Modern protocols like MQTT and
OPC UA publish/subscribe are helping to bring new applications closer to the edge. Tey have been designed (or even retro- fitted) as lightweight publish/subscribe messaging transports. Further, they are
ideal for connecting remote devices to edge applications, or quickly integrating devices into centralised cloud infrastructure. However, these solutions are not perfect;
what’s needed is a single approach that handles both edge and cloud connectivity in an interoperable manner.
Data-centricity and convergence In industrial settings, raw data consists of individual facts that lack context, are devoid of meaning and difficult to interpret. However, information can best be described as a set of data in context, with relevance to one or more things at a point in time or for a period of time – thus, information must have both relevance and a time frame. Data has a certain value, and
understanding that value requires a methodology for data valuation. Hence, system architects are moving to data centricity for data valuation. Data centricity is an architectural pattern
where data is the primary and permanent asset, regardless of the application. A data-centric architecture uses unified data models to describe a system in terms of
the information exchanged, not devices or applications. Data models provide schemas (what information is flowing and how it is related) and a control model (how and when it flows). Also worth mentioning is that the data
produced today has evolved from simple time-series (timestamp, key, value) to include advanced sensor data, real-time video streams, LIDAR and real-time GPS location, among many others. Managing these complex types of data requires a converged infrastructure solution to provide real-time capabilities and simplify data flow management within a single data-centric connectivity infrastructure.
DDS for data-centricity Te Object Management Group (OMG) Data Distribution Service (DDS) standard is a platform-independent soſtware framework for designing and implementing data-centric soſtware. A DDS data model, a relational concept like a database table, provides a schema, while DDS Quality of Service (QoS) provides exact control over data rates, deadlines and other data-flow parameters. By defining
www.electronicsworld.co.uk September 2023 19
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