First, a few questions:
If you have ever done any data analysis from a sensor or other type of data source, you have most likely followed a process where you collect the data, you convert the data and then use MATLAB to process and analyze the data. Using MATLAB to analyze the data is a very well known tool to accomplish that task. Collecting and converting the data, so that it is usable in MATLAB, can take an enormous amount time. Thanks to an integration that was completed by MathWorks, it is now possible to easily connect MATLAB up with live data that is being published and subscribed to on DDS. With MATLAB being one of the top tools used to analyze data and DDS quickly becoming the data communications middleware of IIoT applications, this integration will enable some very rapid prototyping and test analysis for developers. This blog post will walk through a few examples of how to publish DDS data and also how to subscribe to DDS data using MATLAB.
To say that the task of selecting your Industrial IoT (IIoT) communication infrastructure is a very complex undertaking would be an understatement. The evaluation of the myriad of commercially available solutions is both time consuming and expensive. Try downloading and evaluating multiple solutions of each type of infrastructure and you will quickly find yourself in the midst of a project that will take several engineers a good six months to complete. We’ve all been there, and I want to help you save yourself some valuable time!
I can't believe I have been at RTI for more than 12 years now! In that time, I have seen the evolution of the OMG Data Distribution Service from its early days, as well as the realization of RTI's mission to create the best DDS implementation available.
One of the benefits of the Industrial Internet for manufacturers and OEM's is the access to live data for more and more applications. This data availability enables performance analytics, more robust business metrics, predictive maintenance analysis, and various other capabilities to be realized by end users and equipment manufacturers alike. In the enterprise, Web Services dominate the typical access to data. On the manufacturing floor or in deployed systems, real-time data delivery is a requirement. DDS is one of the primary infrastructures used to share large amounts of data in very low latency delivery times.
One of the primary use cases for the IIoT (Industrial Internet of Things) is to collect sensor data and deliver that to an enterprise cloud for enhanced real-time visibility in to remote operational systems. This is very important for applications such as Oil & Gas, Manufacturing Plant Production monitoring, Healthcare Patient Monitoring and Power Substation Monitoring. With advances in network infrastructure and the promise of higher bandwidth WAN (Wide Area Network) connections, the ability to pull raw sensor data across the WAN to a backend enterprise cloud where data processing and predictive maintenance solutions can be implemented, and monitored. Enabling this type of architecture provides great agility for organizations to respond and react to changing conditions for their deployed systems.