As IoT adoption and functionality advance, systems become more feature-rich, the number of connection points increases exponentially, and managing physical devices, data, and digital networks becomes more complex. Organizations will have to meet this complexity head-on in order to be able to leverage the benefits of IoT.

The pairing of IoT development and Model-Based Systems Engineering (MBSE) is growing in popularity as a way to successfully deliver IoT projects. MBSE is proving itself as a viable approach to IoT implementation because of its “Systems of Systems” approach.

Spending Quality Time with your IoT Project

At first glance, Model-Based Systems Engineering might seem like an overly-complex methodology for product teams to deliver their IoT projects as fast as possible. And yes, initially this approach might take longer than a more informal one. The catch is that rushing a product to market quickly can often lead to its ultimate demise through unforeseen defects and customer disappointment.

Moreover, while a first project using MBSE might seem to require more time, following projects are likely to progress even faster than before. Deployment times improve due to a better understanding of how to apply the principles, and thanks to the avoidance of unnecessary iterations, wasted resources, and having to deal with surprise malfunctions.

It’s important to remember that IoT projects aren’t limited to gadgets that make our lives a little easier. Autonomous travel, smart medical devices and connected machinery could all have life-threatening consequences should errors occur in the IoT system and network. Taking the extra time to perfect and evaluate a system before launch is crucial in such cases.

What MBSE has to Offer IoT

By employing Model-Based Systems Engineering, an IoT system could be built, evaluated, maintained, and improved by addressing questions like these…

  • Has a model-based engineering approach been used to specify the IoT system’s requirements?
  • Does the IoT system functionality answer the user requirements?
  • Have the requirements been tested with a user group?
  • Is the IoT system’s testing automated? And, what is the best way to manage the IoT testing process?
  • Are design and functionality decisions guided by analysis and simulations?
  • Is the network architecture and overall design being verified using system-level analysis?

The above questions demonstrate the fact that MBSE points to a data-driven evaluation and decision-making process.

This approach places a lot of emphasis on verifying a project across a number of domains by utilizing software, hardware, and cyber-physical IoT simulations.

Creating a Digital Twin

An exciting outcome of using Model-Based Systems Engineering for IoT is the creation of a “Digital Twin”. A digital twin is a customized high-resolution digital model that works in parallel to an actual system.

For example, a manufacturing plant could have a digital twin that precisely reproduces the dynamics of all the systems within the plant. Every process in the plant from raw materials processing and production to quality control and delivery can be monitored and repeated in the digital twin in real time. This allows for risk-free experimentation – through simulation, processes can be modified to any extent, and the results can be measured and compared.

Model-Based Systems Engineering for IoT

As with all new technologies, there is no golden rule or single tried-and-true method for integrating IoT into a product or manufacturing process. Each use case is different, and different approaches will have to be considered along with a good amount of customization and testing.

Model-Based Systems Engineering is an established engineering discipline, which means it offers a defined pool of professionals with a specific skill set. As a framework with its own principles and solutions, and because of its data-driven approach, Model-Based Systems Engineering could be extremely useful in successfully delivering complex IoT systems.