Addressing The Productivity Gaps In IIoT

While IIoT shows much promise, the current state of SDKs could pose a major problem.


The Industrial Internet of Things (IIoT) can be enabled by many different topologies and permutations of underlying infrastructure, but the one constant is that it brings new levels of profitability, efficiency, and productivity to businesses and industries near and far.

The IIoT is enabled by connectivity from device to device, and device to and from the cloud. However, it’s not only about how these devices connect to each other in a secure fashion, but also how they collect, aggregate, process, and send data up and down – and across – the IIoT infrastructure.

Figure 1: As the IIoT evolves, connected devices and their associated systems are becoming extremely complex – which often means multiple cloud vendors might share the same IIoT infrastructure.

In my previous article, A New Approach to a Fragmented Industrial IoT, I discussed how with so much complexity, the industry has become somewhat fragmented. I touched on a few considerations that might help if we are to coalesce and move forward with the next generation of IIoT ecosystems.

And while the IIoT shows much promise, there is yet another issue that must be brought to light. It stems primarily from what cloud providers offer their customers by way of software development kits (SDKs).

According to Gartner (September, 2017) the top three worldwide cloud providers are Amazon Web Services, Microsoft Azure, and Alibaba in the Infrastructure as a Service (IaaS) public cloud market. These are just the top providers, and many others exist. So there are a significant number of cloud vendors building compelling, but different solutions to enable IIoT. These vendors use standard communication protocols, which are supported in their embedded software development kits (SDKs).

The cloud-vendor SDKs are equipped to access the necessary services at the cloud backend, but typically do not inherently provide all of the monitoring or management features for a gateway or edge device where various assets need to be connected and managed. There tends to be even less support for the management of smaller end-node devices. So in many cases, developers or IIoT system architects have no choice but to extend the SDK with additional management and application deployment features.

It’s here where the “productivity gaps” appear. If we look closer, we find two distinct types:

IoT Capability Gap: To varying degrees, cloud vendors invest to enable connected devices to take advantage of their cloud backend features. The investments range from just an embedded library of connectivity protocols, to enabling limited capabilities for gathering telemetry data or initiating software updates. For most IIoT systems, the desired capability includes all of the above and more. However, the various cloud backend solutions only provide a portion of the needs to satisfy the desired end-device capability. The difference is called the IoT capability gap.

Device Implementation Gap: Not surprisingly, cloud providers do not focus their investments to broadly enable the specific runtime environments of all types of devices to connect to the cloud backend. How could they when connected devices range from simple, low-cost, single-purpose devices, to highly complex devices such as smart edge gateways capable of executing machine learning and artificial intelligence? These devices might run on a proprietary OS, an RTOS, or Linux. The required features of a given device determine how it will be implemented on a specific embedded platform. This includes integrating SDK hooks to the platform, implementing specific boot strategies for secure software updates, and instrumenting these devices to provide critical system health monitoring and diagnostic data – just to name a few instances. The device implementation gap can consume a significant amount of engineering and testing resources.

Figure 2: The IoT capability gap is the difference between what a business requires and what the cloud vendor SDK enables within an IoT device. The device implementation gap is the work required to integrate the device SDK onto a specific hardware platform which is an active participant at the edge or end node of an IIoT infrastructure.

Needless to say, business executives, software architects, and end-device manufacturers are all looking for more efficient ways to address these productivity gaps.

Filling the gaps
Mentor has just announced a new framework approach that addresses IIoT fragmentation and the productivity gaps (Figure 3). The new Mentor Embedded IoT Framework (MEIF) does not replace technologies and investments already provided by cloud vendors; rather, it fills the capability gap by complementing and extending those technologies, and fills the implementation gap by integrating the features fully with edge or end node device platforms. The new Mentor framework is both cloud and OS independent.

Figure 3: The Mentor Embedded IoT Framework complements and extends cloud vendor SDKs and enables integration and portability to the underlying device platform.

If we look closer, the MEIF design enables integration of cloud-vendor provided embedded SDKs (dark grey in Figure 3) alongside a well-defined set of IIoT-enabling runtime software (light blue in Figure 3), which can be extended as needed. Through this framework, users can connect all of their devices throughout the IIoT infrastructure in a secure, scalable manner. Costs are minimized when it comes to learning, implementing, and otherwise enabling smart devices from powerful gateways to smart sensors on the edge.

With the regular season of major league baseball now underway, it makes sense to say we are in the second inning of IIoT development and functionality. (What would you say?) There is still a lot of game left. Of course, in these early innings there’s bound to be a few missteps. Device manufacturers and the associated players are faced with challenges related to device management, unknown/multiple clouds, portability, scalability, and the need to remotely monitor and diagnose their devices.

But when it comes to fragmentation and the productivity gaps, the Mentor IoT Framework smashes it out of the ballpark (to continue the metaphor one last time) as it complements and extends the investments already made by cloud backend vendors to provide comprehensive IIoT features and capabilities – from the cloud right down to the hardware of an edge or end node device.

For a more detailed explanation of the Mentor Embedded IoT Framework and our involvement in IIoT technologies, please visit the Mentor Industrie 4.0 website.

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