February 02, 2018

Overcoming Industrial IoT Challenges to Better Use Data from the Edge

By Special Guest
John Younes, co-Founder and COO, Litmus Automation

As the Internet of Things has come of age over the past few years, consumer applications have often taken center stage. IoT can offer more value, however, in industrial applications by using data from the edge to solve manufacturing problems, improve operations, and make predictive maintenance a reality.

Analyst firm IDC predicts that by 2019, 45 percent of IoT-created data will be stored, processed, analyzed and acted upon close to or at the edge of the network. The challenge is that many companies want to take advantage of the value Industrial IoT offers to improve their business, but they do not know how to undergo a digital transformation without getting rid of existing legacy equipment.

The Complexities of Industrial IoT
Several challenges await those who want to embrace Industrial IoT but don’t think things through carefully – challenges for which traditional IoT platforms do not have effective solutions. In a recent report Forrester said IoT is “a complex technology that defies simple approaches,” pointing out that enterprises have been using simple sensors for years but that a plethora of communications and software technologies, protocols, and standards makes it extremely difficult to take data from the edge and apply it to the backend application.

As endorsed by Forrester, the first challenge firms working to embrace Industrial IoT will find is figuring out how the solution will communicate with all of the different legacy controllers and devices found on the factory floor. They all utilize one protocol or another for communication, and data formats will not be standard across the different pieces of equipment. There isn’t a standardized approach for connecting industrial devices, and there isn’t going to be one anytime soon.

The second challenge, and quite possibly the most important one, is security. There are many possible vulnerabilities in IoT solutions. The six main levels of security that need to be implemented are:

  • connecting to devices,
  • transporting data,
  • isolating devices,
  • handling data-at-rest,
  • sending commands and controlling devices, and
  • updating systems.

Without properly addressing all of these vulnerabilities, any IoT implementation is at risk. It is dangerous to implement an IoT platform without properly vetting its security features, as many do not have ways to securely address each one of these issues.

The third challenge is management and deployment of an IoT solution and how to scale distributed solutions. Without a centralized portal or management interface, it is extremely difficult to manage devices, security, and data collection for the many different types of industrial devices out in the field. There must be a complete management UI that encompasses the ability to host drivers for connectivity via edge gateways, with the ability to manage devices and deploy applications and analytics at the edge.

The fourth challenge is figuring out how to make sense out of the data by running various applications at the edge. You need to figure out a mechanism to install, update, and manage applications for a large number of nodes at once. Having the ability to run applications like complex analytics, anomaly detection, and machine learning tools at the edge is extremely beneficial for saving cloud and bandwidth costs for an IoT implementation. Without having a mechanism to control and manage this process for a large number of gateways at once, it becomes nearly impossible to do this kind of analysis.

IIoT Platforms Fill the Gap
Platforms are largely accepted as the solution to these IIoT challenges for their simplicity, built-in security, and interoperability with varied legacy devices. IIoT platforms and middleware fill the gap between old and modern equipment – lying between physical devices and the end user software application such as predictive analytics.

Once industrial firms decide exactly what data they want to capture and how they will use it on the backend, they should look for a platform that matches up with their needs. For instance, they should choose a middleware that can be installed on their gateway or industrial PC of choice and then can connect to the application desired such as a local data store, complex event processing engine, analytics, data filtering and cloud connectivity.

The ideal IIoT platform can speak the many languages of IIoT protocols.  An existing library of downloadable applications enabling gateway-based processing without any extra coding can save a lot of time and money. The ability to manage edge devices remotely and integrate with any number of IoT cloud-based platforms is also key to a successful implementation.

Industrial IoT middleware simplifies the process, using one software solution to manage a marketplace of applications and industrial drivers to enable gateways to interact with machines and legacy equipment. With a secure edge-level solution to connect to nearly all industrial devices and systems, manufacturers can liberate, process, and integrate the data from the factory floor into the cloud or on-premises enterprise systems in a new way.

About the author: John Younes is co-Founder and COO at Litmus Automation (www.litmusautomation.com).

Edited by Ken Briodagh

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