The Complexities of Scaling IoT Projects
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 IoT offers to improve and scale their business, but they do not know how to undergo a digital transformation quickly and in a cost-effective manner.
The question today isn’t when or if an organization will deploy an IoT project – the question is how big will the project become? How much data does the organization need to collect and from how many edge devices? Will that number grow and change over time?
Many companies lack the in-house expertise to answer these questions sufficiently. As organizations look at IoT solutions there are several considerations that are often overlooked, and IoT scalability is the most common. An IoT solution must be able to handle a large number of connected devices and scale as the business requires with increased reliability. The solution should be future-proof, allowing for change without requiring a whole new system.
Strategies for scaling IoT Projects
There are several considerations for scaling distributed IoT solutions including:
- gateway device management,
- industrial driver and connectivity management,
- edge processing and filtering of data at scale
- a centralized dashboard for a complete view and management of data and devices,
- running analytics applications at scale at the Edge and Cloud,
- achieving cloud connectivity at scale,
- and security management.
Another specific challenge when scaling an IoT project is figuring out how to make sense out of the data by running various applications at the edge. Organizations need a mechanism to install, update and manage applications for a large number of nodes at once. Having the ability to run complex analytics, anomaly detection and machine learning tools at the edge is extremely beneficial for saving cloud and bandwidth costs for an IoT implementation. It is nearly impossible to do this kind of analysis without having a mechanism to control and manage this process for a large number of gateways at once.
A centralized portal or management interface is the key to managing devices, security and data collection for the many different types of legacy industrial devices out in the field. There must be a complete management UI that encompasses the ability to seamless connect to a wide number of PLCs, CNCs and robotic systems via edge gateways, with the ability to manage devices and deploy applications and analytics at the edge.
A Flexible Platform
The only way to successfully implement an IoT project that scales and allows the enterprise to incorporate new types of information is to use a flexible platform that can adapt to the future. Without such platform adaptability, application development means a lot of headaches. Companies risk being stuck with a static system, which forces them to constantly invest resources—significantly, time—in developing their next generation IoT products and services.
There is a strategic dimension to developing adaptive IoT products. Applying good IoT techniques and design principles will probably generate revenue value-added services that will likely generate revenue, but for enterprises to have the biggest impact they will have to leverage the network effects of a big-data ecosystem, either pre-existing or self-created. The risks of not doing so, and the rewards of achieving this, should be apparent.
IoT 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 back-end, 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, which includes local applications for data storage, complex event processing, analytics, data filtering, cloud connectivity and overall management of the solution and deployment.
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. Also key to a successful implementation is the ability to manage edge devices remotely from the cloud, and store data in a central location for analytics and visualization.
Industrial IoT middleware simplifies the process, using one software 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 modern and flexible way.
This article was written by John Younes, the Co-Founder & Chief Operating Officer at Litmus Automation. He is in charge of operations and growth for the company and draws on considerable experience working with start-ups and early stage companies.