The article will discuss:
- The challenges with managing edge devices
- Edge device management and the role of industrial cloud services
- Edge device management and how Industrial IoT platforms help
The need for real-time processing or processing at the speed of thought is the foundation behind edge computing. Edge computing delivers low latency processing in which data capture and analytics happen close to the data source. In this comprehensive post on edge computing, the role of edge devices and edge networks are defined. It also highlights the fact that millions of edge devices are currently in use today and, with the limited computing resources of each device, the question of edge device management is one that must be answered.
Edge device management and its challenges
By 2025, 75 billion edge devices are expected to be in operation across the manufacturing industry, healthcare, finance, and domestic settings. Each device is expected to produce, capture or analyze data. In some cases, edge devices are expected to accomplish all three objectives as well as transfer data to centralized data centers or the cloud.
Although these devices are capable of managing the data they capture according to their software specifications, they’re also expected to be a part of a larger, interconnected ecosystem. This ecosystem provides enterprises and domestic users with visibility into entire processes. For example, a home automation system made up of multiple edge devices that monitor and control lighting, window blinds, temperature, etc. is capable of edge computing. As individual devices, they handle specific processing tasks but as an interconnected system, more computing power is needed for the edge device management.
Lack of scalable computing power is one challenge edge devices face when edge device management is considered. Other challenges include ensuring edge devices are secure, especially for devices that connect to external data centers, and scalability issues. For smaller edge devices, the lack of viewing screens limits visibility into the work they do. Although they may function optimally, some form of data visibility is required when their performance affects an entire system.
These challenges show that edge device management requires an all-encompassing solution that brings scalability, visibility, and flexibility to the table. The 360-degree view into edge device performance then provides real insight into an entire enterprise network or the home automation system being used.
Edge device management and the industrial cloud
Managing edge devices refers to being able to visualize the performance of every single device on a network regardless of how small the device is. It also refers to the ability to run diagnostic checks, secure, upgrade, and receive information from edge devices. This is where industrial cloud solutions come into the picture for manufacturing outfits.
The manufacturing industry is expected to drive growth in the use of edge devices, and having a dedicated edge device management solution to manage the terabytes of data these devices produce is a necessity. The industrial cloud is that solution. Industrial cloud platforms provide the scalability and computing resources needed to manage the large data sets IIoT and edge devices collect.
Industrial cloud solutions also provide visuals into the functionality of these devices and eliminate the need for employing trial and error methods to pinpoint faulty devices. With the industrial cloud, finding the proverbial needle in a haystack of tens of interconnected devices becomes a simplified process.
One major benefit of using industrial cloud solutions for IIoT devices is the availability of inbuilt edge applications that support specific industrial activities. In many cases, these applications do not function appropriately on public cloud platforms because they are built solely for industrial cloud platforms. Industrial cloud platforms also provide subscription-based services which can prove to be economical in the long run. The flexibility in pricing and scaling up computing needs makes subscribing a preferred option for many enterprises compared to developing DIY cloud solutions.
Industrial IoT platforms and edge device management
Industrial IoT platforms take edge device management to the next level as they integrate the use of artificial intelligence and machine learning algorithms for edge device management. With these algorithms, extensive data analysis and data-driven insights that enhance the use of edge devices and shop floor equipment can be achieved. One example is executing overall equipment efficiency (OEE) or total effective equipment performance (TEEP) calculations automatically and on a regular basis.
IoT platforms also provide communication workflows for edge device management. These workflows provide deeper insight into connected environments and showcase the effects of one process or even its effects on another or on the ecosystem. Communication workflows also deliver alerts and notifications that help decision-makers optimize edge computing processes. The visualization IoT platforms bring to edge device management also makes comparing shop floor data against benchmark data possible.
This process involves collecting data from diverse facilities to create an optimal benchmark of data. Data collected from shop floors with similar assets can then compare their productivity levels to the benchmarked data. The insight the comparison provides is then used to optimize asset utilization. It can also be used to highlight other shortcomings within production cycles. These shortcomings can be as extensive as a poor material handling system or as minute as excess spent in toilet breaks by shop floor operators.
IoT platforms also serve as Software as a Service platforms (SaaS). They provide the APIs, algorithms, and repositories enterprises can leverage to build edge applications. Once built, these applications can be deployed within edge devices, edge networks, or on the cloud depending on what they were built to accomplish.
Lastly, IoT platforms leverage the scalability and flexibility of cloud computing. This ensures enterprises scale up their edge device management requirements only when more devices or data are produced. This also reduces the cost of edge device management when making use of an IoT platform.
Conclusion
When deploying edge computing within shop floors or other assets, edge device management is an important aspect an enterprise must consider. The choice of an edge device management platform should also consider how well the platform meets your industry’s specific needs. These needs revolve around building industry-specific applications, cybersecurity, scalability, and subscription cost.
Also, for enterprises building private edge device management platforms, the total cost of managing the platform and the DIY project must be considered. While for SMEs interested in edge computing, a subscription plan to an industrial cloud platform or an IoT platform is the more affordable option for edge device management.
