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IoT device management challenges

IoT Device Management: Overcome These Challenges with IoT Platforms

The Industrial Internet of Things (IIoT) holds the promise of a fully automated future of instant insight. As a recent phenomenon, the emerging paradigm of the industrial IoT platform has become the epitome of innovation and streamlining. And an industrial IoT platform effectively overcomes typical IoT device management challenges.

One such platform involves the integration of thousands of industrial IoT devices and maintains connectivity to the network. It handles the efficient management of endpoints and enterprise applications in production. And it takes over the control and monitoring of the various components of the IIoT ecosystem. The goal is the seamless flow of data between connected entities. An IIoT platform is thus a bundled offering incorporating IoT devices, data, processes, and ecosystem integrations. 

Data-driven organizations have a competitive advantage when leveraging the power of IIoT platforms to the fullest. Still, to get there, you need to start from the beginning. You start from the management of your IoT devices as part of an IIoT orchestration platform.  

According to a recent definition by,

“device management refers to all of the tools, capabilities, and processes necessary to support IoT solutions effectively at scale.” Device management spans the entire life cycle of IIoT assets. It encompasses tasks such as “onboarding new devices, automatically identifying device issues, classifying devices into states … and decommissioning old devices.” 

How to approach IoT device management: considering the big picture

Once you have covered the fundamental concepts of IoT device management, you can start thinking about the specifics of your singular, custom device management solution. A viable device management solution is holistic, adopting a big-picture and a full-circle approach. It starts from app development all the way up to device decommissioning as part of IoT lifecycle management.

At the same time, a fully rounded device management solution is contextual. It considers concrete on-premises realities and the pain points of your organization. 

Ideally, your device management solution will equip you to navigate through a heterogeneous industrial landscape. That is, you will be able to handle legacy machines and manage them through their remaining life cycles, integrate new IoT devices, create device groups, and ensure continual connectivity within an evolving IIoT setting. The last point alludes to another feature of a competitive device management solution. It is responsive both in terms of what happens on the shop floor and in terms of future developments.

Facing two typical challenges to IIoT device management

The classic challenges that companies face remain the heterogeneous device landscape and scalability.

Multi-level brownfield 

High heterogeneity remains the biggest challenge to IIoT networks and their connectivity capabilities. This heterogeneity applies to the historically diverse assets of an organization. These often involve old industrial machines and various types of analog equipment. This means machines belonging to different generations and equipped with widely varying capabilities.

Added to the diversity of legacy assets is the sheer diversity of vendor-specific IoT devices. Here you have to deal with the idiosyncrasies of IoT devices built by different manufacturers. But then again, heterogeneity applies not only to the level of old machines and vendor-specific IoT devices. It also applies to the level of tasks and resources. In terms of task management, you need to be able to seamlessly deploy the right app to your various IoT devices in real-time, allocate resources to functions, and monitor device status.  

What does this mean for an IIoT platform? Any new form of software architecture must take into account and coexist with IoT devices already in use and handle these IoT devices reliably. This is especially valid within the current innovation landscape where we have many companies willing to welcome digitalization. These companies are already transitioning to Industry 4.0 but continue to operate within an environment of legacy assets.

As transitions are never easy and full digitalization remains an ongoing effort, companies need to develop foresight and implement device management solutions that do justice to both existing legacy equipment and an expanding fleet of new IoT devices with front-line connection mechanisms. 

Massive-scale IIoT

The sheer number of connected IoT devices can also become an issue if you have a solution that encounters difficulties when scaling out. If your initial pilot project had included only a small number of IoT devices, you cannot simply infer that your IoT solution will continue to work when you begin to scale out indefinitely. There, you may encounter completely new IoT device management challenges.

And, apart from getting ready for heterogeneous and globally dispersed connected ecosystems, you need to spare a thought on the management of bulk IoT devices. Once you start scaling up, you will have to deploy complex rules on larger numbers of device groups. 

While you can begin with a small number of connected IoT devices, it is essential to retain the big picture in mind and think at least two steps ahead of your current situation. This mindset will prepare you for the times when you will have to perform massive scaling out on large batches of IoT devices in dynamic deployment scenarios. Your device management solution has to be ready for these times. 

The case of IIoT and edge device management

Given the recent trend towards increased data processing and analytics performed at the edge, device management solutions need to respond to this development in edge computing. In this way, users can continue to seamlessly generate value from their data and perform analytics to the fullest extent possible on the edge of an IoT network. In a fully automated setting, IoT devices at the edge, located close to the data source, are the first guard of defense against machine downtimes. 

What IIoT edge capabilities do you need? A snapshot

A viable device management model will allow you to run intelligence as close to the data source as possible to adapt the streaming model dynamically and capture relevant data at the source. As opposed to the in-depth data analysis capabilities in the cloud that involve the consolidation of data from various non-local sources and powerful AI operations, data processing at the edge still has its limits.

Only local data can be processed at the edge; the storing and processing capabilities are small compared to the capabilities of the cloud. But the closer we get to the data, the more we can tap into the capacity of IoT edge devices. We can make use of smart filtering before the data gets to the cloud. To achieve all of this, you need highly efficient remote asset maintenance as part of device management. 

Remote configuration is also part of the picture. You need to be able to adjust configurations in real-time, and/or update and re-deploy regularly. Many of these actions will not apply to individual assets only but will have to be implemented on a massive scale, at regular intervals. An IIoT device management solution that can carry this complexity can make all the difference in such settings.

A unified device management platform with an integrated device management studio helps you to achieve this efficiency. The IoT development platform with integrated app development and device management functionalities—interoperable and integratable with a variety of open-source tools— makes it possible to face these and similar IoT device management challenges. 

Handling device settings, connectivity, and security

Apart from the infrastructural and IoT device management challenges we have discussed already, industrial manufacturers also face difficulties on the device settings level, as well as the connectivity and security levels. How does the Record Evolution platform help you here?

  • Managing device settings. You can observe IoT devices and manage device settings according to the needs of the IoT environment.
  • Remote management of connectivity settings. Connectivity can be controlled by employing the same mechanisms as those used in device management. You can utilize remote management to alter connectivity settings on the fly. 
  • Deploying over the air. When you are ready to roll out your application on a group of IoT devices, you can simply create a deployment.
  •  Network quality. Data streaming is not always guaranteed to be top-notch. To overcome this, you can work with local buffers. This also involves an optimal streaming model and the implementation of such models via automated parameter selection.
  • Network security. The risk of intrusions or attacks when harvesting from multiple data sources cannot be underestimated. Local governments and intra-governmental bodies are in the process of introducing new legislation addressing IoT products. You need to ensure that your products are fully compliant. 

In an industrial environment, where IoT is embedded as part of the production process, you are constantly enmeshed in dynamic yet automated processes where IoT deployments or updates must not interrupt the production. You need to build a deployment mechanism so that new functionalities can be built and run, and new IoT devices can be added to the network.

Your device management/deployment method should be flexible enough to implement the app registry locally or in the cloud. But it should also address security issues such as intrusion and intervention prevention. 

Use your own IoT development environment

On the Record Evolution platform, you have a development environment with the following constituents, actors, and/or capabilities:

  • Device configuration. IoT devices are easy to set up. New IoT devices and machines can be seamlessly connected to the messaging infrastructure and offer functionality (two-way communication), authentication methods to secure the devices (IP hacking), over-the-air updates, sync of IoT devices, offline-mode buffering, registration of IoT devices, intelligence at the edge, development of new IoT devices, etc.
  • App development suite. Developers can write apps, deploy live code, and access the applications running on IoT devices for updates and maintenance. 
  • Deployment of custom apps. Applications have complex components and these components are built as microservices in containers. A container is like a virtual machine as it can stack up to build a complex app. A well-known application that provides containers is Docker. Container technologies are flexible in versioning.
  • Device management studio. Handles the connectivity and grouping of devices as part of task management. Facilitates the handling of typical IoT device management challenges. 
  • Built-in expertise. The Record Evolution platform thrives on years of expertise gathered in cross-industry settings and encapsulates competencies in embedded programming, data engineering, data science, backend and frontend development. 

You have an orchestration hub for IoT devices and device lifecycle management, can build your own IoT solution by writing apps (live coding) right on the platform, and deploy over the air on multiple IoT devices and device groups.

When integrating data science capabilities, the IoT platform additionally allows you to unify IoT data from multiple sources and highly heterogeneous IoT devices, combine the data on the platform, enrich it with other information, perform both instant (at the edge) and in-depth (in the cloud) data processing and analytics, and generate reports and visualizations based on these analyses. 

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About Record Evolution

We are a data science and IoT team based in Frankfurt, Germany, that helps companies of all sizes innovate at scale. That’s why we’ve developed an easy-to-use industrial IoT platform that enables fast development cycles and allows everyone to benefit from the possibilities of IoT and AI.