IoT Design Patterns – Edge Code Deployment Pattern

Development with traditional software architectures and IoT architectures is quite different, particularly when it comes to the design patterns. To put it clearly, the detail and abstraction levels in IoT design patterns vary. Therefore, for creating high-quality IoT systems, it is important to assess various layers of design patterns in the internet of things.

Design patterns are used to add robustness and allow abstraction to create reusable systems. Keep in mind that design involves working with technical information and creativity along with scientific principles for a machine, structure, or system to execute pre-defined functions, enabling maximum efficiency and economy.

IoT helps to integrate the design patterns of both the software and hardware. A properly-designed application in internet o things can comprise of microservices, edge devices, and cloud gateway for establishing a connection between the Internet and the edge network so the users are able to communicate via IoT devices.

Development for configuring systems and linking IoT devices come with an increased level of complexity. Design patterns in IoT tackle issues to manage edge applications starting from initialization and going towards deployment. In developing IoT applications, edge deployment pattern is one of the most commonly used design patterns.

Edge Code Deployment Pattern


How to make sure the developers are able to deploy code bases for several IoT devices and ensure that they achieve the required speed. Similarly, there are also concerns regarding the security loopholes of the application. Additionally, it includes how to configure IOT devices while not worrying about the time-consuming phases of build, deployment, test, and release.


One of the primary factors for deploying a portion code is maintainability for deploying IoT devices which are remotely based. When developers resolve bugs and enhance the code, they want to add to ensure that this updated code is deployed to its corresponding IoT devices as soon as possible. This assists with the distribution of functionality across IoT devices. After some time, the developers may be required to perform reconfiguration of the application environment.

Let’s consider that you are working on an IoT system which uses billboards to show advertisements in a specific area. Throughout the day, your requirements include modifying the graphical and textual elements on the screen. In such a case, adaptability and maintainability emerge as two of the toughest challenges where the developers are required to update and deploy the code to all the corresponding IoT devices simultaneously.

Often networking connectivity slows down the internet of things ecosystem. To combat this dilemma, you can incorporate the relevant changes rather than proceeding the complete application upload with a network that is already struggling to maintain performance.

Developers are required to consider programming as their primary priority and search for the right tools which can assist with their development. It is important to ensure that the IoT devices’ code deployment tools remain transparent so it can help the developers. This can assist in achieving a deployment environment that is fully automated.

Subsequently, the safety of operations is also improved. As discussed before, your development pipeline should be aimed at building, deploying, testing, releasing and distributing the application for the devices in the internet of things ecosystem.

For testing, you can use the generated image with an environment that resembles the production environment and initiate your testing. After the completion of the tests, the IoT devices take out the relevant image. This image is derived out from the configuration files, the specification of the container, and the entire code. One more aspect is that there is a need for a mechanism which can help coders to rollback their deployment to a prior version so they do not have to deal with outage—something which is quite important for the IoT devices.

Additionally, for the new code requirements, the deployment stage has to contain and consider configurations and dependencies of the software. For this purpose, they need reconfiguration for the environment of the application and the entire tech stack via safely and remotely to maintain consistency.


Since in recent times developers are extensively using version control systems, it can help with deployments as well. Today, Git is heavily used by developers for maintaining versioning and sharing code. Git can serve as the initial point for triggering the building system and proceed with the deployment phase.

Developers can use Git for pushing a certain code branch at the server’s Git repository which is remotely based so it gets alerts about the software’s new version. Afterward, they can use hooks for triggering the build system initiating the following phase where they can deploy the code in the devices. A new Docker image is built by the build server which adds the image’s newly-created layers. They are received by the central hub of Docker so the devices can pull. With this strategy, a developer can use any version control system like Git to deploy code when the IoT devices are distributed geographically.

After regular intervals, these devices can inquire the central hub or registry to see if there are any new versions. Similarly, the server itself can alert the devices of a new image version release. Afterward, the newly-created image layers are pulled by the IoT devices where they generate a container and make use of the new code.

To summarize, after there are changes in the code, Git is used for commits and pushes. Subsequently, a freshly-built Docker image is created and moves to the IoT devices which then makes use of the image for generating a container and utilizing the code that has been delayed. Each commit starts the deployment pipeline and modifies the source code—thereby it is published for all the devices.


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