Open Source IoT Platforms: A Comparison

As the Internet of Things (IoT) continues to grow, more and more companies and developers are looking to build connected devices and applications. One of the key decisions when embarking on an IoT project is which platform to use. There are a variety of open source IoT platforms available, each with their own strengths and drawbacks. In this article, I will compare some of the most popular open source IoT platforms to help you choose the right one for your needs.

Overview of Open Source IoT Platforms

Some of the most widely used open source IoT platforms include:

• Apache Mynewt – A real-time operating system for constrained devices like wearables or IoT sensors. It’s very lightweight and modular.

• RIOT – Another open source OS optimized for IoT devices with memory constraints. Focuses on security and real-time capabilities.

• Zephyr – Backed by Linux Foundation, Zephyr is a RTOS aimed at connected resource-constrained devices. Supports Bluetooth, Wi-Fi and sub-GHz protocols.

• PlatformIO – Not an operating system, but an end-to-end platform with developer tools to build embedded and IoT applications. Integrates with popular IDEs.

• TensorFlow Lite – For running machine learning models on IoT edge devices. Library from Google provides pre-trained ML models for common tasks.

• AWS IoT – Managed cloud service from Amazon Web Services to connect and manage IoT devices at scale.

• Azure IoT – Microsoft’s cloud solution for IoT apps. Includes device management, data collection, analysis, and integration with other Azure services.

Key Differences and Comparison

While all aimed at IoT use cases, these open source platforms differ significantly in their architectures, capabilities, and target applications. Here is an overview of the key differences between the platforms:

• Operating System vs Platform – Some like Mynewt and RIOT are embedded real-time operating systems, while others like AWS IoT and Azure IoT are full-fledged cloud platforms. PlatformIO is a developer toolkit for building apps.

• Hardware Support – The RTOSes like Mynewt and Zephyr are optimized for low-powered microcontrollers. PlatformIO and cloud platforms support a wider range of hardware.

• Constrained vs Non-Constrained Devices – Apache Mynewt, RIOT targets constrained IoT devices while PlatformIO, TensorFlow Lite and cloud platforms are geared for more powerful hardware.

• Language and Tooling – Mynewt uses Newt tool and C/C++ languages. Zephyr uses CMake and PlatformIO has its own IDE. Cloud platforms have SDKs for many languages.

• Network Protocols – Most platforms support common wireless protocols like Bluetooth, Wi-Fi, LoRa. Some like Zephyr also support cellular and mesh networking.

• Cloud Integration – AWS IoT and Azure IoT have seamless integration with cloud services. Others have varying degrees of cloud support through third-party services.

• Machine Learning support – TensorFlow Lite and cloud platforms make it easy to deploy ML models on devices. Other platforms have limited or no native ML capabilities.

Detailed Comparison

Here is a more detailed overview of how these platforms compare to each other:

| Platform | OS/Type | Language | Hardware Support | ML Support | Cloud Integration |
|-|-|-|-|-|-|
| Apache Mynewt | Embedded OS | C/C++ | Microcontrollers, sensors | Minimal | Medium (Through third-party cloud services) |
| RIOT OS | Embedded OS | C/C++ | Low-power MCUs | None | Medium (Add-on packages) |
| Zephyr | Embedded RTOS | C/C++ | Microcontrollers | Minimal | Medium (Third-party integration) |
| PlatformIO | Developer framework | C/C++, Python, Rust | Microcontrollers, SOCs, SBCs | Minimal | High (Many cloud platform plugins) |
| TensorFlow Lite | ML framework | Python, C++ | Microcontrollers, phones, PCs | Excellent (Pre-trained models) | Medium (Requires custom cloud integration) |
| AWS IoT | Cloud platform | Wide variety through SDKs | Broad hardware support | Good (SageMaker integration) | Excellent (Fully managed platform) |
| Azure IoT | Cloud platform | Wide variety through SDKs | Broad hardware support | Good (ML Studio integration) | Excellent (Fully managed platform) |

Choosing the Right Platform

With this comparison in mind, here are some key considerations when choosing an open source IoT platform:

• For heavily resource-constrained devices like sensors and wearables, the embedded real-time OSes like Mynewt and RIOT are a good choice.

• For more powerful IoT devices and gateways, Zephyr, PlatformIO or TensorFlow Lite strike a good balance.

• If you need seamless cloud integration, predictive capabilities with ML, and scalability, the cloud platforms like AWS IoT and Azure IoT are preferable.

• Evaluate hardware compatibility, language preference, tooling requirements, and other technical factors.

• Prototyping on local devices or cloud platforms first is recommended, before picking a final production platform.

• Consider development costs, product roadmap, community support and documentation when choosing.

Conclusion

There are a wide variety of capable open source IoT platforms available today, from tiny RTOSes to fully managed cloud solutions. This article provided a high-level overview of the most popular options and their key differences. The right platform depends on your application requirements, target devices, skill sets and business objectives. Prototyping different options is recommended before committing to a platform for developing commercial IoT products.