Architecture selection
The optimal embedded software for a particular system varies based on the hardware footprint of the device. For example, network security protocols, while necessary for preserving data privacy and integrity, can have a relatively large RAM footprint. For intranet and internet connections, use TLS with a combination of a strong cipher suite and minimal footprint.
| IOTPERF01: How do your architectural decisions adapt to device hardware resources? |
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AWS IoT supports elliptic curve cryptography (ECC) for devices connecting to AWS IoT using TLS. A secure software and hardware system on device should take precedence during the selection criteria for your devices. AWS also has a number of IoT partners that provide hardware solutions that can securely integrate to AWS IoT.
In addition to selecting the right hardware partner, you might choose to use a number of software components to run your application logic on the device, including FreeRTOS and AWS IoT Greengrass. You can orchestrate native OS processes on specific hardware to improve performance and you also can run containerized workloads for isolation.
Defining and analyzing key performance metrics for your IoT applications helps you understand the performance characteristics for your application. Logging and end-to-end application monitoring are key for measuring, evaluating, and optimizing the performance of your IoT applications.
IOTPERF01-BP01 Optimize for device hardware resources utilization
When designing IoT solutions, it's crucial to consider the limited hardware resources available on edge devices, such as processing power, memory, and battery life. Optimizing resource utilization can significantly improve performance, efficiency, and overall device longevity. AWS offers several services and tools to help architects and developers optimize their solutions for device hardware constraints.
Level of risk exposed if this best practice is not established: High
Prescriptive guidance IOTPERF01-BP01-01 Apply efficient runtimes and code language for embedded devices.
When making architectural decisions, thoroughly evaluate the hardware capabilities of the target devices, and select appropriate AWS services and configurations to optimize resource utilization. This approach can lead to improved performance, extended battery life, and cost savings by reducing cloud processing and data transfer requirements. Some key tools to consider in your device components are:
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AWS IoT Device Client SDK: Provides lightweight, optimized libraries for various programming languages. These libraries enable efficient communication with AWS IoT Core and other AWS services, minimizing resource consumption on edge devices. For optimal performance, select based on your device constraints, prioritizing lower-level languages for battery-powered or resource-limited deployments:
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Embedded C SDK: Best for highly constrained devices with minimal RAM (256KB+). Offers lowest memory footprint and power consumption.
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C++ SDK: Balances performance with developer productivity for embedded Linux applications and gateways.
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Python, JavaScript, or Java SDKs: Choose only when device resources permit, as they trade performance for ease of development, in general.
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FreeRTOS: A real-time operating system for microcontrollers that is designed to be resource-efficient and highly configurable. It allows developers to tailor the OS to specific hardware requirements, reducing the overall footprint.
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For more complex scenarios, AWS IoT Greengrass is an open source edge runtime and cloud service that helps you build, deploy, and manage device software. It manages devices to act locally on the data they generate, run predictions based on machine learning models, filter and aggregate device data, and only transmit necessary information to the cloud. By processing data locally, AWS IoT Greengrass minimizes network latency and optimizes resource utilization, particularly for time-sensitive or bandwidth-constrained applications.
Prescriptive guidance IOTPERF01-BP01-02 Leverage edge gateways as hubs to bridge communications with the cloud.
Edge gateways act as intermediaries, aggregating data from multiple devices, performing local processing and filtering, and intelligently managing communication with the cloud. This approach offloads workloads from the cloud and reduces network traffic and latency. By implementing edge gateways with AWS IoT Greengrass, you can deploy AWS Lambda functions, machine learning models, and other application components directly on these gateways, enabling real-time processing and decision-making at the edge. This architecture not only enhances performance but also improves resilience by allowing continued operation during intermittent cloud connectivity, maintaining data integrity, and minimizing potential disruptions.
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