Shulou(Shulou.com)06/01 Report--

In this issue, the editor will bring you about the solution of AWS IoT. The article is rich in content and analyzes and narrates it from a professional point of view. I hope you can get something after reading this article.

With the proliferation of Internet of things devices, enterprises need a solution to collect, store and analyze the data of their devices. Amazon Web Services provides some useful tools for designing powerful data pipelines for IoT devices.

The Internet of things (IoT) provides every industry with an unparalleled opportunity to meet its business challenges. With the increase of devices, people need a solution to connect, collect, store and analyze the data of the devices. Amazon Web Services provides a variety of services to help connected devices easily and securely interact with cloud applications and other devices to adapt to a variety of user scenarios. Having said that, every solution architect in this area knows the functionality and reliability of AWS Cloud. Migrating or designing Internet of things (IoT) solutions on the AWS platform allows people to focus on their core business without the hassle of infrastructure management and monitoring. This will ensure high availability for the customer. No matter which solution is designed, you should choose the best platform to maintain the stability of the solution. AWS is such a platform.

There are some practices to consider when designing IoT solutions using AWS. If the right AWS services are used for customer requirements, the IoT solution will be able to deliver results in a more secure, reliable, and scalable manner.

Design for reliable large-scale operation

The Internet of things system must deal with the high-speed and large-capacity data captured by devices and gateways. The overflow of incoming data may be caused by sudden growth of business or sometimes due to malicious attacks. In this case, the cloud system architecture should be extensible to handle such data.

The best way is to send the data to queues and buffers in a real-time in-memory database before storing it. This helps to implement real-time events and reduce the rate of data insertion to prevent database crashes or slow responses.

Devices can publish data to AWS Kinesis, or they can use AWS IoT rules to forward data to AWS SQS and Kinesis to store it in time series stores, such as AWS S3 Magi Redshiftje data Lake or Elastic search data stores. These data stores can be used to generate custom dashboards or AWS Quick Sight dashboards.

Route a large amount of data through a data tube

Consuming incoming data from device topics directly to a single service prevents the system from achieving full scalability. Sometimes, this approach limits the availability of the system in the event of failures and data flooding.

The AWS IoT rules engine is designed to connect endpoints to the AWS IoT Core in an extensible manner. However, all AWS services have different data flow properties and have their own advantages and disadvantages. All services cannot be used as a single entry point to the system. Sometimes, it can cause subsequent failures and cannot be recovered. For example, for bulk data, consider buffering (Elasti Cache) or queuing (SQS) incoming data before invoking other services, which allows you to recover from subsequent failures.

The AWS IoT rules engine allows multiple AWS services, such as Lambda,S3,Kinesis,SQS or SNS, to be triggered in parallel. After the Internet of things system captures the data, it will enable AWS terminal nodes (other AWS services) to process and transform the data. This allows you to store data in multiple data stores at the same time. The safest way to ensure that all data is processed and stored is to redirect all device subject data to SNS, which is used to handle data flooding to ensure that incoming data is reliably maintained, processed, and delivered to the correct channel. To make it more scalable, you can use multiple AWS topics, SQS queues and Lambda for different / group SNS device topics. Before processing data, consider storing it in secure storage such as queues, Amazon Kinesis,Amazon S3, or Amazon Redshift. This approach ensures that data is not lost due to flooding of messages, unwanted exception code, or deployment problems.

Automate equipment configuration and upgrade

As the business grows and many devices connect to the IoT ecosystem, manual operations such as device configuration, boot software, security configuration, rule action settings, and device OTA upgrades are not feasible. Minimizing human-computer interaction during initialization and upgrade is important to save time and reduce costs.

Designing built-in functions within the device for automatic configuration and leveraging the appropriate tools provided by AWS to handle device configuration and management enables the system to achieve the desired operational efficiency with minimal human intervention.

AWS IoT provides a set of features for batch imports with a set of policies that can be integrated with dashboards and manufacturing processes where devices can be pre-registered to AWS IoT and certificates can be installed on devices. The device provisioning process can then declare a device and attach it to another user or any other entity. AWS provides the ability to trigger and track device OTA upgrades.

Adopt an extensible architecture for custom components

When an Internet of things system is connected to an external device, the scope does not end by connecting, controlling, and reporting devices. Consider adopting the latest technologies such as data science and machine learning, or integrating third-party components into Internet of things systems such as IFTTT,Alexa or Google Home. The architecture of the Internet of things should ensure that external components can be easily integrated into the solution without performance bottlenecks.

Check offline access and processing

Sometimes, you don't have to process data for all devices in the cloud. In many cases, there is no continuous Internet connection available. For this case, add an AWS Greengrass to the edge. Greengrass processes and filters data locally at the edge and reduces the need to send all device data upstream. You can capture all data, keep it for a limited period of time, and then send it to the cloud based on error events or on-demand / request. If you need time series data, you can schedule a regular process to send device data to the cloud, which can be used for future enhancements such as AWS Machine Learning models and cloud analysis tools.

Select the correct data store

The Internet of things system generates high-speed, large capacity and a variety of data. Each Internet of things device or device theme can have different formats that may not be managed through a single database or similar type of data store. Architects should be careful when choosing database formats and data stores. Frequently used static data can be stored in the Elastic cache, which helps improve performance. This helps to achieve the scalability and maintainability of the system.

Filter and transform data before processing

All data input to the Internet of things system may need to be processed or converted and then redirected to storage. AWS IoT rules provide operations to redirect messages to different AWS services. The architect should divide all data into different forms (that is, processing, ignoring / static data (such as configuration), and direct storage).

AWS IoT service architecture

This is what the AWS IoT solution is like. If you happen to have similar doubts, you might as well refer to the above analysis to understand. If you want to know more about it, you are welcome to follow the industry information channel.

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