Design Best Practices With AWS IoT
Design Best Practices With AWS IoT
IoT presents an unparalleled opportunity for every industry to address their business challenges.
Join the DZone community and get the full member experience.Join For Free
Internet of Things (IoT) presents an unparalleled opportunity for every industry to address their business challenges. With the proliferation of devices, one needs a solution to connect, collect, store, and analyze the device data. Amazon Web Services provides various services that help connected devices to easily and securely interact with cloud applications and other devices for various user scenarios. Having said this, every Solution Architect in the field knows the capabilities and reliabilities of AWS Cloud. Migrating or designing Internet of Things (IoT) solutions on to AWS platform enables one to focus on core business without the hassle of infrastructure management and monitoring. This will ensure high availability to the customers. No matter which solution is designed, one should select the best platform to keep the solution stable. AWS is one such platform.
There are few practices to be considered in designing IoT solutions with AWS. If the right AWS services are used for customer requirements, then IoT solutions will be able to deliver results in a more secure, reliable, and scalable manner.
Design to Operate at Scale Reliably
IoT systems must handle high-velocity and high-volume data captured by devices and gateways. The overflow of incoming data can be expected due to the sudden growth of the business, or sometimes due to a malicious attack. In such cases, the cloud system architecture should be scalable to handle such data.
The best approach is to send data to queue, buffer, or real-time in-memory databases before storing it in storage. This helps to achieve real-time events and to slow down the data insertion rate to prevent the database crash or to prevent a slower response.
The device can publish data to AWS Kinesis, or AWS IoT rule can be used to forward data to AWS SQS and Kinesis to store it in time-series stores like AWS S3, Redshift, DataLake, or Elasticsearch for data storage. The data store can be used to generate a custom dashboard or AWS QuickSight dashboards.
Fig 1: AWS IoT — Prevent Data Loss
Route Large Data Volumes Through Data Pipelines
Consuming incoming data from device topics directly to a single service prevents systems to achieve full scalability. Sometimes, such an approach limits the availability of the system on events of failure and data flood.
AWS IoT Rules Engine is designed to connect endpoints to AWS IoT Core in a scalable way. But all AWS services have different data flow properties and its own pros and cons. All services cannot be used as a single point of entry to the system. Sometimes, it can create subsequent failure with no recovery. For example, in cases of high-volume data, consider buffering (ElastiCache) or queuing (SQS) the incoming data before invoking other services, which enables the ability to recover from subsequent failures.
AWS IoT Rules Engine allows the triggering of multiple AWS services like Lambda, S3, Kinesis, SQS, and SNS in parallel. Once data is captured by the IoT system, it then enables AWS endpoints (other AWS services) to process and transform data. This enables the ability to store data into multiple data stores simultaneously.
The most secure and best way to ensure all data is processed and stored is to redirect all device topics data to an SNS, which is designed to handle data flood processing, ensuring that incoming-data is reliably maintained, processed, and delivered to the proper channel. To make it more scalable, multiple SNS topics, SQS queue, Lambda for a different/group of AWS device topics can be used. One should consider storing the data in safe-storage like a Queue, Amazon Kinesis, Amazon S3, and Amazon Redshift before processing. This practice ensures no data loss due to message floods, un-wanted exception code, or deployment issues.
Fig 2: AWS IoT — Using Topics and Rules to redirect Data to the proper channel
Automate Device Provisioning and Upgrades
As the business grows and numerous devices connect to the IoT ecosystem, manual processes — such as device provisioning, bootstrapping the software, security configuration, rule-actions setup, device OTA upgrades — are not feasible. Minimizing human interaction in the initialization process and upgrades is important to save time and cost.
Designing built-in capabilities within the device for automated provisioning and leveraging the proper tools that AWS provides to handle device provisioning and management allows systems to achieve desired operational efficiencies with minimal human intervention.
AWS IoT provides a set of functionalities that can be used for batch import with a set of policies that can be integrated with dashboard or manufacturing process where a device can be pre-registered to AWS IoT and certificates can be installed in the device. Later, device provisioning flow can claim device and attach with user or any other entity. AWS provides the facility to trigger and track OTA upgrades for devices.
Adopt Scalable Architecture for Custom Components
As the IoT system connects to external world devices, the scope doesn't end by connecting, controlling, and reporting of devices. Think about adopting the latest technologies like data science and machine learning or integrating third-party components in the IoT system like IFTTT, Alexa, or Google Home. The architecture of IoT should ensure that the external components can be easily integrated into the solution without any performance bottlenecks.
Check for Offline Access and Processing
Sometimes, it is not necessary to process all your machine data in the cloud. In many cases, there is no continuous Internet connectivity available. For such a scenario, add AWS Greengrass at the edge. Greengrass processes and filters data locally on edge and reduces the need to send all device data upstream. One can capture all data, hold it for a limited amount of time, and send it to cloud on error events or on demand/request. If there is a need for time-series data, then one can schedule a periodic process that sends device data to the cloud, which can be used for future enhancements like AWS Machine Learning models and cloud analytics tools.
Fig 3: AWS Greengrass — Adding processing on Edge
Choose the Right Data Storage
IoT systems generate high-speed, high-volume, and varieties of data. Each IoT device or device topic can have different formats, which may not be manageable through a single database or a similar type of data-store. An architect should be careful while choosing database format and data-store. Sometimes, a single data-store works fine, or a hybrid data-store for a different purpose helps to achieve high throughput. Frequently-used static data can be stored in the ElastiCache, which helps to improve performance. Such practices help to achieve scalability and maintainability of the system.
Filter and Transform Data Before Processing
All incoming data to the IoT system may require processing or transforming, after which it can be redirected to storage as it is. AWS IoT rule provides action to redirect messages to different AWS services. An architect should dive all data in different forms, i.e. processing-needed, ignored/static data (like Config), and direct storage.
AWS IoT Service Architecture
Fig 4: AWS IoT — Put all data together
AWS IoT helps to achieve quick device connectivity, secure data ingesting, easy device management, multi-protocol support, and much more.
Published at DZone with permission of Chandani Patel . See the original article here.
Opinions expressed by DZone contributors are their own.