IoT application benefits immensely if it can get cloud to work, and cloud evolves everyday to better serve IoT
The IoT world is fast recognizing the edge, that integration with cloud can provide to the business. The challenge lies in pursuing the course at the cost of time, and skills investment. Its more logical, and cost effective in the long run , to consult cloud domain experts.
The ever evolving cloud with its multiple vendors often present a market spoilt for choices, this variety that makes investigating and selecting a technology, time consuming.
Introduction of Docker for IoT completely changes the pace of development and provides a consistent platform for developing, testing and deploying IoT applications.
We have tried to put together a guideline on how cloud and container technologies can enhance each stage of IoT.
• Creating IoT Applications –
There are some eco-systems like the one given below which give developers the power of creating their IoT Apps with simple event based processing.
AWS IOT Button based on their DASH platform provides a easy and convenient way of developing IoT applications and are becoming popular among startups and enterprises alike. AWS provides an out of the box rules engine and Lambda functions can be written around them. The rules engine triggers the related execution of these lambda functions.
Additionally the eco-system provides easy storage and access to their notification sever, the AWS SNS.
More complex IoT applications can be developed faster with container technologies like Docker. During prototyping and early phases of development, IoT projects are often developed using generic microcontroller-based development boards or single-board computers (SBCs) like the Raspberry Pi. Containers can be used to capture a development environment that is known to work for each device revision, and to share this environment among a team of developers.
For example, if an application was using several types of Arduino-compatible development boards, a docker image containing the Arduino command line toolkit and a dumb-terminal emulation program can be created as a baseline development image. This base image can be used to create new variant images for each of the development boards that require specific custom drivers.
• Managed Device Communication
The Big 3, AWS, Azure, and Google are fast developing IoT cloud platforms which can provide managed services for low latency messaging, real-time processing and predictive analytics and management practices.
For example Google Cloud has a comprehensive, fully-managed real-time messaging service that allows you to send and receive messages between independent applications.
This Cloud Pub/Sub service is designed to provide “at least once” delivery at low latency with on-demand scalability to 1 million messages per second (and beyond).
AWS provides an AWS IOT Platform for both compute and storage which can talk to a number of endpoints and connect to a host of services like AWS Lambda, Amazon Kinesis, Amazon S3, Amazon Machine Learning, Amazon DynamoDB, Amazon CloudWatch, AWS CloudTrail, and Amazon Elasticsearch.
Additionally it can manage messaging between devices which are not connected.
Microsoft Azure has a similar solution.
AWS GreenGrass enables local execution of AWS Lambda, messaging, device shadows and security.
• Storage
While AWS integrates its S3 and DynamoDB into its AWS IoT Cloud eco system, Google Cloud Platform offers storage in three flavors for different needs as well as managed MySQL and globally-scalable NoSQL databases and archival storage.
• Big Data
Large-scale data analytics are provided for real-time processing and predictive behaviors. BigQuery is Google’s fully managed and serverless data warehouse for analytics. No database administrator is needed and it can scan TB in seconds and PB in minutes.
Amazon Elasticsearch Service combined with Amazon EMR and Amazon Athena provides a comprehensive Big Data Infrastructure. Amazon Kinesis and Redshift are used for analytics and data warehousing.
