1. Autonomous Decision Making in Edge Devices with AWS IoT TopicRule


    Autonomous decision-making on edge devices with AWS IoT involves creating rules to process and act upon data generated by your IoT devices. AWS IoT allows you to define SQL-like rules to process device data and trigger actions in the AWS cloud or on the local device itself. The actions can include storing the data, transforming the data, invoking AWS Lambda functions, or even sending a notification through Amazon SNS, among other options.

    In Pulumi, you can describe and provision this setup using the aws.iot.TopicRule resource. This resource allows you to define a rule with an SQL statement to select data from MQTT messages, and an action, such as writing the data to an S3 bucket or invoking an AWS Lambda function.

    Below, you'll find a Pulumi program written in Python that will help you set up an AWS IoT TopicRule. This rule listens for MQTT messages on a specified topic, evaluates those messages, and performs actions based on the content of those messages. For example, it might send a message to an S3 bucket if a particular condition is met.

    import pulumi import pulumi_aws as aws # Define an AWS IoT topic rule iot_topic_rule = aws.iot.TopicRule("myIotTopicRule", enabled=True, sql="""SELECT * FROM 'iot/topic' WHERE temperature > 50""", sql_version="2016-03-23", description="Trigger actions when temperature readings exceed 50", actions=[ # Define an action to write the data to an S3 bucket when the rule is triggered aws.iot.TopicRuleActionArgs( s3=aws.iot.TopicRuleActionS3Args( bucket_name="myBucketName", # Make sure this bucket exists key="myKey/${timestamp()}", # The key pattern for stored messages role_arn="arn:aws:iam::123456789012:role/iot_access" # Replace with the appropriate IAM role ARN ) ) ] ) # Export the name of the topic rule pulumi.export("topic_rule_name", iot_topic_rule.name)

    In this program:

    • We import Pulumi and the AWS Pulumi SDK.
    • We use the aws.iot.TopicRule resource to create a new IoT topic rule.
    • We enable the rule with enabled=True.
    • The SQL statement filters messages where the temperature value in the message payload is greater than 50.
    • The SQL version is specified to be "2016-03-23".
    • We add a description to our rule for clarity.
    • We set up an action. In this example, messages that trigger the rule are written to an S3 bucket (replace myBucketName with your actual bucket and provide the correct IAM role ARN with permissions to write to the S3 bucket).
    • The key for each message stored in S3 will be constructed dynamically using a timestamp.
    • Finally, we export the name of the topic rule, which can be helpful for reference purposes in your Pulumi stack.

    Please note that you will need to replace "myBucketName" with the name of your S3 bucket and add the appropriate IAM role ARN that the IoT service will use to write to the S3 bucket. The IAM role must have the necessary permissions to write objects to the specified S3 bucket.

    This program can be expanded to include additional actions, such as invoking a Lambda function, sending a notification through SNS, or integrating with other AWS IoT services such as IoT Analytics, IoT Events, and IoT SiteWise.