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Deep Dive on AWS IoT

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AWS IoT is a managed cloud platform that lets connected devices easily and securely interact with cloud applications and other devices. As an IoT developer, you will need to interact with AWS services like Amazon Kinesis, AWS Lambda, and Amazon Machine Learning to get the most from your IoT application. In this session, we will do a deep dive on how to define rules in the Rules Engine, or retrieve the last known and desired state of device using Device Shadows, routing data from devices to AWS services to leverage the entire cloud for your Internet of Things application.

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Deep Dive on AWS IoT

  1. 1. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Vadim Jelezniakov | Dev Manager, AWS IoT 19 April 2016 IoT on AWS Deep Dive
  2. 2. Goal: Answer these questions 1. How do I extract value from my device data? Using IoT rules engine to process your data 2. How do I visualize my device data? Connecting the rules engine to Elasticsearch/Kibana 3. How do I generate predictions? Using Amazon Machine Learning predict function in your IoT rules
  3. 3. AWS IoT
  4. 4. Back to the questions 1. How do I extract value from my device data? Using IoT rules engine to process messages 2. How do I visualize my device data? Connecting the rules engine to Elasticsearch/Kibana 3. How do I generate predictions ? Using Amazon Machine Learning predict function in your IoT rules
  5. 5. Rules Engine
  6. 6. Rules engine: Extracting value from messages • Filter messages with certain criteria • Transform the payload of messages • React based on messages • Move messages to other topics • Move messages to other systems • Predict changes based on trends
  7. 7. What is a rule? ruleArn: arn:aws:iot:<region>:<account_id>:rule/<uniq_rule_name> rule: • ruleName: human-readable name • description: human-readable description • ruleDisabled: true/false • sql: e.g. SELECT * FROM ‘pm/topic’ • actions: • action 1: • action 2:
  8. 8. What is a rule? • ruleName: human-readable name • description: human-readable description • ruleDisabled: true/false • sql: e.g. SELECT * FROM ‘pm/topic’ • actions: • action 1: • action 2:
  9. 9. What is a rule? • ruleName: human-readable name • description: human-readable description • ruleDisabled: true/false • sql: e.g. SELECT * FROM ‘pm/topic’ • actions: • action 1: • action 2:
  10. 10. Rules engine: Filter and transform • Filter messages with certain criteria • Transform the payload of messages • React based on messages • Move messages to other topics • Move messages to other systems • Predict changes based on trends
  11. 11. Rules engine: Filter and transform • SQL • SELECT * FROM topic WHERE condition • SELECT status.space_id AS room_id FROM 'iot/tempSensors/#' WHERE temp > 50 • Functions (in SELECT or WHERE) • String manipulation (regex support) • Mathematical operations • Context-based helper functions • Crypto support • UUID, timestamp, rand, etc.
  12. 12. Rules engine: React and move • Filter messages with certain criteria • Transform the payload of messages • React based on messages • Move messages to other topics • Move messages to other systems • Predict changes based on trends
  13. 13. Rules engine: React (and move) "rule": { "ruleName": "republishPredictions”, "ruleDisabled": false, "sql": "SELECT no AS id, cycle AS c_number FROM 'pm/topic' WHERE failure = 1", "description": "Republish ids of engines that are predicted to fail within 30 cycles", "actions": [ { "republish": { "topic": "pm/failures", "roleArn": "arn:aws:iam::012345678901:role/iot-actions-role” } } ], }
  14. 14. Rules engine: React (and move) "rule": { "ruleName": "republishPredictions”, "ruleDisabled": false, "sql": "SELECT no AS id, cycle AS c_number FROM 'pm/topic' WHERE failure = 1", "description": "Republish ids of engines that are predicted to fail within 30 cycles", "actions": [ { "republish": { "topic": "pm/failures", "roleArn": "arn:aws:iam::012345678901:role/iot-actions-role” } } ], }
  15. 15. Rules engine: Move messages to other systems • Filter messages with certain criteria • Transform the payload of messages • React based on messages • Move messages to other topics • Move messages to other systems • Predict changes based on trends
  16. 16. Rules engine: Move messages to other systems Invoke a Lambda function Put object in an S3 bucket Insert, update a DynamoDB table Publish to an SNS topic or endpoint Publish to a Kinesis stream Publish to Firehose Republish to AWS IoT Publish to Elasticsearch Capture a CloudWatch metric or change an alarm Write to SQS queue
  17. 17. 1. How do I extract value from my device data? Using IoT rules engine to process messages 2. How do I visualize my device data? Connecting the rules engine to Elasticsearch/Kibana 3. How do I generate predictions? Using Amazon Machine Learning predict function in your IoT rules
  18. 18. 1. How do I extract value from my device data ? Using IoT Rules Engine to process messages 2. How do I visualize my device data? Connecting the rules engine to Elasticsearch/Kibana 3. How do I generate predictions ? Using Amazon Machine Learning (AML) predict function in your IoT Rules
  19. 19. ElasticSearch Integration - New
  20. 20. Basic flow for using Elasticsearch 1. Configure your Elasticsearch domain 2. Use an IoT rule to send device data to the Elasticsearch domain you configured 3. Use Kibana to visualize your device data
  21. 21. AWS IoT Elasticsearch demo
  22. 22. 1. How do I extract value from my device data? Using IoT rules engine to process messages 2. How do I visualize my device data? Connecting the rules engine to Elasticsearch/Kibana 3. How do I generate predictions? Using Amazon Machine Learning predict function in your IoT rules
  23. 23. 1. How do I extract value from my device data ? Using IoT Rules Engine to process messages 2. How do I visualize my device data ? Connecting the Rules Engine to ElasticSearch / Kibana 3. How do I generate predictions? Using Amazon Machine Learning predict function in your IoT rules
  24. 24. Failures are often hard to predict
  25. 25. Importance of the good predictions Right balance depends on risk/cost ratio: 1. Predict a failure too soon = replacing a part that doesn’t need repair yet – loss 2. No prediction = risk of sustaining a bigger loss
  26. 26. Amazon ML Predict Function
  27. 27. Basic flow for using predictions 1. Use an IoT rule to forward device data to S3 2. Train your Amazon ML model using the data from S3 3. Use an IoT rule to: • Obtain predicted value from real-time prediction endpoint in Amazon ML • Emit a CloudWatch metric (or trigger an alarm) 4. Use an IoT rule to emit details of a predicted failure
  28. 28. AWS IoT predict function for Amazon ML machinelearning_predict( 'ml-XXXXXX', 'arn:aws:iam::<account_id>:role/<role>', *).predictedLabel ml-XXXXXX – Amazon ML predictor you trained account_id – your account ID role – a role in your account that: • Has access to Amazon ML • Part of a trust relationship b/w your account and IoT
  29. 29. AWS IoT predict demo
  30. 30. More fun with WebSockets: predict.vadimj.io
  31. 31. Summary • Extract value from device data: rules engine • Visualize your data: rules engine with Elasticsearch/Kibana integration • Using predictions: machinelearning_predict()
  32. 32. Thank You! @vadimj

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