deep dive on aws iot

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Vadim Jelezniakov | Dev Manager, AWS IoT

19 April 2016

IoT on AWS

Deep Dive

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

AWS IoT

Back to the questions


Using IoT rules engine to process messages



3. How do I generate predictions ?


your IoT rules

Rules Engine

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

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:

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:

Rules engine: Filter and transform







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.

Rules engine: React and move







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”

}

}

],

}

Rules engine: Move messages to other systems







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







your IoT rules

1. How do I extract value from my device data ?

Using IoT Rules Engine to process messages



3. How do I generate predictions ?

Using Amazon Machine Learning (AML) predict

function in your IoT Rules

ElasticSearch Integration - New

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

AWS IoT Elasticsearch demo







your IoT rules

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



your IoT rules

Failures are often hard to predict

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

Amazon ML Predict Function

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

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

AWS IoT predict demo

More fun with WebSockets:

predict.vadimj.io

Summary

• Extract value from device data: rules engine

• Visualize your data: rules engine with

Elasticsearch/Kibana integration

• Using predictions: machinelearning_predict()

Thank You!@vadimj

deep dive on aws iot

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