Proof of Concept to solve problems for On-Premise Edge Device and How they can hookup around AWS IoT core and Services

1. Confidential Customized for Proof of Concept Version 1.0
Aws IoT Edge
Management
Greengrass and Core solution By Avinash Patil and Aditya Dubey

2. Overview
★ Effective use of IoT with integrated solution with AWS Greengrass
★ Use of Lambda function which containerized for Greengrass connection to gather data from IoT Devices
★ Use of S3 for data storage for sensor and machine data
★ DevOps provisioning of IoT environment using Python Scripts and Cloudformation
★ Use of AWS Managed Databases for Synchronizing device data and events
★ Schema Design for Handling IoT Data with tables

3. Problems to solve
1
IoT Device manager Management
2
AWS Cloudwatch,S3 or Firehose, SNS type
solution for failure notification
3
AWS IoT GreenGrass data retrieval and
send to Inhouse IoT Devices
4
Manage connection between IoT Edge
devices with AWS Greengrass

4. Implement aws greengrass
★ Install the AWS IoT Greengrass Core Software or SDK for python in the hardware(eg: Raspberry pi)
★ Configures your cloud-based resources.
○ Generate the device certificate.
○ Configure greengrass group.
○ Configure rule engine to maintain a connection and process the data to lambda function or other aws service.
★ Deploy greengrass group to production with lambda.

5. Communication between AWS IoT core and
Greengrass

6. Create and package a lambda function
★ Create lambda function by using AWS IoT Greengrass Core SDK for Python3.x
★ Create a deployment package with all dependencies and upload it to the aws lambda console.
★ Use the lambda action to store data into S3 or DynamoDB.
Configure the Lambda Function for AWS IoT Greengrass
★ Use the AWS IoT console to add the Lambda function to your Greengrass group.
★ Configure group-specific settings for the Lambda function.
★ Add a subscription to the group that allows the Lambda function to publish MQTT messages to AWS IoT.
★ Configure local log settings for the group.
★ Verify the Lambda Function whether it is running on the core device by using aws iot core console.

7. Messaging
★ Create MQTT Pub/Sub broker for communication.
★ Apply filter to MQTT topic for filtering the messages.
★ Create subscription to access the messages for device
activity or state.
★ Use the MQTT broker to send/receive the failure
message of a device.

8. Device Shadow
A JSON document that represents the device state of
your devices and lambdas. It sync the stored data to
cloud or our own server.
★ Create a lambda to trigger S3 service to store the device
info in S3 database.
★ Create a rest API call to communicate with our own
Server.
★ Create a lambda with different action to store shadow
data or other info to our server through the API call.

9. Security
★ Create root CA certificate with sigv4 credentials
separately for production.
★ Local CA certificate should be different from
production.
★ Create and maintain separate policies and roles for
different IAM users as per the requirement.

10. IoT management with AWS Solution
Propose a solution and integrate existing
IoT devices to have on device analysis
and core management.

11. Understanding
the IoT Edge
Devices

12. AWS Cost Estimation
Underlying infrastructure uses AWS Greengrass, Lambda , S3
and Managed Database like DynamoDB, RDS,etc.
Total Cost of Ownership : (For Approx 10 million Requests and
three MQTT Broker) : Approx 100 $
★ AWS Greengrass : 1.49 $ per device
★ AWS IoT Edge : (for 100 devices)
○ Connectivity Charges : 3.45$
○ Messaging Charges : 9.75$
○ Device Shadow & Registry Charges : 3,75$
○ Rules Engine Charges : 1.80 $
★ AWS S3 : 12 $
★ AWS ActiveMQ/ AWS SNS: 23.0 $
★ AWS Databases:
○ DynamoDB : 30.6 $ or
○ RDS Multi AZ : 54.38 $
QUICK TIP
AWS Cost is based for 10 million
request and can be reduced with
combined solution