HomeIoTLinked autos telemetry – Processing Protobuf messages with AWS IoT Core

Linked autos telemetry – Processing Protobuf messages with AWS IoT Core


In join autos functions, telemetry knowledge is often very in depth, containing construction and unstructured knowledge. To ship knowledge over to the Cloud you should utilize Protocol Buffers (Protobuf – binary format). Protobuf supplies the applying with an environment friendly but properly structured compressing mechanism. The built-in protocol documentation makes knowledge serialization and deserialization extra manageable than JavaScript object notation (JSON). Nevertheless, producer and shopper should function on an outlined shared schema to encode and decode it correctly.

On this weblog we’ll cowl the very best practices whereas utilizing Protobuf to encoding and decoding. Additionally, you will study step-by-step how you can use AWS IoT Core and AWS Lambda to ingest and course of Protobuf for consumption

Answer Structure

Determine 1. Structure diagram

Answer overview

  1. You’ll simulate a linked automotive and authenticate it to AWS IoT Core. The machine will first encode the payload and ship it over Message Queuing Telemetry Transport (MQTT) to AWS IoT Core
  2. As soon as the message is obtained by AWS IoT Core you’ll use AWS IoT Rule which can invoke an AWS Lambda perform so as to decode the payload.
  3. The rule sends messages to Amazon Kinesis Knowledge Firehouse after which shops it in Amazon S3
  4. Every time a brand new file is written on Easy Storage Service (Amazon S3), AWS Glue Crawler will crawl the information to deduce the schema and make it out there within the AWS Glue Knowledge Catalog.
  5. We are going to the use Amazon Athena to do Advert-hoc querying an visualize it in Amazon fast sight.

AWS IoT Core

AWS IoT Core securely connects your simulated IoT machine and routes the encoded messages to AWS companies with out managing the underlying infrastructure. You possibly can then use guidelines for AWS IoT to decode your payload knowledge and ahead it to Amazon Kinesis Knowledge Firehose.

Amazon Kinesis Knowledge Firehose

Amazon Kinesis Knowledge Firehose captures the incoming knowledge from your rule for AWS IoT and cargo it as batch in parquet format in our Amazon S3 Bucket.

Amazon S3

Amazon S3 serves as a knowledge lake in your knowledge that you should utilize for additional evaluation and visualization.

AWS Glue

The AWS Glue Knowledge Catalog is your persistent retailer for the metadata (e.g., schema and placement of the information). It’s a managed service that permits you to retailer, annotate, and share metadata within the AWS Cloud.

For writing information to Amazon S3, you should utilize AWS Glue crawler to scan knowledge, classify it, carry out schema extractions, and retailer the metadata routinely within the AWS Glue Knowledge Catalog.

Amazon Athena

Amazon Athena makes use of the metadata classification from AWS Glue Knowledge Catalog to carry out ad-hoc queries on the information.

Amazon QuickSight

You possibly can visualize your knowledge and construct a customized dashboard utilizing Amazon QuickSight

Answer Walkthrough


  • You want a PC with an internet browser, ideally with the newest model of Chrome / FireFox
  • You will need to have entry to an AWS account with Administrator Entry privileges
  • When you don’t have an AWS Account comply with the directions to create one.
  • You’ll use Cloud formation template to create the setup surroundings and you may delete the surroundings as soon as performed
  • Following AWS companies will probably be used:
    • AWS IoT Core
    • Amazon Kinesis Knowledge Firehose
    • Amazon S3
    • AWS Glue
    • Amazon Athena
    • Amazon QuickSight
    • Amazon Cloud9

Setup answer

Creating and setup AWS Cloud9 surroundings

Use the next hyperlink to setup the check surroundings utilizing AWS Cloud9 for this weblog AWS IoT Machine Shopper Workshop (IoT quickstart) (workshops.aws). You could  choose any area near your location.

Setup AWS IoT Factor and SDK

Open Cloud9 terminal and let’s setup Python SDK for us to make use of.

Create the folder you’ll use to attach the  IoT factor utilizing Cloud9 terminal window.

mkdir -p /dwelling/ubuntu/surroundings/protobuf-python-aws-iot-device/certs
cd /dwelling/ubuntu/surroundings/protobuf-python-aws-iot-device/

Setup the dependencies:

copy and paste the next necessities.txt


after which run the next:

python3 -m venv venv
supply ./venv/bin/activate
pip set up -r necessities.txt

Setup your AWS IoT Factor comply with steps outlined right here.

As soon as we have now created the factor let’s add these certificates in our Cloud9 occasion for us to attach from there.

Add the newly created certificates and RootCA into ‘certs’ folder created earlier.

Machine and Schema

Right here is the Protobuf schema that we are going to use. Create file the next file automotive.proto file and replica and paste the next content material.

syntax = "proto2";

bundle automotive;

message Automotive {
  required float battery_level = 1;
  required float battery_health = 2;
  required float battery_discharge_rate = 3;
  required float wheel_rpm = 4;
  required float mileage_left = 5;

You have to to compile and generate the suitable library, right here is the corresponding file you should utilize and save into following file automotive_pb2.py

# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler.  DO NOT EDIT!
# supply: automotive.proto
"""Generated protocol buffer code."""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()

DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'nx10x61utomotive.protox12nautomotive"x84x01nnAutomotivex12x15nrbattery_levelx18x01 x02(x02x12x16nx0ex62x61ttery_healthx18x02 x02(x02x12x1enx16x62x61ttery_discharge_ratex18x03 x02(x02x12x11ntwheel_rpmx18x04 x02(x02x12x14nx0cmileage_leftx18x05 x02(x02')

_AUTOMOTIVE = DESCRIPTOR.message_types_by_name['Automotive']
Automotive = _reflection.GeneratedProtocolMessageType('Automotive', (_message.Message,), {
  '__module__' : 'automotive_pb2'
  # @@protoc_insertion_point(class_scope:automotive.Automotive)
if _descriptor._USE_C_DESCRIPTORS == False:
  DESCRIPTOR._options = None
# @@protoc_insertion_point(module_scope)

Let’s create our file that can execute our machine simulation. Copy and paste the next content material in a file named foremost.py

 * Copyright 2010-2017 Amazon.com, Inc. or its associates. All Rights Reserved.
 * Licensed below the Apache License, Model 2.0 (the "License").
 * You could not use this file besides in compliance with the License.
 * A duplicate of the License is positioned at
 *  http://aws.amazon.com/apache2.0
 * or within the "license" file accompanying this file. This file is distributed
 * categorical or implied. See the License for the precise language governing
 * permissions and limitations below the License.

from AWSIoTPythonSDK.MQTTLib import AWSIoTMQTTClient
import logging
import time
import argparse
import json
import automotive_pb2
import numpy as np

AllowedActions = ['both', 'publish', 'subscribe']

# Customized MQTT message callback
def customCallback(shopper, userdata, message):
    print("Obtained a brand new message: ")
    print("from matter: ")

# Learn in command-line parameters
parser = argparse.ArgumentParser()
parser.add_argument("-e", "--endpoint", motion="retailer", required=True, dest="host", assist="Your AWS IoT customized endpoint")
parser.add_argument("-r", "--rootCA", motion="retailer", required=True, dest="rootCAPath", assist="Root CA file path")
parser.add_argument("-c", "--cert", motion="retailer", dest="certificatePath", assist="Certificates file path")
parser.add_argument("-k", "--key", motion="retailer", dest="privateKeyPath", assist="Personal key file path")
parser.add_argument("-p", "--port", motion="retailer", dest="port", kind=int, assist="Port quantity override")
parser.add_argument("-w", "--websocket", motion="store_true", dest="useWebsocket", default=False,
                    assist="Use MQTT over WebSocket")
parser.add_argument("-id", "--clientId", motion="retailer", dest="clientId", default="basicPubSub",
                    assist="Focused shopper id")
parser.add_argument("-t", "--topic", motion="retailer", dest="matter", default="sdk/check/Python", assist="Focused matter")
parser.add_argument("-m", "--mode", motion="retailer", dest="mode", default="each",
                    assist="Operation modes: %s"%str(AllowedActions))
parser.add_argument("-M", "--message", motion="retailer", dest="message", default="Hi there World!",
                    assist="Message to publish")

args = parser.parse_args()
host = args.host
rootCAPath = args.rootCAPath
certificatePath = args.certificatePath
privateKeyPath = args.privateKeyPath
port = args.port
useWebsocket = args.useWebsocket
clientId = args.clientId
matter = args.matter

if args.mode not in AllowedActions:
    parser.error("Unknown --mode choice %s. Have to be one among %s" % (args.mode, str(AllowedActions)))

if args.useWebsocket and args.certificatePath and args.privateKeyPath:
    parser.error("X.509 cert authentication and WebSocket are mutual unique. Please choose one.")

if not args.useWebsocket and (not args.certificatePath or not args.privateKeyPath):
    parser.error("Lacking credentials for authentication.")

# Port defaults
if args.useWebsocket and never args.port:  # When no port override for WebSocket, default to 443
    port = 443
if not args.useWebsocket and never args.port:  # When no port override for non-WebSocket, default to 8883
    port = 8883

# Configure logging
logger = logging.getLogger("AWSIoTPythonSDK.core")
streamHandler = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s - %(identify)s - %(levelname)s - %(message)s')

# Init AWSIoTMQTTClient
myAWSIoTMQTTClient = None
if useWebsocket:
    myAWSIoTMQTTClient = AWSIoTMQTTClient(clientId, useWebsocket=True)
    myAWSIoTMQTTClient.configureEndpoint(host, port)
    myAWSIoTMQTTClient = AWSIoTMQTTClient(clientId)
    myAWSIoTMQTTClient.configureEndpoint(host, port)
    myAWSIoTMQTTClient.configureCredentials(rootCAPath, privateKeyPath, certificatePath)

# AWSIoTMQTTClient connection configuration
myAWSIoTMQTTClient.configureAutoReconnectBackoffTime(1, 32, 20)
myAWSIoTMQTTClient.configureOfflinePublishQueueing(-1)  # Infinite offline Publish queueing
myAWSIoTMQTTClient.configureDrainingFrequency(2)  # Draining: 2 Hz
myAWSIoTMQTTClient.configureConnectDisconnectTimeout(10)  # 10 sec
myAWSIoTMQTTClient.configureMQTTOperationTimeout(5)  # 5 sec

# Join and subscribe to AWS IoT
if args.mode == 'each' or args.mode == 'subscribe':
    myAWSIoTMQTTClient.subscribe(matter, 1, customCallback)

# Publish to the identical matter in a loop eternally
loopCount = 0
automotive = automotive_pb2.Automotive()
dataPointsSin = np.linspace(-np.pi, np.pi,100)
whereas True:
    if args.mode == 'each' or args.mode == 'publish':
        # 100 linearly spaced numbers
        automotive.battery_level = abs(dataPointsSin[loopCount % 100]) / np.pi
        automotive.battery_health = 100 - loopCount % 100
        automotive.battery_discharge_rate = 4.8
        automotive.wheel_rpm = 3000
        automotive.mileage_left = loopCount % 100
        message = bytearray(automotive.SerializeToString())
        myAWSIoTMQTTClient.publish(matter, message, 1)
        if args.mode == 'publish':
            print('Revealed matter %s: %sn' % (matter, message))
        loopCount += 1
    # Copyright Amazon.com, Inc. or its associates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0.

Lambda layer

Create a lambda layer that will probably be used to retailer protobuf libraries, then execute the next command:

mkdir -p ./protobuf/python/customized
cd ./protobuf/python
pip3 set up protobuf --target .                                      
cd customized
cp ../../../automotive_pb2.py ./
echo 'customized' >> ../protobuf-*.dist-info/namespace_packages.txt
echo 'customized/demo_pb2.py' >> ../protobuf-*.dist-info/RECORD
echo 'customized' >> ../protobuf-*.dist-info/top_level.txt
cd ../../
zip -r protobuf.zip .
aws lambda publish-layer-version --layer-name protobuf --zip-file fileb://protobuf.zip --compatible-runtimes python3.8

Setup S3 bucket

We are going to in a primary time create an S3 bucket the place will retailer our knowledge and which we’ll question it from. Create an S3 bucket, fill out a reputation, for the identify will probably be “ACCOUNT-ID-connected-evs”, go away the remaining as default. and click on on create bucket. Please observe your bucket identify as we’ll reusing it by means of the entire this weblog.

Determine 2.

Setup Kinesis knowledge firehose

Create a supply stream, the supply stream will write the obtained knowledge from the linked vehicles to Amazon S3. Choose supply Direct PUT, vacation spot Amazon S3, and fill out a reputation, for me will probably be: “my-delivery-stream-connected-evs“.

Determine 3.

In vacation spot settings, choose the S3 bucket that you just beforehand created, as bucket prefix, fill out “uncooked“ and error prefix as ”errors“. Miss the remaining as default and wait jiffy earlier than this completes.

Determine 4.

Setup AWS IoT Rule

Create the AWS IoT Rule, we’ll use the IoT rule throughout the lambda creation, please observe your rule identify. It is advisable choose all knowledge coming from the subject connected-cars/knowledge, after which invoke the incoming knowledge with a lambda perform so as to decode the protobuf encoded payload. You first have to encode the binary string in base64. For the SQL assertion please copy and paste the next, please exchange ACCOUNT_ID along with your account ID

Choose Message Routing

  • Choose Guidelines
  • Choose Create rule
  • Give Rule identify (i.e. we’re utilizing “MyConnectedEVSRuleToFirehose”)
  • Give Rule description
  • Use the next question for the rule: SELECT aws_lambda("arn:aws:lambda:us-east-1:ACCOUNT_ID:perform:my-protobuf-decoder", {"knowledge": encode(*, "base64"), "clientId": clientId()}) as payload, timestamp() as p_time FROM 'connected-cars/knowledge'
  • Choose Subsequent
  • In Connect rule actions
    • Choose settings as per Determine 6
    • Choose Add motion
  • Choose Subsequent
  • In Assessment and Create

Determine 5.

Determine 6.

Setup lambda

Create AWS Lambda perform and provides the identical identify as earlier when creating AWS IoT Rule. Choose Python 3.8 for runtime.

Determine 7.

After creating the layer for the protobuf half, please use the next code:

import json
import base64
from customized import automotive_pb2

print('Loading perform')

def lambda_handler(occasion, context):
    print("Obtained occasion: " + json.dumps(occasion, indent=2))

    ret = {}    

    knowledge = occasion["data"]
    payload_data_decoded = base64.b64decode(knowledge)
    automotive = automotive_pb2.Automotive()
    elems = automotive.ListFields()
    for elem in elems:
        ret[elem[0].identify] = elem[1]
    ret["clientId"] = occasion["clientId"]
    return ret

Within the configuration tab and permissions, go to the resource-based coverage and click on on add permission. We have to add the mandatory permission to permit the iot rule to invoke our perform. when specifying the arn, please use the identical identify for the rule you created. Click on on save.

Determine 8.

Lastly, we’ll use the beforehand created layer, for that, go within the layer half and Choose ‘Add a layer‘.

Determine 9.

Determine 10.

Protobuf decode/encode

Following JSON will probably be used for additional encoding as Protobuf binary message.

    "battery_level": 100,
    "battery_health" : 50,
    "battery_discharge_rate" : 4.8,
    "wheel_rpm" : 3000,
    "mileage_left" : 88

Let’s publish our first message and test if every part is working:

Pattern command:

Utilizing certificates and AWS IoT factor created earlier, these certificates used within the parameter to ship the message (exchange xxxx with related values in your setup).

supply ./venv/bin/activate
python3 foremost.py -e xxxx-ats.iot.us-east-1.amazonaws.com -c ./certs/xxxx-certificate.pem.crt -r ./certs/AmazonRootCA1.pem -t connected-cars/knowledge -m eza -k ./certs/xxxx-private.pem.key --mode publish

Determine 11.

Go to your bucket after jiffy, it’s best to see information being written.

Setup AWS Glue Crawler

We’re going to now create the Glue Crawler that will probably be chargeable for creating and updating the schema of our knowledge. You possibly can create a crawler on the next hyperlink. For crawler identify, mine will probably be: ‘my_connected_evs_crawler’.

For the Crawler supply kind choose Knowledge shops, for Repeat crawls of S3 knowledge shops choose Crawl all folders. Within the Add Knowledge retailer go away every part by default however for the embody path choose your S3 bucket and the uncooked folder. For me will probably be s3://ACCOUNT_ID-connected-evs/uncooked. click on on subsequent. Don’t add one other datastore. Give a reputation to your function. For the frequency go away as default.

For Configure the crawler’s output, click on on add database, add a database identify my_connected_evs_db and go away the remaining clean. Depart the remaining as default and click on subsequent.

Choose your crawler, and click on on run your crawler. The standing of your crawler needs to be displaying beginning, When the standing of your crawler is stopping, go test your desk in your database. You need to see the next in your uncooked desk:

Determine 12.

Setup Amazon Athena

Go to the Amazon Athena console, you’ll be able to setup your Amazon Athena question outcomes by following this hyperlink.

Choose your database and desk that you just used for the crawler. Run the next question:

SELECT * FROM uncooked;

Determine 13.

Visualize knowledge utilizing Amazon QuickSight

To setup QuickSight, please comply with this hyperlink.

In QuickSight, let’s first create our dataset.  Click on on Dataset on the left. The supply of our dataset will probably be Amazon Athena that we used beforehand to preview our knowledge. If you wish to test the opposite sources which can be supported, please comply with the next hyperlink. Please observe that in our case we use Amazon Athena for simplicity to do advert hoc querying and fast dash-boarding.

Determine 14.

On the next display, click on on new dataset.

Determine 15.

Then click on on Athena.

Determine 16.

Then give a reputation to your knowledge supply, for us, will probably be: ‘my-evs-athena-data-source’. Make to positive to the validate connection. Then click on on Create Knowledge supply.

Determine 17.

Selected the AwsDataCatalog and our db my_connected_evs_db and the uncooked desk. Click on on Use customized SQL.

Determine 18.

We are going to flatten the payload struct with the next question. Copy and paste the question and identify the customized SQL question and click on on Verify question.

SELECT payload.battery_level as battery_level, payload.battery_health as battery_health, payload.battery_discharge_rate as battery_discharge_rate, payload.wheel_rpm as wheel_rpm, payload.mileage_left as mileage_left, p_time, payload.clientId as client_id FROM "my_connected_evs_db"."uncooked";

Determine 19.

Depart the remaining by default and click on on visualize.

Determine 20.

Listed here are some examples of visualization

Click on on the decrease left hand aspect on the desk diagram and on every dimension. You need to see your knowledge in a desk format.

Determine 21.


On this weblog submit we took a pattern JSON payload and encoded into Protobuf binary format and despatched it over to AWS IoT Core, the place we deserialized and decoded utilizing AWS Lambda.The info was then our knowledge lake in Amazon S3 utilizing Kinesis Datafirehose, lastly utilizing this knowledge we visualized our telemetry knowledge of this linked automobile. By following this weblog, you discovered  how one can  compress, serialize and deserialize JSON dataset of linked autos. Utilizing this technique you’ll be able to obtain decrease latency together with compatibility between your finish machine and your shopper.

Concerning the Authors

Syed Rehan is a World Sr. Specialist Options Architect at Amazon Internet Companies and is predicated in London. He’s overlaying world span of shoppers and supporting them as lead IoT Answer Architect. Syed has in-depth information of IoT and cloud and works on this function with world prospects starting from start-up to enterprises to allow them to construct IoT options with the AWS eco system.
Kevin Polossat is a Options Architect at AWS. He works with prospects in France to assist them embrace and undertake the cloud. Exterior of labor, he enjoys wine and cheese.



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