Growing a Distant Job Monitoring Software on the edge utilizing AWS IoT Greengrass (half 1)


Many fashionable industrial operations require in depth monitoring and real-time resolution making for effectivity and security causes. To scale back the surprising community interruption and delay in IoT knowledge processing, edge computing turns into a fascinating choice for real-time IoT knowledge processing and monitoring. Edge computing is a system of micro computing/storage units which are put in on the fringe of the community to effectively course of knowledge domestically. For industrial websites, reminiscent of distant energy crops, oil rigs, and manufacturing facility flooring, an edge software with a consumer interface (UI) element affords nice benefits to automate localized operations and enhance workforce expertise.

Nonetheless, conventional IoT purposes on the edge can expertise some challenges, reminiscent of lack of centralized life-cycle administration and a protracted growth cycle. AWS IoT Greengrass V2, the most recent open supply edge run time from AWS, affords a pathway to deal with the above-mentioned challenges. With AWS IoT Greengrass, you possibly can entry pre-built software program elements to enhance the modularity of your software and speed up your growth efforts. AWS IoT Greengrass additionally affords centralized safety measures via a built-in log supervisor to facilitate edge element deployment. Furthermore, the mixing of AWS IoT Greengrass with AWS IoT Machine Administration allows you to monitor and handle your machine fleet at scale.

Since AWS IoT Greengrass V2 launched, AWS is actively serving to AWS Accomplice, TensorIoT, to develop numerous purposeful purposes on the edge utilizing this service. Such purposes cowl use instances reminiscent of: connection to industrial methods and IoT units, low latency knowledge processing and analytics to derive operational insights, and synthetic intelligence (AI)/machine studying (ML) purposes on the edge. On this weblog, we’ll showcase a UI software on the edge with AWS IoT Greengrass V2, a collectively developed resolution by TensorIoT and AWS Skilled Companies. This UI software on the edge comprises a number of customized AWS IoT Greengrass elements to attain versatile knowledge ingestion, and IoT knowledge analytics and visualization on the edge. The applying deployment and updates on a number of edge units could be effectively carried out through AWS IoT Greengrass runtime.

On this weblog, we’ll showcase the way to use this UI on the edge to deal with the next distant industrial job monitoring use instances:

Use case 1 (half 1 of the weblog)

Operators on manufacturing facility flooring could have some reviews or IoT job metadata that can’t be ingested through a pre-defined IoT knowledge mannequin to the cloud. This UI supplies a file add web page, that makes use of a front-end AWS IoT Greengrass V2 element to ingest a JSON file, and sends the file knowledge to a back-end element. Then the back-end element will ship the information to an AWS IoT Greengrass V2 pre-built element: stream supervisor. The stream supervisor element makes it simpler and extra dependable to switch high-volumes IoT knowledge from the sting to the cloud. As a pre-built element, it affords versatile goal locations within the cloud, together with Amazon Easy Storage Service (Amazon S3), Amazon Kinesis, AWS IoT SiteWise and AWS IoT Analytics. It’s designed to work in environments with intermittent or restricted connectivity. You’ll be able to outline a number of knowledge exportation configurations when the sting machine is related or disconnected. It’s also possible to outline bandwidth use, timeout conduct, and set priorities, in order that the crucial knowledge could be exported first when web connection resumes.

This stream supervisor element will obtain two objectives:

  • native knowledge storage;
  • sending knowledge from the native edge machine to AWS.

On this use case, the unmapped knowledge will land in Amazon S3. Quite a lot of knowledge analytics instruments (Amazon Glue and Amazon Athena) /BI instruments can be utilized to research this metadata with different IoT time collection knowledge in a versatile method.

Use case 2 (half 2 of the weblog)

Operators must make judgements with streaming IoT knowledge in a near-real time vogue. The information will assist operators make the suitable choices if it may be proven in an interactive UI software with low latency. Partially 2 of this weblog, you’ll learn to construct a UI software on the edge to indicate streaming IoT job knowledge.

The photographs under spotlight the UI software developed by AWS Skilled Companies and TensorIoT. You should use this app to remotely monitor IoT job standing in near-real time.

Determine 1: UI software on the edge developed by AWS Skilled Companies and TensorIoT for distant IoT job monitoring in near-real time.

The next stroll via comprises detailed steps to develop this UI software on the edge with AWS IoT Greengrass V2. To construct a UI resolution based mostly on the directions shared on this weblog, you don’t want specialised knowledge analytics or IoT knowledge processing expertise.

Time to learnhalf-hour
Time to finish120 minutes

Value to finish


~$2 (at publication time)
Studying degreeSuperior (300)
Companies used

Amazon EC2 occasion, AWS Greengrass V2,

Amazon S3, Amazon CloudWatch

Answer overview

Right here is the answer structure of this workflow on the edge. 4 user-defined elements will likely be constructed on this two-part weblog:

  • A front-end AWS IoT Greengrass UI element written in ReactJS, which comprises all UI pages utilized in each use instances (half 1 of weblog);
  • A JSON file add element written in Python, that collects file knowledge from the front-end UI, and sends the JSON info to Amazon S3 through stream supervisor (half 1of weblog);
  • A dummy writer publishes wind turbine knowledge at a charge of 1 message each 10 seconds, and sends the message to a WebSocket element through IPC protocol (half 2 of weblog);
  • A WebSocket element serves the IoT messages from the dummy writer element to the UI element through a WebSocket server (half 2 weblog).

Moreover these customized elements, there are two pre-built AWS IoT Greengrass V2 elements deployed with this resolution:

  • Log supervisor for software monitoring
  • Stream supervisor for edge machine knowledge exportation

On this weblog, an Ubuntu 22.04 LTS EC2 occasion with AWS IoT Greengrass v2 runtime is used to simulate an edge machine with AWS IoT.

Determine 2: The general diagram of the UI software on the edge.  


For this stroll via, you need to have the next stipulations:

  • An AWS account. In the event you don’t have an AWS Account, comply with the directions to create one, except you’ve been supplied occasion engine particulars.
  • A consumer position with administrator entry (service entry related to this position could be constrained additional when the workflow goes to manufacturing).
  • Current fashionable browser (Newest model of Firefox or Chrome)
  • No specialised information is required to construct this resolution, however fundamental Linux and Python information will assist.

Stroll By way of

This two-part publish comprises the next stroll via steps and can present detailed directions for growing this resolution:
Half 1 for establishing runtime and constructing a JSON file add software (this publish):

  • Step 1: Setup Amazon S3 bucket
  • Step 2: Setup AWS IoT Greengrass V2 and dependencies
  • Step 3: Launch a UI element
  • Step 4: Launch a file add software
  • Step 5: Check the file add software

Half 2 for constructing the IoT job monitoring software:

  • Step 1: Launch a dummy writer
  • Step 2: Launch a WebSocket element
  • Step 3: Configure deployment for log supervisor
  • Step 4: Check IoT job monitoring UI web page

Please word that this publish options the important thing resolution milestones for conciseness, however readers ought to go to the GitHub repository for a full stroll via and supply code.

Step 1: Setup an Amazon S3 bucket

First, create a bucket in Amazon S3 utilizing the AWS Administration Console. Please use these directions to create an Amazon S3 bucket and take a word of the bucket identify. It is advisable present this Amazon S3 parameter to the JSON file add element in Step 4 of this stroll via.

Second, you should utilize comparable steps to create an Amazon S3 bucket for saving edge element artifacts. Please take a word of this Amazon S3 bucket so you should utilize it within the element deployment steps.

Step 2: Setup AWS IoT Greengrass V2 on an Amazon Elastic Cloud Compute (Amazon EC2)  occasion

1. Set up dependencies on the Amazon EC2 Occasion

Arrange the Amazon EC2 occasion of kind t2.medium with ubuntu 22.04 base picture within the AWS account with the next consumer knowledge:

sudo su
apt-get replace
apt-get set up -y python3-pip zip jq
git clone
python3 -m pip set up ./aws-iot-device-sdk-python-v2

After the Amazon EC2 occasion standing modifications to working, use these directions to setup an AWS session supervisor entry position to entry this Amazon EC2 occasion with out SSH. Subsequent, use Join with the Amazon EC2 occasion through AWS Programs Supervisor Session Supervisor (SSM) part of this weblog to entry the Amazon EC2 occasion through SSM. Lastly, set up AWS Command Line Interface (CLI) V2 on the Amazon EC2 occasion:

sudo ./aws/set up

2. Set up AWS IoT Greengrass on the Amazon EC2 occasion

You should use Set up Greengrass on Amazon EC2 occasion part of this weblog to put in AWS IoT Greengrass on the sting machine.

Earlier than you launch the next customized elements, please make sure you add the mandatory Amazon S3 entry coverage (e.g. PutObject and GetObject) to the AWS IoT Greengrass position alias in order that this IoT machine can entry Amazon S3 based on these directions.

Step 3: Launch the UI element

Elements are constructing blocks that allow simple creation of advanced workflows reminiscent of native knowledge processing, messaging, knowledge administration and machine studying inference. With the most recent IoT Factor Group definition from AWS IoT Greengrass V2, any updates made by UI designer could be effectively orchestrated to all vacation spot targets by revising the element deployment on the AWS IoT Factor group.

The next steps present the way to deploy the earlier packaged ReactJS UI code as an edge element on AWS Greengrass V2.
1. Change the listing to elements/edgeui/aws-gg-deploy within the listing the place the GIT repository was cloned to the Amazon EC2 occasion.

2. Modify the deployment script by changing the next placeholders along with your personalized values in _setEnv() part:

S3_BUCKET for edge element artifacts

Press Ctrl-X, then press Y to avoid wasting the modified file with the identical identify.

3. Run the next script to deploy this element:

chmod 744

This bash script takes roughly 90 seconds to complete. As soon as it exists, please verify that you’ve the next element created in your AWS IoT Greengrass V2 console, and its newest standing is proven as working:

Determine 3: UI element standing proven in AWS IoT Greengrass console.

Please word this UI element comprises all UI pages utilized in each use instances of this two-part weblog.

Step 4: Launch a JSON file add software

Subsequent, the JSON file add element could be launched. This element makes use of a Flask software to obtain the JSON file knowledge from the entrance UI element. As soon as it receives the JSON file, it would ship the JSON file to Amazon S3 by utilizing the stream supervisor element. The stream supervisor element will likely be launched with the file add element by defining it as a element dependency within the recipe file as:

    VersionRequirement: '^2.0.0'
    VersionRequirement: '^2.0.0'
    DependencyType: HARD

To launch the JSON file add element, you should utilize the identical step as Step 3 to switch the deployment script  in elements/com.fileUploader/aws-gg-deploy folder.

Subsequent, modify the recipe-file-template.yaml file in the identical listing by including arguments of your S3 bucket identify that was setup in Step 1 in Run command of Manifests part as:

Run: Script: python3 -u {artifacts:decompressedPath}/$artifacts_zip_file_name/$artifacts_entry_file -b="Exchange along with your Amazon S3 bucket identify"

Please substitute the default Amazon S3 bucket identify with your individual Amazon S3 bucket you created in step 1. Save this recipe template file with Ctrl-X, then press Y to avoid wasting the modified recipe file with the identical identify.

Lastly, run the identical instructions as proven in Step 3, to execute

This bash script takes roughly 30 seconds to complete. As soon as it exists, please verify that you’ve the next element created in your AWS IoT Greengrass V2 console, and its newest standing is proven as working:

Determine 4: Standing of the JSON file add element proven in AWS IoT Greengrass console.

Step 5: Check the JSON file add software

Open an internet browser in your laptop computer, and log in UI with the next tackle:


The next file add UI web page needs to be proven:

Determine 5: JSON file add UI web page.

Choose Add run configuration file from the center field of the UI and add a JSON file of your selection. There may be one pattern JSON file supplied in the Github repository (within the folder: jsonfile_sample)so that you can add for testing functions, and it comprises pattern metadata for a wind turbine inspection job. As soon as you choose the file, select Begin inspection to ship the file knowledge to the com.fileUploader element and the file knowledge will likely be uploaded to the Amazon S3 bucket underneath the folder ggstreamdata as:

Determine 6: JSON file uploaded to S3 by utilizing the Filer Uploader software.

Abstract of half 1

This weblog exhibits a UI software on the edge, collectively developed by AWS Skilled Companies and AWS Accomplice, TensorIoT, for distant industrial job monitoring and processing. Partially 1 of this two-part collection, you discovered the way to use the most recent AWS IoT Greengrass V2 run time to develop a file add software for ingesting unmapped IoT metadata. With AWS IoT Greengrass V2 run time and pre-built elements, you possibly can speed up your growth efforts for an IoT software on the edge. The modularized elements developed on this software could be simply deployed and up to date to a number of IoT units.

Partially 2, you’ll learn to develop a second IoT software on the edge to observe distant IoT jobs with a customized Greengrass element for processing real-time IoT knowledge. Subsequent, you’ll learn to use a pre-built Greengrass element, log supervisor, for monitoring Greengrass element well being. Lastly, it is possible for you to to check the performance of the IoT software on the edge.

Name to motion

Please end the entire resolution by visiting half 2 of this weblog publish collection.

In regards to the creator

Julia Hu is a Sr. AI/ML Options Architect with Amazon Net Companies. She has in depth expertise in IoT structure and Utilized Knowledge Science, and is a part of each the Machine Studying and IoT Technical Area Group. She works with clients, starting from start-ups to enterprises, to develop AWSome IoT machine studying (ML) options, on the Edge and within the Cloud. She enjoys leveraging newest IoT and large knowledge expertise to scale up her ML resolution, scale back latency, and speed up business adoption.
Martin Lehofer is Follow Supervisor for Industrial Knowledge with Amazon Net Companies. Martin has in depth Industrial IoT expertise in a variety of verticals and purposes; from Predictive Upkeep, Knowledge Analytics, Distributed Computing to Synthetic Intelligence. At AWS Skilled Companies, Martin leads a group of architects and engineers serving to strategic industrial clients to attain their enterprise outcomes utilizing knowledge analytics.
Joyson Neville Lewis is an IoT Knowledge Architect at AWS Skilled Companies. He has labored as a Software program/Knowledge engineer earlier than diving into the Conversational AI and Industrial IoT house the place he works with firms to attach the dots between enterprise and AI utilizing Voice Assistant/Chatbot and IoT options.
Tanya Lobo Parmar is a Director with TensorIoT, overseeing operations within the EMEA area. She is focussed on getting extra clients in European markets to modernize and develop utilizing AWS Cloud. She additionally enjoys managing difficult initiatives in IoT, Machine Studying and Managed AI.
Vidya Ramaswamy is a Senior Software program Engineer at TensorIoT, an AWS Accomplice and has 8+ years of expertise in software program growth. She enjoys coding and growing numerous IoT options.
Rajeev Pulleti is a Frontend Engineer from TensorIoT with proficient information in Swift, Goal-C, and Javascript.


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