Handle IoT machine state anyplace utilizing AWS IoT System Shadow service and AWS IoT Greengrass


Web of Issues (IoT) builders typically have to implement a sturdy mechanism for managing IoT machine state both regionally or remotely. A typical instance is a brilliant dwelling radiator, an IoT machine the place you need to use the built-in management panel to regulate the temperature (machine state), or set off temperature adjustment messages remotely from a software program software operating within the cloud.

You may shortly construct this mechanism through the use of the AWS IoT System Shadow service. The AWS IoT System Shadow service could make a tool’s state accessible to your online business logic, even within the case of intermittent community connection.

As well as, to effectively handle your machine’s software program lifecycle and speed up your growth efforts, you need to use AWS IoT Greengrass together with its pre-built elements. AWS IoT Greengrass is an open-source edge runtime and cloud service for constructing, deploying, and managing machine software program. One of many elements of AWS IoT Greengrass is the shadow supervisor, which permits the native shadow service in your core machine. The native shadow service permits elements to make use of interprocess communication (IPC) to work together with native shadows. The shadow supervisor part manages the storage of native shadow paperwork, and in addition handles synchronization of native shadow states with the AWS IoT System Shadow service.

On this weblog submit, I’m utilizing AWS IoT System Shadow service and AWS IoT Greengrass along with a Raspberry Pi and Sense HAT {hardware} to simulate a wise dwelling radiator. This demonstration makes use of a single digit quantity (0 – 9) to simulate the output energy. This quantity is the machine state that we need to handle from anyplace, native and distant. The consumer can change this quantity by means of an area {hardware} change (the built-in joystick on the Sense HAT) in addition to remotely from a cloud-based software.

The Raspberry Pi exhibits the quantity on the Sense HAT LED show, indicating the radiator output energy. The consumer can push up on the joystick on the Sense HAT to extend the quantity (or push all the way down to lower it).

Raspberry Pi simulating home radiator
Determine 1. Raspberry Pi – simulating dwelling radiator

Architecture overview
Determine 2. Structure overview

By following this weblog submit, you’ll be able to shortly begin constructing and testing your IoT options for managing your machine’s state anyplace.


To observe by means of this weblog submit, you will have:


Software program:


Step 1: Set up and configure the AWS IoT Greengrass core software program on the Raspberry Pi.

In an effort to make your Raspberry Pi as an AWS IoT Greengrass core machine, observe step 1 to step 3 within the AWS IoT Greengrass Getting began doc. I created the machine with the next configuration:

  • Core machine title: PiWithSenseHat
  • Factor group: RaspberryPiGroup

Now you need to be capable to see this machine in your AWS console.

AWS IoT Greengrass core device in console
Determine 3. AWS IoT Greengrass core machine in console

Step 2: Deploy prebuilt AWS IoT Greengrass elements to the machine

The subsequent step is to deploy prebuilt AWS IoT Greengrass elements to the machine. AWS IoT Greengrass offers and maintains a set of prebuilt elements that may speed up our growth. On this demonstration, I’m deploying the next elements:

  • greengrass.Cli:
    Offers an area command-line interface that you need to use on core gadgets to develop and debug elements regionally
  • greengrass.ShadowManager
    Permits the native shadow service in your core machine and handles synchronization of native shadow states with the AWS IoT System Shadow service
  • greengrass.LocalDebugConsole (optionally available)
    Offers an area dashboard that shows details about your AWS IoT Greengrass core gadgets and its elements


  1. Go to AWS IoT Greengrass console
  2. Navigate to Deployment in Greengrass gadgets, create a brand new deployment
  3. Deployment goal might be both Factor group RaspberryPiGroup, or Core machine
  4. Choose these 3 elements from Public elements

Select the prebuilt components
Determine 4. Choose the prebuilt elements

  1. Configure aws.greengrass.ShadowManager part

In Configure elements step, choose aws.greengrass.ShadowManager, then click on Configure part

Configure aws.greengrass.ShadowManager
Determine 5. Configure aws.greengrass.ShadowManager

  1. Arrange part model and configuration json of aws.greengrass.ShadowManager

Configure aws.greengrass.ShadowManager details
Determine 6. Configure aws.greengrass.ShadowManager – particulars

  • Model: 2.3.1
  • Configuration to merge:  
  "synchronize": {
    "coreThing": {
      "traditional": true,
      "namedShadows": [
    "shadowDocuments": [],
    "course": "betweenDeviceAndCloud"
  "rateLimits": {
    "maxOutboundSyncUpdatesPerSecond": 100,
    "maxTotalLocalRequestsRate": 200,
    "maxLocalRequestsPerSecondPerThing": 20
  "shadowDocumentSizeLimitBytes": 8192

The json configuration synchronizes a named shadow, referred to as NumberLEDNamedShadow on this instance, in each instructions, betweenDeviceAndCloud possibility. In your real-world software, you may use a number of named shadows, and with 1 means or bi-directional synchronization. Examine the main points of the aws.greengrass.ShadowManager in its doc.

  1. Full the Create Deployment wizard to complete the deployment.

On the finish of the Step 2, the Raspberry Pi is able to synchronize a named shadow NumberLEDNamedShadow between machine and cloud, through the use of AWS IoT Greengrass core software program and the prebuilt part.

Step 3: Create AWS IoT Greengrass elements for simulating good dwelling radiator with native management

Now create two AWS IoT Greengrass elements for simulating a wise dwelling radiator with native management. We are able to leverage interprocess communication (IPC) for the interior communication between the elements. If you’re not conversant in how one can construct customized AWS IoT Greengrass elements, please observe step 4 within the Getting began doc. On this weblog, we create and take a look at them regionally.

  1. Part instance.sensehat.joystick: Seize the occasions from the joystick and publish the occasions to an IPC matter “ipc/joystick” (It was outlined as a variable within the recipe).
  2. Part instance.sensehat.led: Subscribe the IPC matter “ipc/joystick”, replace the native shadow and the Sense HAT LED show.

3.1 Create part com.instance.sensehat.joystick

This part is publishing occasions of the built-in joystick to AWS IoT Greengrass core IPC. The occasion is like:


You will discover the part recipe and artifact from weblog supply code repo. As a substitute of laborious coding the IPC matter within the supply code, it’s outlined within the recipe as a variable.

3.2 Create part com.instance.sensehat.led

Now create the second part named com.instance.sensehat.led. You will discover the part recipe and artifact within the supply code repo. Within the recipe it defines the entry permission to IPC and shadow paperwork.

This part:

  • Maintains a quantity as machine state, and shows it on LED
  • Subscribes to joystick occasion matter by way of IPC
  • Primarily based on the acquired joystick occasion, improve/lower the quantity
  • Periodically checks the shadow. If there’s a new quantity within the shadow doc, replace the machine with that quantity.

Workflow of com.example.sensehat.led component
Determine 7. Workflow of com.instance.sensehat.led part

Demo: Handle the machine state in motion

Now the Raspberry Pi as simulator is prepared to be used.

It responds to 2 varieties of occasions:

  • New joystick occasion: Management the machine regionally
  • New cloud shadow doc: Management the machine remotely

Logic to control the device either locally or remotely
Determine 8: Logic to manage the machine both regionally or remotely

To see the machine shadow in motion:

  1. Go to AWS IoT Greengrass console
  2. Navigate to Issues, choose PiWithSenseHat
  3. In System Shadows, you could find the NumberLEDNamedShadow. Notice that you do not want to manually create this shadow. When machine studies again the shadow for the primary time, it would create it for you whether it is lacking.

locate the device shadow in AWS console
Determine 9. find the machine shadow in AWS console

Demo 1: Replace the machine regionally through the use of joystick

  1. Use the joystick to extend/lower the quantity regionally (The preliminary quantity was 6. I firstly deceased it to 0, then elevated it to 2).
  2. Observe the machine shadow doc is up to date in actual time in AWS console. The change is sync to the cloud shadow in the actual time.
    • standing is modified to “machine up to date by native”
    • quantity is modified to the brand new worth from native

Using joystick to update the number
Determine 10: Utilizing joystick to replace the quantity, and report the brand new worth to cloud in actual time

Demo 2: Replace the machine remotely by updating machine shadow doc in cloud

  1. At first of this demo, the machine LED was displaying 0, and machine shadow doc was
      "state": {
        "desired": {
          "quantity": 0
        "reported": {
          "standing": "machine is up to date by native",
          "quantity": 0
  2. In AWS IoT Core console, Edit the shadow doc with the next Json (you’ll be able to skip the “reported” part), then click on Replace
      "state": {
        "desired": {
  1. Observe the Raspberry Pi LED updates the quantity. The change is pushed from cloud to native machine. Now the machine is displaying quantity 9:
    • standing is modified to “machine up to date by shadow”
    • quantity is modified from 0 to 9.

Update the device remotely by updating device shadow document in cloud
Determine 11. Replace the machine remotely by updating machine shadow doc in cloud

Because the show quantity will be up to date both by native joystick or remotely from AWS console, the newest replace takes priority. Subsequently when the replace is completed regionally, it will be significant to set the “desired” worth again to distant shadow in cloud, so the distant shadow is aware of the brand new “desired” worth and won’t replace it within the subsequent shadow sync cycle. See extra on the doc device-shadow-empty-fields.

Cleansing up

  • Delete/disable IAM consumer which you used for putting in AWS IoT Greengrass core software program in Raspberry Pi
  • Below AWS IoT console, navigate to Greengrass gadgets
    • In Core System choose the machine PiWithSenseHat and Hit delete on prime proper.
    • In Factor teams delete RaspberryPiGroup
  • Take away these two customized elements from Raspberry Pi
    • Run the next instructions within the terminal on Raspberry Pi
      • sudo /greengrass/v2/bin/greengrass-cli --ggcRootPath /greengrass/v2 deployment create --remove "com.instance.sensehat.led"
      • sudo /greengrass/v2/bin/greengrass-cli --ggcRootPath /greengrass/v2 deployment create --remove "com.instance.sensehat.joystick"
    • Uninstall AWS IoT Greengrass core software program from Raspberry Pi
      • Comply with the steps in this doc to uninstall the AWS IoT Greengrass core software program out of your Raspberry Pi.


On this submit, you discovered how one can use AWS IoT System Shadow service and AWS IoT Greengrass to construct a sturdy resolution for managing IoT machine state, whether or not it’s executed regionally or remotely. Now you can focus by yourself enterprise logic, and let these two AWS companies to do the heavy lifting for managing machine state anyplace. At the moment these two customized elements are created and deployed regionally within the machine. The subsequent step might be making them accessible in AWS IoT Greengrass, so you’ll be able to deploy them to extra gadgets. In an effort to that, you’ll be able to observe step 5 and step 6 in AWS IoT Greengrass doc.

In regards to the writer

Feng Lu

Feng Lu is a Senior Options Architect in AWS with 18 years skilled expertise. He’s keen about serving to organizations to craft scalable, versatile, and resilient architectures that handle their enterprise issues. At the moment his focus is on connecting the bodily world and cloud with IoT applied sciences, and uniting computing/AI capability to make our bodily setting smarter and higher.

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