1. Real-time Asset Tracking in Supply Chain with AWS Location Service


    To implement real-time asset tracking in a supply chain using AWS Location Service with Pulumi, you will need to create several AWS resources:

    1. A Tracker to collect location data from devices.
    2. A Geofence Collection to monitor when tracked items enter or leave a geofenced area.
    3. A Map to visualize the movement of items on a map.
    4. Optionally, Place Index resources if you wish to convert addresses into coordinates or vice versa.

    Below is a Pulumi program written in Python that sets up these AWS Location Service resources.

    import pulumi import pulumi_aws as aws # Create a new map resource # This allows you to display asset locations on a map. map_resource = aws.location.Map("MyMap", map_name="MyMapResource", configuration={ "style": "VectorHereBerlin" # You can choose different map styles provided by Location Service. }, tags={ "Environment": "production", "Name": "MyMap", }) # Create a new geofence collection # This is used to monitor assets entering or exiting a geofence you define. geofence_collection = aws.location.GeofenceCollection("MyGeofenceCollection", collection_name="MyGeofenceCollectionResource", description="This collection contains my geofences for asset tracking", tags={ "Environment": "production", "Name": "MyGeofenceCollection", }) # Create a new tracker resource # The tracker receives location updates from devices to enable you to track them. tracker = aws.location.Tracker("MyTracker", tracker_name="MyTrackerResource", description="Tracker for monitoring asset locations in real-time", tags={ "Environment": "production", "Name": "MyTracker", }) # Create a Place Index resource if needed # It provides ability to search for places or geocode addresses. place_index = aws.location.PlaceIndex("MyPlaceIndex", index_name="MyPlaceIndexResource", data_source="Esri", # You can choose between Esri and Here as data sources. data_source_configuration={ "intendedUse": "SingleUse", # Define the intended use as 'SingleUse' or 'Storage'. }, tags={ "Environment": "production", "Name": "MyPlaceIndex", }) # Export the outputs of the resources which can be used in other parts of your infrastructure. pulumi.export("map_name", map_resource.map_name) pulumi.export("geofence_collection_name", geofence_collection.collection_name) pulumi.export("tracker_name", tracker.tracker_name) pulumi.export("place_index_name", place_index.index_name)

    Each resource is tagged with an Environment for easier resource management and a Name for identification. The map_resource allows us to visualize the tracked assets. The geofence_collection lets us define geographical boundaries and monitor assets entering or leaving them. The tracker tracks the assets' locations for real-time updates. The place_index is optional; it could be helpful if your use case involves address geocoding.

    Remember, before running such a Pulumi program, you should have configured your AWS credentials and Pulumi CLI correctly. This script should be placed in a __main__.py file in your Pulumi project directory.

    This program provides the basic infrastructure for your asset tracking system. However, you will need to build your application logic to handle location updates from your assets and interact with these resources. The AWS SDK for your programming language can be used to programmatically access the AWS Location Service from your application code.