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IoT Edge Computing and Fog Computing

IoT Edge Computing and Fog Computing are two techniques used in the field of IoT to manage the large amounts of data generated by IoT devices.

  • IoT Edge Computing

IoT Edge Computing involves processing data on the edge of the network, i.e. on the IoT devices themselves or on gateways that sit between the devices and the cloud. This technique can help reduce latency and bandwidth usage, as well as increase reliability and security. Here's an example of how to use Edge Computing in Python using the Edge Impulse library:

python
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import numpy as np
from edge_impulse_linux.image import ImageImpulseRunner

# load the Edge Impulse model
model = ImageImpulseRunner('/path/to/model.eim')

# initialize the camera
camera = Camera()

# process the camera images
while True:
    # capture an image from the camera
    image = camera.capture()

    # preprocess the image
    image = preprocess_image(image)

    # run the Edge Impulse model on the image
    result = model.classify(image)

    # postprocess the result
    result = postprocess_result(result)

    # send the result to the cloud
    send_to_cloud(result)

This code loads an Edge Impulse model, initializes a camera, captures images from the camera, preprocesses the images, runs the Edge Impulse model on the images, postprocesses the results, and sends the results to the cloud.

  • Fog Computing

Fog Computing involves processing data on devices that sit between the edge devices and the cloud, such as gateways or servers located closer to the edge. This technique can help reduce latency and bandwidth usage, as well as provide more processing power and storage than the edge devices themselves. Here's an example of how to use Fog Computing in Python using the Apache NiFi framework:

python
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import nipyapi

# connect to the NiFi instance
nipyapi.config.nifi_config.host = 'http://localhost:8080/nifi-api'

# create a NiFi processor group
processor_group = nipyapi.canvas.create_process_group(
    parent_pg_id='root',
    new_pg_name='My Processor Group'
)

# create a NiFi processor
processor = nipyapi.canvas.create_processor(
    parent_pg_id=processor_group.id,
    processor={
        'config': {
            'type': 'ExecuteStreamCommand',
            'properties': {
                'Command': '/path/to/my/script.sh',
                'Command Arguments': 'arg1 arg2 arg3'
            }
        }
    }
)

# connect the processor to a NiFi input port
input_port = nipyapi.canvas.get_input_ports(processor_group.id)[0]
nipyapi.canvas.create_connection(
    source_id=input_port.id,
    dest_id=processor.id
)

# connect the processor to a NiFi output port
output_port = nipyapi.canvas.get_output_ports(processor_group.id)[0]
nipyapi.canvas.create_connection(
    source_id=processor.id,
    dest_id=output_port.id
)

This code connects to an Apache NiFi instance, creates a NiFi processor group, creates a NiFi processor that executes a shell script with arguments, connects the processor to a NiFi input port and a NiFi output port, and sets up a data flow between the input and output ports. This data flow can be used to process data on the gateway before sending it to the cloud.



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