<\/span><\/h3>\nAfter you have modified the configuration by including publish on change within the earlier step, we are going to restart AWS IoT System Consumer.<\/p>\n
Run the next command to restart:<\/p>\n
sudo systemctl restart aws-iot-device-client<\/code><\/pre>\n<\/span>Step 3: Automobile knowledge simulation<\/span><\/h3>\nLet\u2019s setup the related automobile simulation knowledge generator and stream to AWS IoT Core. We are going to create the file (vehicle_data_generator.py<\/em>) and run this to continually stream random knowledge which can comprise automobile standing, DTCs (Diagnostic Hassle Codes), location, driver behaviour, and battery standing.<\/p>\nRun the next command to setup the file and obtain the code:<\/p>\n
cd \/residence\/ubuntu\/workshop_dc\nvim vehicle_data_generator.py\n<\/code><\/pre>\nEnter the next code within the file (vehicle_data_generator.py<\/em>):<\/p>\nimport json\nimport time\nimport random\nimport logging\nfrom datetime import datetime, timezone\nfrom pathlib import Path\n\n# Arrange logging\nlogging.basicConfig(degree=logging.INFO, format=\"%(asctime)s - %(levelname)s - %(message)s\")\nlogger = logging.getLogger(__name__)\n\n# File path\nFILE_PATH = Path(\"\/residence\/ubuntu\/workshop_dc\/pubfile.txt\")\n\ndef generate_vehicle_status():\n return {\n \"vehicleId\": \"VIN123456789\",\n \"timestamp\": datetime.now(timezone.utc).isoformat(),\n \"standing\": {\n \"ignition\": random.alternative([\"ON\", \"OFF\"]),\n \"pace\": spherical(random.uniform(0, 120), 1),\n \"fuelLevel\": spherical(random.uniform(0, 100), 1),\n \"batteryLevel\": spherical(random.uniform(0, 100), 1),\n \"odometer\": spherical(random.uniform(0, 100000), 1),\n \"engineTemp\": spherical(random.uniform(70, 110), 1),\n \"tirePressure\": {\n \"frontLeft\": spherical(random.uniform(30, 35), 1),\n \"frontRight\": spherical(random.uniform(30, 35), 1),\n \"rearLeft\": spherical(random.uniform(30, 35), 1),\n \"rearRight\": spherical(random.uniform(30, 35), 1)\n }\n }\n }\n\ndef generate_dtcs():\n return {\n \"vehicleId\": \"VIN987654321\",\n \"timestamp\": datetime.now(timezone.utc).isoformat(),\n \"dtcs\": [\n {\n \"code\": \"P0\" + str(random.randint(100, 999)),\n \"description\": \"Random DTC Description\",\n \"severity\": random.choice([\"WARNING\", \"CRITICAL\", \"INFO\"])\n }\n ]\n }\n\ndef generate_location():\n return {\n \"vehicleId\": \"VIN246813579\",\n \"timestamp\": datetime.now(timezone.utc).isoformat(),\n \"location\": {\n \"latitude\": spherical(random.uniform(30, 45), 4),\n \"longitude\": spherical(random.uniform(-125, -70), 4),\n \"altitude\": spherical(random.uniform(0, 1000), 1),\n \"heading\": spherical(random.uniform(0, 359), 1),\n \"pace\": spherical(random.uniform(0, 120), 1)\n }\n }\n\ndef generate_driver_behavior():\n return {\n \"vehicleId\": \"VIN135792468\",\n \"timestamp\": datetime.now(timezone.utc).isoformat(),\n \"driverBehavior\": {\n \"harshAccelerations\": random.randint(0, 5),\n \"harshBraking\": random.randint(0, 5),\n \"speedingEvents\": random.randint(0, 10),\n \"averageSpeed\": spherical(random.uniform(40, 80), 1),\n \"idlingTime\": random.randint(0, 600),\n \"fuelEfficiency\": spherical(random.uniform(20, 40), 1)\n }\n }\n\ndef generate_battery_status():\n return {\n \"vehicleId\": \"VIN753951456\",\n \"timestamp\": datetime.now(timezone.utc).isoformat(),\n \"batteryStatus\": {\n \"stateOfCharge\": spherical(random.uniform(0, 100), 1),\n \"vary\": spherical(random.uniform(0, 300), 1),\n \"chargingStatus\": random.alternative([\"CHARGING\", \"NOT_CHARGING\"]),\n \"voltage\": spherical(random.uniform(350, 400), 1),\n \"present\": spherical(random.uniform(-200, 200), 1),\n \"temperature\": spherical(random.uniform(20, 40), 1),\n \"healthStatus\": random.alternative([\"GOOD\", \"FAIR\", \"POOR\"])\n }\n }\n\ndef write_to_file(knowledge):\n attempt:\n # Make sure the listing exists\n FILE_PATH.dad or mum.mkdir(dad and mom=True, exist_ok=True)\n \n # Write the info to the file\n with FILE_PATH.open('w') as f:\n json.dump(knowledge, f)\n logger.data(f\"Efficiently wrote knowledge to {FILE_PATH}\")\n besides PermissionError:\n logger.error(f\"Permission denied when making an attempt to write down to {FILE_PATH}\")\n besides IOError as e:\n logger.error(f\"I\/O error occurred when writing to {FILE_PATH}: {e}\")\n besides Exception as e:\n logger.error(f\"Surprising error occurred when writing to {FILE_PATH}: {e}\")\n\ndef primary():\n turbines = [\n generate_vehicle_status,\n generate_dtcs,\n generate_location,\n generate_driver_behavior,\n generate_battery_status\n ]\n\n whereas True:\n attempt:\n knowledge = random.alternative(turbines)()\n write_to_file(knowledge)\n time.sleep(10)\n besides KeyboardInterrupt:\n logger.data(\"Script terminated by consumer\")\n break\n besides Exception as e:\n logger.error(f\"An surprising error occurred: {e}\")\n time.sleep(10) # Wait earlier than retrying\n\nif __name__ == \"__main__\":\n attempt:\n primary()\n besides Exception as e:\n logger.essential(f\"Crucial error occurred: {e}\")\n<\/code><\/pre>\nAfter you have copied over the code (or file) then run the code utilizing the next command:<\/p>\n
python3 vehicle_data_generator.py<\/code><\/p>\nUpon a profitable run you will note:<\/p>\n
INFO \u2013 Efficiently wrote knowledge to \/residence\/ubuntu\/workshop_dc\/pubfile.txt<\/em><\/p>\nIn AWS IoT Core console, navigate to:<\/p>\n
\n- Check\n
\n- MQTT take a look at shopper\n
\n- Subscribe to subject: \/subject\/workshop\/dc\/pub<\/em><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
It’s best to see the stream of knowledge arriving; that is similar knowledge we are going to use for analytics.<\/p>\n
![\"\"](\"https:\/\/d2908q01vomqb2.cloudfront.net\/f6e1126cedebf23e1463aee73f9df08783640400\/2024\/08\/13\/fig1.6.png\")
<\/p>\n
Determine 1.6: MQTT subject exhibiting knowledge arriving into AWS IoT Core<\/em><\/p>\n