IoT Solution: Azure IoT Hub, Stream Analytics, and Real-Time Dashboards

IoT Solution: Azure IoT Hub, Stream Analytics, and Real-Time Dashboards

Introduction

[Explain IoT solution requirements: device connectivity, telemetry ingestion, real-time analytics, alerting, visualization.]

Solution Architecture

Components Overview

Step-by-Step Guide

Step 1: Provision IoT Hub

az iot hub create \
  --resource-group rg-iot \
  --name contoso-iothub \
  --sku S1 \
  --location eastus

Step 2: Register Devices

az iot hub device-identity create \
  --hub-name contoso-iothub \
  --device-id sensor-01

# Get connection string
az iot hub device-identity connection-string show \
  --hub-name contoso-iothub \
  --device-id sensor-01

Step 3: Simulate Device Telemetry

Node.js Device Code:

const { Client } = require('azure-iot-device');
const { Mqtt } = require('azure-iot-device-mqtt');

const connectionString = 'HostName=...';
const client = Client.fromConnectionString(connectionString, Mqtt);

client.open((err) => {
    if (err) {
        console.error('Connection error:', err);
    } else {
        setInterval(() => {
            const telemetry = {
                deviceId: 'sensor-01',
                temperature: 20 + Math.random() * 10,
                humidity: 60 + Math.random() * 20,
                timestamp: new Date().toISOString()
            };
            const message = new Message(JSON.stringify(telemetry));
            client.sendEvent(message, (err) => {
                if (err) console.error('Send error:', err);
                else console.log('Message sent:', telemetry);
            });
        }, 5000);
    }
});

Step 4: Configure Stream Analytics Job

Input: IoT Hub

{
  "name": "iothub-input",
  "type": "Microsoft.StreamAnalytics/streamingjobs/inputs",
  "properties": {
    "type": "Stream",
    "datasource": {
      "type": "Microsoft.Devices/IotHubs",
      "properties": {
        "iotHubNamespace": "contoso-iothub",
        "sharedAccessPolicyName": "service",
        "sharedAccessPolicyKey": "...",
        "endpoint": "messages/events",
        "consumerGroupName": "$Default"
      }
    },
    "serialization": {
      "type": "Json",
      "properties": { "encoding": "UTF8" }
    }
  }
}

Query: Aggregate Telemetry

SELECT
    deviceId,
    System.Timestamp() AS WindowEnd,
    AVG(temperature) AS AvgTemperature,
    AVG(humidity) AS AvgHumidity,
    MAX(temperature) AS MaxTemperature,
    MIN(temperature) AS MinTemperature
INTO [powerbi-output]
FROM [iothub-input]
GROUP BY deviceId, TumblingWindow(minute, 5);

-- Anomaly Detection
SELECT
    deviceId,
    temperature,
    AnomalyDetection_SpikeAndDip(temperature, 95, 120, 'spikesanddips') OVER(LIMIT DURATION(minute, 10)) AS anomalyScore
INTO [alerts-output]
FROM [iothub-input]
WHERE AnomalyDetection_SpikeAndDip(temperature, 95, 120, 'spikesanddips') OVER(LIMIT DURATION(minute, 10)) > 0.8;

Output: Power BI

{
  "name": "powerbi-output",
  "type": "Microsoft.StreamAnalytics/streamingjobs/outputs",
  "properties": {
    "datasource": {
      "type": "PowerBI",
      "properties": {
        "dataset": "IoTTelemetry",
        "table": "SensorData",
        "groupId": "...",
        "refreshToken": "..."
      }
    }
  }
}

Step 5: Azure Function for Alerts

[FunctionName("ProcessAlert")]
public static async Task Run(
    [EventGridTrigger] EventGridEvent eventGridEvent,
    ILogger log)
{
    var alert = JsonConvert.DeserializeObject<IoTAlert>(eventGridEvent.Data.ToString());
    
    if (alert.AnomalyScore > 0.8)
    {
        // Send email via SendGrid
        await SendAlertEmail(alert.DeviceId, alert.Temperature);
        
        // Log to Application Insights
        log.LogWarning($"High temperature alert: Device {alert.DeviceId}, Temp: {alert.Temperature}°C");
    }
}

Step 6: Power BI Real-Time Dashboard

Create Streaming Dataset:

1. Power BI Service → Workspace → New → Streaming Dataset
2. Source: Azure Stream Analytics
3. Fields: deviceId, WindowEnd, AvgTemperature, AvgHumidity

Dashboard Tiles:

• Line chart: AvgTemperature over time
• Card: Current AvgTemperature
• Gauge: MaxTemperature (threshold 30°C)
• Table: Top 5 devices by temperature

Step 7: Device Twin Management

Update Desired Properties:

az iot hub device-twin update \
  --hub-name contoso-iothub \
  --device-id sensor-01 \
  --set properties.desired='{"telemetryInterval": 10000}'

Device Code (Handle Twin Update):

client.getTwin((err, twin) => {
    twin.on('properties.desired', (delta) => {
        if (delta.telemetryInterval) {
            telemetryInterval = delta.telemetryInterval;
            console.log('Updated interval:', telemetryInterval);
        }
    });
});

Step 8: Cold Path Storage (Cosmos DB)

Stream Analytics Output:

SELECT *
INTO [cosmosdb-output]
FROM [iothub-input];

Cosmos DB Configuration:

{
  "datasource": {
    "type": "Microsoft.Storage/DocumentDb",
    "properties": {
      "accountId": "contoso-cosmos",
      "accountKey": "...",
      "database": "IoTData",
      "collectionNamePattern": "telemetry",
      "partitionKey": "/deviceId"
    }
  }
}

Advanced Patterns

Edge Computing with IoT Edge

Deploy Module to Edge Device:

az iot edge set-modules \
  --device-id edge-device-01 \
  --hub-name contoso-iothub \
  --content deployment.json

Module: Local Anomaly Detection

{
  "modulesContent": {
    "$edgeAgent": { ... },
    "$edgeHub": { ... },
    "anomalyDetectionModule": {
      "version": "1.0",
      "type": "docker",
      "settings": {
        "image": "mcr.microsoft.com/azureml/anomaly-detection:latest",
        "createOptions": "{}"
      }
    }
  }
}

Predictive Maintenance

ML Model Integration:

1. Train model (Azure Machine Learning)
2. Deploy as Azure Container Instance
3. Stream Analytics calls scoring endpoint
4. Route predictions to maintenance workflow

Digital Twin Integration

Azure Digital Twins:

var client = new DigitalTwinsClient(new Uri("https://contoso-dt.api.weu.digitaltwins.azure.net"), credential);

var patch = new JsonPatchDocument();
patch.AppendReplace("/Temperature", telemetry.Temperature);
await client.UpdateDigitalTwinAsync("sensor-01-twin", patch);

Security & Compliance

Device Authentication

• Use X.509 certificates for production devices
• Rotate device keys via DPS (Device Provisioning Service)
• Implement device attestation

Data Encryption

• In-transit: TLS 1.2+
• At-rest: Azure Storage encryption, Cosmos DB encryption

Access Control

# Create custom IoT Hub policy
az iot hub policy create \
  --hub-name contoso-iothub \
  --name device-telemetry \
  --permissions RegistryRead DeviceConnect

Monitoring & Troubleshooting

IoT Hub Metrics:

AzureMetrics
| where ResourceProvider == "MICROSOFT.DEVICES"
| where MetricName == "d2c.telemetry.ingress.success"
| summarize TotalMessages = sum(Total) by bin(TimeGenerated, 5m)

Stream Analytics Diagnostics:

AzureDiagnostics
| where ResourceType == "STREAMANALYTICS"
| where Level == "Error"
| project TimeGenerated, OperationName, ResultDescription

Cost Optimization

Best Practices

• Batch device messages when possible
• Use device twins for configuration management
• Implement retry logic with exponential backoff
• Monitor IoT Hub throttling metrics
• Archive historical data to cold storage

Key Takeaways

• IoT Hub simplifies device connectivity and management.
• Stream Analytics enables real-time windowed aggregations.
• Power BI streaming datasets provide instant dashboards.
• Event Grid decouples alerting from core data flow.

Next Steps

• Implement predictive maintenance workflows
• Add Azure Digital Twins for spatial intelligence
• Explore Time Series Insights for historical analysis

Additional Resources

• Azure IoT Hub
• Stream Analytics
• Power BI Streaming
• IoT Edge

What IoT scenario will you build first?