Kafka + MQTT: A Scalable Networked Platform for Big Data and IoT Applications

Cloud Technologies
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Kafka + MQTT: A Scalable Networked Platform for Big Data and IoT Applications

In today’s data-driven world, the convergence of big data, IoT, and machine learning is revolutionizing the way we process and analyze vast amounts of information. At the heart of this revolution lies a scalable networked platform that can efficiently handle the sheer volume of data generated by IoT devices. In this article, we will delve into the details of building such a platform using Apache Kafka, MQTT, and deep learning techniques.

A Scalable Central Nervous System: Apache Kafka

Apache Kafka is a distributed streaming platform that serves as a scalable central nervous system for our networked platform. Its ability to handle high-throughput and provide low-latency data processing makes it an ideal choice for our use case. We will leverage Kafka’s features, such as Kafka Streams and KSQL, to build a real-time data processing pipeline that can handle millions of events from connected equipment.

MQTT: A Standardized Protocol for IoT Communication

MQTT (Message Queuing Telemetry Transport) is a lightweight, publish-subscribe-based messaging protocol that is widely used in IoT applications. Its simplicity and efficiency make it an ideal choice for our platform. We will use MQTT to communicate between Kafka and IoT devices, reducing the effort and costs associated with traditional MQTT broker setups.

Deep Learning for Anomaly Detection

Deep learning is a key component of our platform, enabling us to build sophisticated models that can detect anomalies in IoT data. We will use H2O’s deep learning capabilities to train models that can predict anomalies in sensor data. Our model will be deployed as a KSQL UDF (User-Defined Function), allowing us to easily integrate it into our Kafka Streams pipeline.

Implementation Details

Our implementation involves the following key components:

  1. Kafka Streams: We will use Kafka Streams to build a real-time data processing pipeline that can handle millions of events from connected equipment.
  2. KSQL: We will use KSQL to define a stream processing application that can integrate with our deep learning model.
  3. MQTT: We will use MQTT to communicate between Kafka and IoT devices, reducing the effort and costs associated with traditional MQTT broker setups.
  4. Deep Learning: We will use H2O’s deep learning capabilities to train models that can predict anomalies in sensor data.

Code Snippets

Here is the code snippet for our KSQL UDF:

package com.github.megachucky.kafka.streams.machinelearning;

import java.util.Arrays;
import hex.genmodel.GenModel;
import hex.genmodel.easy.EasyPredictModelWrapper;
import hex.genmodel.easy.RowData;
import hex.genmodel.easy.exception.PredictException;
import hex.genmodel.easy.prediction.AutoEncoderModelPrediction;
import io.confluent.ksql.function.udf.Udf;
import io.confluent.ksql.function.udf.UdfDescription;

@UdfDescription(name = "anomaly", description = "anomaly detection using deep learning")
public class Anomaly {

  // Model built with H2O R API:
  // anomaly_model <- h2o.deeplearning(x = names(train_ecg), training_frame = train_ecg, activation = "Tanh", autoencoder = TRUE, hidden = c(50,20,50), sparse = TRUE, l1 = 1e-4, epochs = 100)
  private static String modelClassName = "io.confluent.ksql.function.udf.ml" + ".DeepLearning_model_R_1509973865970_1";

  @Udf(description = "apply analytic model to sensor input")
  public String anomaly(String sensorinput) {
    System.out.println("Kai: DL-UDF starting");
    GenModel rawModel;
    try {
      rawModel = (hex.genmodel.GenModel) Class.forName(modelClassName).newInstance();
      EasyPredictModelWrapper model = new EasyPredictModelWrapper(rawModel);

      // Prepare input sensor data to be in correct data format for the autoencoder model (double[]):
      String[] inputStringArray = sensorinput.split("#");
      double[] doubleValues = Arrays.stream(inputStringArray).mapToDouble(Double::parseDouble).toArray();
      RowData row = new RowData();
      int j = 0;
      for (String colName : rawModel.getNames()) {
        row.put(colName, doubleValues[j]);
        j++;
      }
      AutoEncoderModelPrediction p = model.predictAutoEncoder(row);

      // Calculate Mean Square Error => High reconstruction error means anomaly
      double sum = 0;
      for (int i = 0; i < p.original.length; i++) {
        sum += (p.original[i] - p.reconstructed[i]) * (p.original[i] - p.reconstructed[i]);
      }
      double mse = sum / p.original.length;
      System.out.println("MSE:" + mse);
      String mseString = "" + mse;
      return mseString;
    } catch (InstantiationException | IllegalAccessException | ClassNotFoundException e) {
      System.out.println(e.toString());
    } catch (PredictException e) {
      System.out.println(e.toString());
    }
    return null;
  }
}

Running the Demo

To run the demo, follow these steps:

  1. Install Confluent Platform.
  2. Deploy the UDF using the ksql-udf-deep-learning-mqtt-iot project.
  3. Create events and deal with them using the ksql-fork-with-deep-learning-function project.

Conclusion

In this article, we have demonstrated how to build a scalable networked platform using Apache Kafka, MQTT, and deep learning techniques. Our platform can efficiently handle the sheer volume of data generated by IoT devices and detect anomalies in real-time. We have also provided code snippets and implementation details to help you get started with your own project.

GitHub Projects

  • ksql-udf-deep-learning-mqtt-iot: A GitHub project that provides the KSQL UDF for deep learning anomaly detection.
  • ksql-fork-with-deep-learning-function: A GitHub project that provides the KSQL fork with deep learning function.

References