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AI-Powered Anomaly Detection in IoT Networks Using Google Cloud ML and Pub/Sub

IoT Sensor Data

AI-Powered Anomaly Detection in IoT Networks Using Google Cloud

  • Use Case : Industrial IoT networks, smart homes, and smart city infrastructures generate continuous streams of sensor and device data. Anomalies in these streams—such as unexpected temperature spikes, abnormal vibrations, or unauthorized access—can indicate equipment failures, security breaches, or operational issues. Real-time anomaly detection ensures timely alerts and preventive actions.

Objective

  • Detect anomalies in real-time IoT sensor data streams using AI/ML models.

    Enable proactive maintenance, security monitoring, and operational reliability.

    Use Google Cloud ML and Pub/Sub to handle high-throughput streaming data and scalable ML inference.

    Reduce false positives and latency for real-time decision-making.

Project Description

  • This project implements a real-time anomaly detection system for IoT networks:

    Data Ingestion: IoT sensors (temperature, vibration, pressure, network logs) send data to Pub/Sub.

    Preprocessing: Clean, normalize, and aggregate streaming sensor data using Dataflow.

    ML Model Training: Train anomaly detection models (e.g., Autoencoders, LSTM-based models) using Vertex AI / Cloud ML on historical datasets.

    Real-Time Inference: Deploy trained models to detect anomalies on incoming data streams in near real-time.

    Alerting & Automation: Trigger Cloud Functions to notify operators, trigger automated responses, or isolate faulty devices.

    Monitoring & Visualization: Use dashboards to visualize anomalies, sensor status, and system health metrics.

Key Technologies & Google Cloud Platform Services

  • GCP Service Purpose
    Cloud IoT Core Securely connects and manages IoT devices, collects telemetry data.
    Pub/Sub Streams real-time IoT data into processing pipelines for anomaly detection.
    Dataflow Preprocesses, aggregates, and transforms streaming data for ML inference.
    Vertex AI / Cloud ML Trains and deploys anomaly detection models; provides scalable inference.
    BigQuery Stores historical IoT data for model training, evaluation, and analytics.
    Cloud Functions Triggers real-time alerts or automated responses when anomalies are detected.
    Cloud Storage Stores raw and processed sensor data, model checkpoints, and logs.
    Cloud Monitoring / Logging Tracks data pipeline health, model performance, and anomaly trends.
    Looker / Data Studio Visualizes anomalies, system status, and historical patterns for operators.
    Cloud Key Management Service (KMS) Encrypts IoT and model data for privacy and security compliance.