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Edge AI Deployment for Low-Latency IoT Applications Using Google Cloud IoT Edge

IoT Sensor Data

AI-Powered Anomaly Detection in IoT Networks Using Google Cloud

  • Use Case : Applications like autonomous vehicles, smart factories, and real-time video analytics require ultra-low latency for processing IoT data. Sending all data to the cloud introduces delays and bandwidth costs. Deploying AI models on edge devices ensures real-time inference and faster decision-making while reducing cloud dependency.

Objective

  • Deploy AI/ML models on edge devices to perform real-time data processing close to the source.

    Reduce latency and bandwidth usage for IoT applications.

    Ensure scalable, manageable, and secure deployment of edge AI workloads using Google Cloud IoT Edge.

    Integrate edge inference with cloud analytics and monitoring for hybrid AI solutions.

Project Description

  • This project implements an edge AI deployment pipeline for IoT applications:

    IoT Device Setup: Connect IoT sensors and devices (cameras, environmental sensors, industrial machines) to Cloud IoT Core.

    Edge AI Model Deployment: Deploy pre-trained ML models (e.g., object detection, anomaly detection) on edge devices using Edge TPU / IoT Edge runtime.

    Real-Time Inference: Perform AI inference locally on devices to detect anomalies, classify objects, or trigger alerts.

    Cloud Integration: Send summarized insights, anomalies, or model updates to the cloud for storage, analytics, or further processing.

    Monitoring & Management: Use Cloud Monitoring and IoT Core for managing edge devices, deploying model updates, and monitoring performance.

    Automation & Alerts: Trigger Cloud Functions or other workflows based on edge-inferred events (e.g., machine fault detection, security alerts).

Key Technologies & Google Cloud Platform Services

  • GCP Service Purpose
    Cloud IoT Core Securely connects and manages IoT devices; streams data to cloud or edge.
    Edge TPU / IoT Edge Devices Runs lightweight AI inference locally with low latency; accelerates edge ML workloads.
    Pub/Sub Streams events and edge-generated insights from devices to cloud analytics pipelines.
    Dataflow Processes aggregated data from multiple edge devices for further cloud analytics.
    Vertex AI / Cloud ML Train models in the cloud and deploy optimized versions to edge devices.
    Cloud Functions Event-driven automation triggered by edge-inferred anomalies or decisions.
    BigQuery Stores aggregated edge and cloud data for historical analysis and reporting.
    Cloud Monitoring / Logging Tracks edge device performance, inference latency, and connectivity.
    Cloud Storage Stores model artifacts, device logs, and edge-generated data for archival.
    Cloud Key Management Service (KMS) Ensures secure storage and transfer of sensitive model and sensor data.