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Nimish Bordiya
Nimish Bordiya

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Cloud vs Edge vs Local Computing (Security Camera Example)

#architecture #cloudcomputing #iot

Cloud vs Edge vs Local Computing (Security Camera Example)

1. Cloud Computing (Centralized)

  • How it works:

    • Camera records video → uploads raw/processed video to the cloud.
    • Cloud server runs AI (e.g., motion detection, face recognition).
    • Alerts/recordings sent back to user app.

  • Diagram:

[Camera] ---> [Internet] ---> [Cloud Server] ---> [User App]
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  • ✅ Pros:

    • Powerful processing (scalable).
    • Centralized updates & analytics.
    • Easy to manage across many devices.

  • ❌ Cons:

    • High bandwidth usage (video streaming to cloud).
    • Latency (slower response).
    • Privacy concerns (sensitive data stored in cloud).

2. Edge Computing (Near-device Processing)

  • How it works:
    • Camera sends data to a nearby edge device (e.g., router with AI chip, local server, ISP edge node).
    • AI processing (motion detection, object recognition) happens on the edge.
    • Only relevant results/alerts sent to cloud or user.

Diagram:

[Camera] ---> [Edge Device/Local Gateway] ---> [Cloud/Optional] ---> [User App]
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  • ✅ Pros:
    • Reduced bandwidth (only relevant data sent).
    • Faster response (low latency).
    • Balance between performance & cloud convenience.
  • ❌ Cons:
    • Requires investment in edge hardware.
    • Still partial dependency on cloud.
    • Limited compared to cloud’s massive compute.

3. Local Computing (On-device)

  • How it works:

    • Camera itself has built-in AI chip.
    • Processes video locally (motion detection, storage on local drive).
    • Sends alert directly to user app without cloud.

  • Diagram:

[Smart Camera] ---> [User App]   (No Cloud needed)
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  • ✅ Pros:
    • Very low latency (real-time).
    • No internet dependency.
    • High privacy (data stays local).
  • ❌ Cons:
    • Limited storage & compute power.
    • Harder to update/improve AI models.
    • No central management for multiple devices.

Comparison Table

Feature Cloud Computing Edge Computing Local Computing
Latency High (due to internet) Medium (close to device) Very low (real-time)
Privacy Low (data leaves home) Medium (partial local processing) High (data stays local)
Cost Subscription/server cost Edge device setup cost Hardware cost (smart cam)
Processing Power Very high (scalable) Medium (edge hardware dependent) Low (device-limited)
Internet Dependency Always needed Partial Not required

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