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Cover image for Cloud vs Edge vs Local Architecture for Security Camera
Irene Koner
Irene Koner

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Cloud vs Edge vs Local Architecture for Security Camera

#ai #blockchain #security #cloudcomputing

1. Cloud Computing Architecture

Working

  • Security camera continuously streams video to the cloud server.
  • The cloud handles storage, AI video analytics, and event detection.
  • Alerts/notifications are pushed to the user’s mobile/PC app.
  • Playback and monitoring happen from anywhere using the internet.

Diagram

+-------------+      Internet      +----------------+
| Security    | -----------------> | Cloud Server   |
| Camera      |                    | (Processing,   |
| (Video Feed)| <----------------- | Storage, AI)   |
+-------------+      Alerts        +----------------+
                                      |
                                      v
                                 +-----------+
                                 |   User    |
                                 +-----------+
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Pros

  • Scalable: Easy to connect unlimited devices.
  • Accessible Anywhere: Remote monitoring from any location.
  • Centralized Management: All processing and updates happen in the cloud.

Cons

  • High Latency: Depends on internet speed.
  • Privacy Risks: Data stored on third-party servers.
  • Recurring Costs: Requires subscription/storage charges.
  • Downtime Issues: If cloud is down, system may fail.

2. Edge Computing Architecture

Working

  • Camera sends data to a local edge device/gateway (e.g., router, small server, AI box).
  • Edge device does real-time AI processing (motion detection, anomaly recognition).
  • Only filtered/processed data is sent to the cloud for backup or analytics.
  • User receives instant alerts with reduced bandwidth usage.

Diagram

+-------------+     Local Network    +---------------+      Internet     +----------------+
| Security    | -------------------> | Edge Device   | ----------------> | Cloud Server   |
| Camera      |                      | (AI, Filtering|                   | (Storage,      |
| (Video Feed)| <------------------- |  Processing)  | <---------------- | Analytics)     |
+-------------+     Quick Alerts     +---------------+     Summary       +----------------+
        |                                                             
        v                                                              
    +-----------+                                                      
    |   User    |                                                      
    +-----------+
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Pros

  • Low Latency: Faster alerts since AI runs locally.
  • Bandwidth Efficient: Sends only relevant data to the cloud.
  • Better Security: Sensitive data can stay local.
  • Works Offline (partially): Basic detection works even without internet.

Cons

  • Costly Setup: Requires edge devices (AI-enabled gateways).
  • Limited Power: Edge devices have lower processing capacity than cloud.
  • Maintenance: Requires local updates and management.

3. Local Computing Architecture

Working

  • Security camera streams video directly to a local server or NVR/DVR.
  • Processing, storage, and playback are done locally.
  • User can access data only within the LAN (Local Area Network).
  • No dependency on cloud or internet.

Diagram

+-------------+       Local Network       +----------------+
| Security    | ------------------------> | Local Server / |
| Camera      |                           | NVR (Storage & |
| (Video Feed)| <------------------------ | Processing)    |
+-------------+       Alerts/Playback     +----------------+
                                             |
                                             v
                                        +-----------+
                                        |   User    |
                                        +-----------+
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Pros

  • Very Low Latency: Processing happens instantly.
  • Most Secure: Data never leaves the local network.
  • No Internet Required: Works fully offline.
  • One-time Cost: No recurring subscription fees.

Cons

  • Limited Scalability: Adding more cameras needs more local storage.
  • No Remote Access (unless configured via VPN).
  • High Upfront Cost: Expensive NVR/DVR hardware.
  • Maintenance Burden: User must manage backups and updates.

Comparison Table (Cloud vs Edge vs Local)

Feature Cloud Computing Edge Computing Local Computing
Working Camera → Cloud Server → User Camera → Edge Device → Cloud/User Camera → Local Server/NVR → User
Processing Cloud servers Nearby edge device (AI gateway) Local NVR/Server
Latency High (depends on internet) Low (near-real time) Very Low (LAN only)
Scalability Very high, easy to add devices Moderate (depends on edge capacity) Limited (hardware-bound)
Reliability Requires internet/cloud uptime Works with weak internet (partially offline) Fully offline capable
Security Risk of privacy breach (cloud storage) Safer (less data goes to cloud) Most secure (data stays local)
Cost Ongoing subscription/storage costs Medium (edge device + some cloud storage) High upfront cost, no recurring fees
Best Use Case Large-scale, remote access Real-time AI alerts + cloud backup Small setups, secure offline monitoring

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