Smart Security Camera

1. Cloud Computing Architecture

How it works:

1. - Camera captures video and sends it to the cloud server.
2. - Cloud processes motion detection, face recognition, and storage.
3. - User accesses footage remotely via the internet. Diagram:

Camera → Internet → Cloud Server → User App

Pros:

1. Centralized data storage (accessible anywhere).
2. High computational power for AI features.
3. Easy updates and scaling.

Cons:

1. Requires constant internet connection.
2. High latency in video processing.
3. Privacy and data security risks.
4. Ongoing cloud storage costs.

2. Edge Computing Architecture

How it works:

Camera sends video to a nearby edge device (e.g., a home hub or local gateway).

Edge device processes motion detection, compresses data, and only sends alerts or summaries to the cloud.

Diagram:

Camera → Edge Device → (Optional) Cloud → User App

Pros:

1. Lower latency (faster alerts).
2. Reduced cloud bandwidth usage.
3. Better privacy (less raw data sent online).
4. Works partially even with limited internet.

Cons:

1. Edge device adds hardware cost.
2. Limited processing power compared to cloud.
3. Some dependence on internet for storage or remote access.

3. Local Computing Architecture

How it works:

• All video processing and storage happen within the camera or on a local DVR/NVR.
• No internet connection required for functioning. Diagram:

Camera → Local Storage / Local Monitor

Pros:

1.Very low latency.
2.Full privacy (no data leaves home network).
3.Works offline completely.
4.One-time setup cost, no subscription.

Cons:

1. Limited storage capacity.
2. No remote access (unless manually configured).
3. Hardware upgrades needed for better AI features.

Feature	Cloud	Edge	Local
Latency	High	Medium	Very Low
Internet Dependence	Full	Partial	None
Privacy	Low	Medium	High
Cost (Long-term)	High (subscription)	Medium	Low
AI Processing Power	High	Medium	Low–Medium
Scalability	Excellent	Good	Limited

🏁 Conclusion

For a smart security camera,

• Cloud computing is best for heavy AI analytics and global access.
• Edge computing offers a great balance of performance, privacy, and cost.
• Local computing is ideal for privacy-focused or offline use cases.