Edge-to-Cloud Image Pipeline: Raspberry Pi 3B & Oracle Cloud Infrastructure (OCI)

📌 Project Overview

This project implements an automated, end-to-end data ingestion pipeline designed for Edge Computing. Using a Raspberry Pi 3 Model B, the system captures visual data through a USB camera and securely transmits it to OCI Object Storage.

The solution focus on scalability and security, utilizing professional cloud authentication methods to manage non-structured data (images) in a production-ready environment.

OCI Object Storage & Archive Storage - Buckets Section/Objects

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🏗 Architecture

The architecture is divided into two main layers:

1. Edge Layer (Hardware & Local Processing)

• Gateway: Raspberry Pi 3 Model B.
• Capture: USB UVC Camera.
• Environment: Linux (Raspberry Pi OS) with Python-based automation.
• Key Features: Automated frame capture, local buffer management, and secure request signing.

2. Cloud Layer (Oracle Cloud Infrastructure)

• Object Storage: Secure Buckets for high-availability image storage.
• IAM (Identity and Access Management): Strict policy enforcement for device-to-cloud communication.
• Authentication: Industry-standard API Key Pair (RSA Private/Public keys) to sign requests via OCI SDK.

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🛠 Tech Stack

• Languages: Python 3.x
• Cloud Provider: Oracle Cloud Infrastructure (OCI)
• Tools/SDKs: OCI Python SDK, OpenCV (for image capture).
• Security: RSA Key Pair Authentication, IAM Policies.

Project runnig by SSH from HTTP

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🚀 Technical Workflow

1. Capture: A Python service manages the USB camera interface to trigger image captures.
2. Authentication: The system retrieves the local Private Key to generate a signed header for the OCI API.
3. Ingestion: The file is uploaded to the designated OCI Bucket using the oci.object_storage.ObjectStorageClient.
4. Logging: Every transaction is logged locally and verified against the cloud response to ensure data integrity.

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📋 Prerequisites

• Raspberry Pi 3B with Python installed.
• An active Oracle Cloud account.
• OCI CLI/SDK configured with a valid config file and Private Key (.pem).

RASPBERRY PI 3B

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🔧 Future Improvements

• Integration with OCI Vision AI for real-time object detection.
• Implementation of a local SQLite buffer for offline-first capabilities.
• Dashboard visualization for uploaded metadata.

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Developed by: [Wellington Carlos / UNIVESP] Focusing on Data Engineering, IoT, and Cloud Solutions.