Solar Car Cloud Project

This project simulates the data collection and visualization for a solar-powered car using a cloud-based infrastructure. It includes a backend for processing CANbus-like messages, a frontend for data visualization, and a data sender that pushes random CANbus data to Google Cloud Pub/Sub.

Project Overview

• Backend: A Flask application that listens for CANbus data from Pub/Sub and provides API endpoints for the frontend.
• Frontend: A web interface served by Nginx to display real-time car data (voltage, temperature, engine, MPPT).
• Data Sender: A Python script that generates random CANbus-like messages and sends them to Pub/Sub.

Setup and Deployment

The project is containerized using Docker and deployed on Google Kubernetes Engine (GKE). The following sections explain how to build, push, and deploy the services.

Prerequisites

• Google Cloud SDK installed and configured
• Docker installed
• Kubernetes (kubectl) installed and configured to interact with your GKE cluster

Create the GKE Cluster

To create a Google Kubernetes Engine (GKE) cluster, run the following command:

gcloud container clusters create solar-car-cluster \
--zone us-central1-a \
--num-nodes 5 \
--enable-ip-alias

Once the cluster is created, configure kubectl to use the credentials for the cluster

gcloud container clusters get-credentials solar-car-cluster \
--zone us-central1-a \
--project solarcaroncloud

Build and Push Docker Images

To build the Docker images for the backend, frontend, and data sender, and push them to Google Container Registry (GCR), use the provided start.sh script.

./start.sh

The start.sh script will:

1. Build and push the Docker images for the backend, frontend, and data-sender.
2. Apply the Kubernetes deployment configurations.

Alternatively, you can manually run the following commands:

# Build and push backend
docker build -f Backend/Dockerfile -t gcr.io/solarcaroncloud/backend:latest .
docker push gcr.io/solarcaroncloud/backend:latest

# Build and push frontend
docker build -f Frontend/Dockerfile -t gcr.io/solarcaroncloud/frontend:latest .
docker push gcr.io/solarcaroncloud/frontend:latest

# Build and push data sender
docker build -f Data-Sender/Dockerfile -t gcr.io/solarcaroncloud/data-sender:latest .
docker push gcr.io/solarcaroncloud/data-sender:latest

Kubernetes Deployment

To deploy the backend, frontend, and data sender services on Kubernetes, you can also use the start.sh script or manually apply the deployment files:

# Backend deployment
kubectl apply -f backend-deployment.yaml

# Frontend deployment
kubectl apply -f frontend-deployment.yaml

# Data Sender deployment
kubectl apply -f sender-deployment.yaml

Stopping the Project

To stop the project and delete the Kubernetes deployments and cluster, use the end.sh script:

./end.sh

The end.sh script will:

1. Delete the Kubernetes deployments for the backend, frontend, and data sender.
2. Delete the GKE cluster.

Cloud Function: Writing Messages to Google Cloud Storage

This project includes a Cloud Function that listens to a Pub/Sub topic and writes the messages to a Google Cloud Storage bucket. The messages are in the format 153#90B4B8CC1F9B35BA, which consists of a 3-character hexadecimal ID followed by a # and 16 hexadecimal characters representing 4 bytes.

How the Cloud Function Works

• The function is triggered by messages from a Pub/Sub topic.
• It decodes the message from the application/octet-stream format.
• The message is then saved to a Google Cloud Storage bucket, under a specific subdirectory (messages/), with each file named based on the unique event ID of the message.

Deploying the Cloud Function

To deploy the Cloud Function, use the following gcloud command:

gcloud functions deploy write_to_gcs \
    --runtime python311 \
    --trigger-topic <YOUR-TOPIC> \
    --entry-point write_to_gcs \
    --region <YOUR-REGION> \
    --source .

where write_to_gcs is the name of the function.

Running Locally

If you want to run the project locally, follow these steps:

1. Install any necessary dependencies for the backend and data sender:

   pip install -r requirements.txt

2. Start the backend and data sender locally:

   python3 backend.py
   python3 data-sender.py

You can modify the service account, project IDs, and Pub/Sub topics in the configuration files as needed.

Notes

• Ensure that the correct service account and Google Cloud credentials are set up for your Kubernetes cluster.
• The project uses Google Cloud Pub/Sub for communication between the data sender and backend. For more details, please refer to the project documentation.