Setting Up a Network Router or Edge Computing Unit with Dual GbE Carrier Board for Raspberry Pi CM4

The Dual GbE Carrier Board with 4GB RAM and 32GB eMMC is a powerful expansion for the Raspberry Pi Compute Module 4 (CM4). This guide will walk you through setting up the board as a network router or an edge computing unit, leveraging the dual Gigabit Ethernet ports for high-speed networking and data processing.

Part 1: Setting Up a Network Router with Dual GbE Carrier Board

Step 1: Hardware Setup

1. Insert the Raspberry Pi CM4 into the Dual GbE Carrier Board.
2. Connect the power adapter to the board.
3. Attach an Ethernet cable from your ISP modem to the WAN port.
4. Connect a second Ethernet cable from the LAN port to your local network switch or computer.
5. Power on the device.

Step 2: Installing OpenWRT for Router Functionality

1. Download the latest OpenWRT image for CM4:

   wget https://downloads.openwrt.org/snapshots/targets/bcm27xx/bcm2711/openwrt.img.gz
   

2. Flash the image onto the eMMC storage:

   sudo dd if=openwrt.img.gz of=/dev/mmcblk0 bs=4M status=progress
   

3. Reboot the system and access OpenWRT via http://192.168.1.1.
4. Configure the WAN and LAN interfaces under Network > Interfaces.
5. Set up firewall rules and DHCP for network traffic management.

Step 3: Enhancing Router Performance

1. Enable NAT acceleration and packet forwarding:

   echo 1 > /proc/sys/net/ipv4/ip_forward
   

2. Install AdGuard Home for network-wide ad blocking:

   curl -sSL https://raw.githubusercontent.com/AdguardTeam/AdGuardHome/master/scripts/install.sh | sh
   

3. Configure VPN using WireGuard:

   opkg update && opkg install wireguard-tools
   

Part 2: Setting Up an Edge Computing Unit

Step 1: Installing Dependencies

1. Update the system:

   sudo apt update && sudo apt upgrade
   

2. Install Docker for containerized applications:

   curl -fsSL https://get.docker.com -o get-docker.sh
   sudo sh get-docker.sh
   

3. Enable AI and machine learning libraries:

   sudo apt install python3-pip
   pip3 install numpy pandas tensorflow
   

Step 2: Deploying Edge AI Applications

1. Clone an AI project for real-time analytics:

   git clone https://github.com/edgeai-project/real-time-analysis.git
   cd real-time-analysis
   

2. Run an AI inference model using TensorFlow:

   python3 inference.py --model=model.tflite
   

3. Stream data to a cloud dashboard:

   mosquitto_pub -h broker.mqtt.com -t "edge/analytics" -m "Processed data available"
   

Conclusion

The Dual GbE Carrier Board for Raspberry Pi CM4 enables powerful networking and AI-driven edge computing. By setting up the board as a router or an edge computing unit, you can optimize network performance and deploy real-time AI models efficiently.