Advanced Lab: Bidirectional LoRa Communication for ARGOS/MARMOT Drones

I am excited to present my MSc Advanced Lab project, where I extended the ARGOS/MARMOT drone platform with a bidirectional LoRa communication channel. This project built on my previous work in mesh-based drone coordination, now introducing a low-power, long-range out-of-band communication layer.

Project Overview

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The Advanced Lab project focused on enhancing resilience and operational flexibility of the ARGOS/MARMOT system. Core components developed include:

• LoRa Driver – Custom C driver for Raspberry Pi 3B+ with Dragino LoRa HAT, enabling bidirectional packet transmission over SPI.
• Remote Control Client – CLI tool to issue commands such as shutdown and GPS queries to remote nodes.
• Automated Deployment – Ansible workflow for provisioning, software deployment, and systemd service management across multiple nodes.

This architecture ensures control and monitoring capabilities even when Wi-Fi mesh connectivity is unavailable or unstable.

Technical Details

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LoRa Driver

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The driver bridges a TUN/TAP virtual interface with the LoRa module, exposing a standard network interface. Highlights:

• Packet Headers & Fragmentation – Reliable reconstruction over half-duplex links
• Checksum & Sequencing – Ensures data integrity
• Remote Command Handling – CMD_SHUTDOWN, CMD_GPS_REQUEST, CMD_GPS_DATA
• Logging & Telemetry – Systemd journal integration

It runs as a systemd service, with automatic startup and crash recovery.

Remote Control Client

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The client communicates with the driver via a Unix datagram socket, supporting commands:

• shutdown – Stop local service
• remote_shutdown – Command remote nodes
• gps_request / send_gps – Query or transmit GPS coordinates
• status – Check node health

Designed for lightweight, scriptable control in controlled lab environments.

Deployment Automation

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Automated deployment ensures repeatable, scalable setup:

• Install dependencies (WiringPi, CMake, libcorrect)
• Configure hardware and SPI interface
• Compile and deploy driver and client binaries
• Generate node-specific configurations
• Manage systemd services for automatic startup

This reduces human error and downtime while supporting multi-node scalability.

Results

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The system demonstrated functional bidirectional LoRa communication in real-world testing. Observed limitations include:

• Packet loss due to half-duplex nature
• Latency constraints for continuous BATMAN-adv integration
• Opportunity to improve fragmentation and retransmission protocols

Despite these, the project created a robust out-of-band control channel to extend drone network resilience.

Future Directions

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Potential enhancements:

• Rust rewrite – safer memory handling and concurrency
• Advanced packet protocol – adaptive fragmentation and loss mitigation
• Enhanced logging and monitoring – real-time system health visibility

This MSc Advanced Lab project provided hands-on experience in embedded systems, networking, and distributed communications, building a foundation for scalable, fault-tolerant drone operations.