Exploring Hedera Hashgraph for Efficient Data Transfer in MOOS-IvP Aquaticus Testbed

The Hedera hashgraph algorithm has been shown to be Asynchronous Byzantine Fault Tolerant (ABFT) for achieving consensus on adding a transaction into local copies of a hash-graph distributed database. The ABFT result is theoretically the best result that can be achieved for distributed ledger technology (DLT) regarding trusting that the data in each local copy of a distributed global database has not been tampered with during each transaction process to add data into the global distributed database. The hashgraph algorithm ensures that each transaction in each local copy of the global database can be trusted to be a true copy of the data submitted by each node in the set of peer nodes as long as no more than 1/3 of the peer nodes in the peer-to-peer network of hashgraph nodes have been compromised. The Aquaticus, capture the flag (CTF) force-on-force free-play competition between Artificial Intelligence (AI)/Machine Learning (ML) agents enables use of a variety of ML algorithms to build AI/ML agents to play and win the CTF game in a maritime environment by employing the MOOS-IvP autonomy stack. This paper explores the integration of Hedera hashgraph DLT into the MOOS-IvP Aquaticus testbed for efficient and secure data transfer in collaborative autonomy scenarios. The study focuses on developing a multi-node Hedera network to support decentralized, real-time, and tamper-proof communication among autonomous agents in adversarial maritime environments. A detailed network setup using Docker and solo-compose is outlined, including transitioning from single-node to multi-node configurations. The system's application is evaluated in the context of the Aquaticus capture-the-flag (CTF) environment, highlighting its role in synchronizing flag positions and tagging status among unmanned surface vehicles (USVs), Initial findings indicate that the Hedera network can enhance data integrity and scalability while reducing latency in distributed systems. Challenges in scaling and resource optimization are discussed, along with proposed future work to deploy physical nodes using Raspberry Pi and integrate reinforcement learning frameworks like PyQuaticus. This research provides a foundation for advancing decentralized communication in autonomous robotics, emphasizing its potential for secure and robust multi-agent collaboration.