Smarter Swarms: Empirical Assessment of Blockchain-Based RLR and DeTAV Algorithms in Swarm Network

Swarm robotics is an evolving realm, designed to achieve complex tasks collaboratively. This technology faces challenges in secure communication, decentralized decision making, and scalability aspects. While many studies suggested using of Blockchain technology for swarm robotics, the existing Blockchain solutions with Proof of Work (PoW) or Proof of Stake (PoS) based consensus, originally designed for finance and trade network systems, are inefficient for swarm robotics due to their high computation needs or stake-based monopolies. This research addresses these challenges by introducing a Blockchain based Rotational Leadership Role (RLR) consensus algorithm, a voting-based consensus protocol, with Decentralized Task Authorization and Validation (DeTAV), a token-based transaction validation mechanism, to ensure efficiency, security, and scalability features in swarm robotic and drone systems. RLR is a lightweight hybrid solution suitable to run within the limited computing resources of any small robots or aerial drones. A custom-built robotic simulator compared RLR to PoW, demonstrating RLR's superior performance. Based on the experiments conducted with 70 concurrent robots, RLR used under 100MB RAM and 12.5% CPU, while PoW exceeded 450MB RAM and 25% CPU, showing a 500% RAM and 173% CPU reduction by using RLR. RLR's DeTAV also enhanced network security through context-based task validation to restrict suspicious activities initiated by malfunction or malicious robots in the network. Scalability tests performed using 4 to 70 robots revealed RLR's ability to scale effortlessly to handle over 70 nodes. Thus, RLR with DeTAV effectively meets the efficiency, security, and scalability needs of swarm robotic or drone networks.