CLARA: clustered learning automata-based routing algorithm for efficient FANET communication

With the increasing deployment of FANETs, efficient routing algorithms play a crucial role in ensuring reliable and optimal communication among UAVs. This paper presents a novel Clustered Learning Automata-based Routing Algorithm (called CLARA) for FANETs. The proposed algorithm is designed to enhance the performance of routing by leveraging the capabilities of learning automata. The CLARA consists of five key phases. In the first phase, actions, states, and a Q-Table are initialized to facilitate the learning process. Subsequently, in the second phase, drones within the FANET are clustered based on their geographic location and connection characteristics. This clustering enables efficient management of UAVs and improves network performance. In the third phase, the algorithm selects the next hop for data packet transmission, considering factors such as connectivity and reliability. This selection process ensures robust and efficient data delivery within the network. The fourth phase focuses on Q-Table updating and energy management, which optimizes resource allocation and prolongs network lifetime. Finally, in the fifth phase, the cluster head is selected based on its remaining energy, ensuring effective leadership within each cluster. This dynamic selection process enables the efficient distribution of responsibilities and enhances network stability. Simulation results demonstrate the superiority of the CLARA approach compared to existing methods, including OLSR, AODV, and Q-FANET. CLARA algorithm shows its superiority in the criteria of control overhead, routing overhead, computational overhead of energy consumption, network lifetime, PDR, and end-to-end delay.