An Adaptive Probabilistic Broadcast Protocol for Data Transmission in Large-Scale FANETs
Jun Cai, Xiaodong Yi, Wanqiu Chen, Zeting Yu, Jian Chen, Lu Gan, Han Han · 2024
This paper proposes an efficient broadcast protocol for data transmission in large-scale FANETs named an adaptive probabilistic broadcast protocol (APBP). In APBP, each UAV node dynamically senses the connectivity state of the UAV swarm it is located in and adaptively adjusts the rebroadcast strategy to suit the current state. The connectivity states are classified into densely connected state and sparsely connected state according to the connectivity indicator. For the densely connected state, a new mathematical model is established to obtain the best rebroadcast strategy; for the sparsely connected state, with the idea of ant colony algorithm, the importance of each node is distinguished by calculating the pheromone concentration of the nodes, and nodes with different importance have different rebroadcast strategies. Different from related literature that only carried out simulation experiments, this study has built a large-scale semi-physical experimental environment with 101 nodes for simulating FANETs, and has carried out a large number of experiments to verify the effectiveness of the protocol in the physical environment, which can provide important insights for subsequent research on the actual protocol performance. By comparing with the traditional SF protocol and the recently proposed DNA-BSP protocol, the experimental results show that the average packet delivery success ratio is improved by up to 60%, and the end-to-end delay is always kept at a minimum, indicating that the proposed APBP can effectively solve the broadcast storm problem and transmit data efficiently in large-scale FANETs.