A Multi-Token-Based Directional Neighbor Discovery Algorithm for FANETs

Millimeter wave (mmWave) communication is crucial for drones, leading to more being equipped with directional antennas. Consequently, the technology for fast detection of directional antenna neighbors is a major challenge in mmWave Flying Ad-Hoc Networks (FANETs). This paper proposes a fully directional Neighbor Discovery (ND) method called Directional ND with Multi-Token Passing (DNDMTP) Algorithm to work out the aforementioned issue. Specifically, a theoretical derivation of DNDMTP is first presented, followed by a verification of its correctness through simulations that match the theoretical results. Unlike existing methods, DNDMTP is designed to maximize the spatial reuse capability of directional antennas by forwarding tokens across multiple antenna coverage areas, accelerating neighbor discovery in multi-hop topologies. In DNDMTP, the token holder nodes can achieve bidirectional discovery with the neighbor nodes through multiple rounds of broadcasting. Furthermore, the optimal selection of parameters is proposed for different scenarios, e.g., the number of antennas, iterations, and the number of tokens passed. Finally, for comparison purposes, we chose the Scan-Based Algorithm-Deterministic (SBA-D) and Learning Automaton Based ND (LAND) algorithms, which represent deterministic and probabilistic algorithms, respectively. The numerical results from Python show that the proposed DNDMTP can reduce the neighbor discovery time by about 50% compared to existing methods, due to multiple token-holder nodes performing neighbor discovery simultaneously. As the number of nodes and antennas increases, DNDMTP performs better in terms of neighbor discovery time.