Firefly swarm intelligence based cooperative localization and automatic clustering for indoor FANETs

At present, the applications of multiple unmanned aerial vehicles (UAVs) are becoming more and more widespread, covering many civil and military fields. When performing tasks, UAVs will form a flying ad hoc network (FANET) to communicate to each other. However, subject to high mobility, dynamic topology, and limited energy of FANETs, maintaining stable communication performance is a challenging task. As a potential solution, the clustering routing algorithm divides the entire network into multiple clusters to achieve strong network performance. Meanwhile, the accurate localization of UAV is also strongly required when FANETs are applied in the indoor scenario. In this paper, we propose a firefly swarm intelligence based cooperative localization (FSICL) and automatic clustering (FSIAC) for FANETs. Firstly, we combine the firefly algorithm (FA) and Chan algorithm to better cooperative locate the UAVs. Secondly, we propose the fitness function consisting of link survival probability, node degree-difference, average distance, and residual energy, and take it as the light intensity of the firefly. Thirdly, the FA is put forward for cluster-head (CH) selection and cluster formation. Simulation results indicate that the proposed FSICL algorithm achieves the higher localization accuracy faster, and the FSIAC algorithm achieves the higher stability of clusters, longer link expiration time (LET), and longer node lifetime, all of which improve the communication performance for indoor FANETs.

Automated summary

Currently, the widespread applications of unmanned aerial vehicles (UAVs) in various fields pose a challenge in maintaining stable communication performance due to their high mobility, dynamic topology, and limited energy. To address this, this paper proposes a firefly swarm intelligence based cooperative localization (FSICL) and automatic clustering (FSIAC) algorithm, which combines the firefly algorithm (FA) and Chan algorithm for accurate cooperative localization and utilizes a fitness function for cluster-head (CH) selection and cluster formation. Simulation results demonstrate that the proposed algorithms achieve higher localization accuracy, stability of clusters, longer link expiration time (LET), and node lifetime, thereby improving the communication performance for indoor FANETs.