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

Area of Science:

• Computer Science
• Electrical Engineering
• Robotics

Background:

• Flying ad hoc networks (FANETs) composed of multiple unmanned aerial vehicles (UAVs) are increasingly utilized in civil and military applications.
• Maintaining stable communication in FANETs is challenging due to high mobility, dynamic topology, and limited energy resources.
• Accurate UAV localization is crucial, especially for indoor FANET operations.

Purpose of the Study:

• To propose a cooperative localization and automatic clustering algorithm for FANETs based on firefly swarm intelligence.
• To enhance the communication performance of indoor FANETs through improved localization accuracy and network stability.

Main Methods:

• A Firefly Swarm Intelligence based Cooperative Localization (FSICL) algorithm combining the Firefly Algorithm (FA) and Chan algorithm for UAV localization.
• A novel fitness function for FA, incorporating link survival probability, node degree-difference, average distance, and residual energy, representing firefly light intensity.
• An automatic clustering (FSIAC) approach using FA for cluster-head selection and cluster formation.

Main Results:

• The FSICL algorithm demonstrated faster and more accurate cooperative localization of UAVs.
• The FSIAC algorithm resulted in higher cluster stability and longer link expiration time (LET).
• Both proposed algorithms contributed to a longer node lifetime and overall improved communication performance in indoor FANETs.

Conclusions:

• The proposed firefly swarm intelligence based algorithms effectively address the challenges of localization and clustering in FANETs.
• FSICL and FSIAC significantly enhance the reliability and efficiency of communication for indoor FANET applications.
• This approach offers a promising solution for robust and stable operation of UAV networks in complex environments.