SHBIO-FANET: Secure and Hybrid Bio Inspired Optimization for FANETs

Abstract: This paper presents a novel clustering scheme for optimizing energy consumption in Flying Ad Hoc Networks (FANETs) using a Secure and Hybrid Bio-Inspired Optimization (SHBIO) method, integrating Binary Whale Optimization Algorithm (BWOA) and Ant Colony Optimization (ACO). The proposed approach involves calculating the optimal number of cluster heads (CHs) based on coverage demand and bandwidth balancing. By considering UAV energy, inter-cluster and intra-cluster distances, and load balancing, the method selects optimal CHs. Clusters are maintained efficiently, and the nearest high-energy node is chosen for communication. Performance metrics such as end-to-end delay, network lifetime, throughput, packet delivery ratio, and packet loss were evaluated. Results demonstrate that the SHBIO-BWOA_ACO method significantly outperforms previous methodologies, achieving lower energy consumption, higher throughput, and improved network performance.

Keywords: Flying Ad Hoc Networks (FANETs), Binary Whale Optimization Algorithm (BWOA), Ant Colony Optimization (ACO), energy consumption, clustering, network performance, packet delivery ratio, throughput, end-to-end delay.