Energy-Efficient Clustering Scheme for Flying Ad-Hoc Network using a Teaching Learning Based Improving Artificial Bee Colony Optimized LEACH Protocol

In Flying Ad Hoc Networks (FANET) consider of multiple Unmanned Aerial Vehicles (UAVs) with high mobility leads to quickly changing topologies, which are performed automatically onboard the system. To address these challenges, the proposed optimized LEACH protocol aims to increase the network cycle and the number of communicated packets while simultaneously reducing energy node discharge. Additionally, the Teaching Learning Based Optimization (TLBO) algorithm is involved to prevent rapid convergence, which may otherwise lead to getting easily trapped in a local optimum. The determination of the Cluster Head (CH) is performed statically and with fixed scalability to select the CH for transmitting to the Base Station (BS). The Teaching Learning based Improving Artificial Bee Colony Optimized (IABCO) method involves highly searching for high-quality food sources and ensuring sufficient population diversity through exchanging information and cooperation among the colony bees. Thus, the proposed algorithm combines the advantage of ABC strong global search and ability and TLBO rapid convergence. The proposed LEACH-Teaching learning based IABCO method demonstrates high performance compared to existing methods such as Moth Flame Optimization (MFO) and Improved Weighted and Location-Based Clustering (IWLC). Specifically, the IABCO method yields superior results, achieving an energy consumption of 0.1 J, Packet Delivery Ratio (PDR) of 0.99, and Network Lifetime of 15, outperforming existing methods in FANET.