Design of Hybrid Snake Optimizer based Route Selection Approach for Unmanned Aerial Vehicles Communication

• Published in: IEEE access 2024-01, Vol.12, p.1-1
• Main Authors: Alkahtani, Hend Khalid, Mahgoub, Hany, Alotaibi, Faiz, Othman, Kamal M., Allafi, Randa, Salama, Ahmed S.
• Format: Article
• Language: English
• Subjects: Adaptation models; Algorithms; Artificial intelligence; Autonomous aerial vehicles; Clustering algorithms; Communication; Communication networks; Design optimization; Emergency response; Energy efficiency; Energy sources; Energy utilization; Fitness function; Heuristic algorithms; Machine learning; Metaheuristics; Optimization methods; Performance enhancement; Planning; Residual energy; Route selection; Routing; Snake optimizer; Statistics; Task analysis; Unmanned aerial vehicles
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3383031

The effective usage of energy becomes crucial for the successful deployment and operation of unmanned aerial vehicles (UAVs) in different applications, such as surveillance, transportation, and communication networks. The increasing demand for UAVs in different industries such as agriculture, logistics, and emergency response has led to the development of more sophisticated and advanced UAVs. However, the limited onboard energy resource of UAVs poses a major problem for their long-term operation and endurance. In addition, artificial intelligence (AI) and machine learning (ML) could allow UAVs to make more informed and intelligent decisions regarding their operations, resulting in sustainable and more energy-efficient UAV deployment. This article designs a Hybrid Snake Optimizer-based Route Selection Approach for Unmanned Aerial Vehicles Communication (HSO-RSAUAVC) technique. The goal of the HSO-RSAUAVC technique is to explore and select optimal routes for UAV communication. In the presented HSO-RSAUAVC technique, the SO algorithm is integrated with Bernoulli Chaotic Mapping and Levy flight (LF) for enhanced performance. In addition, the HSO-RSAUAVC method derives a fitness function including residual energy (RE), distance, and UAV degree. By incorporating the HSO-RSAUAVC technique, we can dynamically adapt UAV paths to overcome obstacles, decrease communication interference, and optimize energy utilization. To validate the performance of the proposed model, a series of simulations were performed. The comparative result analysis illustrates the better performance of the HSO-RSAUAVC technique in improving the performance and reliability of UAV communication.