Agrawal Juhi, Arafat Muhammad Yeasir
School of Computer Science, University of Petroleum & Energy Studies, Prem Nagar, Dehradun 248007, India.
IT Research Institute, Chosun University, Gwangju 61452, Republic of Korea.
Sensors (Basel). 2024 Dec 26;25(1):72. doi: 10.3390/s25010072.
The high mobility and dynamic nature of unmanned aerial vehicles (UAVs) pose significant challenges to clustering and routing in flying ad hoc networks (FANETs). Traditional methods often fail to achieve stable networks with efficient resource utilization and low latency. To address these issues, we propose a hybrid bio-inspired algorithm, HMAO, combining the mountain gazelle optimizer (MGO) and the aquila optimizer (AO). HMAO improves cluster stability and enhances data delivery reliability in FANETs. The algorithm uses MGO for efficient cluster head (CH) selection, considering UAV energy levels, mobility patterns, intra-cluster distance, and one-hop neighbor density, thereby reducing re-clustering frequency and ensuring coordinated operations. For cluster maintenance, a congestion-based approach redistributes UAVs in overloaded or imbalanced clusters. The AO-based routing algorithm ensures reliable data transmission from CHs to the base station by leveraging predictive mobility data, load balancing, fault tolerance, and global insights from ferry nodes. According to the simulations conducted on the network simulator (NS-3.35), the HMAO technique exhibits improved cluster stability, packet delivery ratio, low delay, overhead, and reduced energy consumption compared to the existing methods.
无人机（UAV）的高机动性和动态特性给飞行自组织网络（FANET）中的聚类和路由带来了重大挑战。传统方法往往无法实现资源利用高效且延迟低的稳定网络。为了解决这些问题，我们提出了一种混合生物启发算法HMAO，它结合了山地瞪羚优化器（MGO）和天鹰座优化器（AO）。HMAO提高了FANET中的集群稳定性，并增强了数据传输可靠性。该算法使用MGO进行高效的簇头（CH）选择，考虑无人机的能量水平、移动模式、簇内距离和一跳邻居密度，从而降低重新聚类频率并确保协调运行。对于集群维护，一种基于拥塞的方法在过载或不平衡的集群中重新分配无人机。基于AO的路由算法通过利用预测移动性数据、负载平衡、容错能力以及来自摆渡节点的全局洞察，确保从簇头到基站的可靠数据传输。根据在网络模拟器（NS - 3.35）上进行的模拟，与现有方法相比，HMAO技术展现出更高的集群稳定性、数据包传输率、更低的延迟、开销以及更低的能耗。
