基于鲸鱼优化算法的飞行 ad hoc 网络智能聚类方案

IF 5.8 2区 计算机科学 Q1 TELECOMMUNICATIONS Vehicular Communications Pub Date : 2024-06-07 DOI:10.1016/j.vehcom.2024.100805
Mehdi Hosseinzadeh , Jawad Tanveer , Faisal Alanazi , Khursheed Aurangzeb , Mohammad Sadegh Yousefpoor , Efat Yousefpoor , Aso Darwesh , Sang-Woong Lee , Amir Masoud Rahmani
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引用次数: 0

摘要

由于无人驾驶飞行器(UAV)的进步,这项新技术被广泛应用于军事和民用领域。多无人机网络通常被称为飞行临时网络(FANET)。由于这些应用,FANET 必须确保通信的稳定性和高可扩展性。这些目标可以通过在 FANET 中采用聚类技术来实现。然而,这些网络的特点,如高移动性节点、有限的能量和动态拓扑,给聚类协议的两个重要过程,即聚类构建和聚类头的选择带来了巨大挑战。本文提出了一种基于鲸鱼优化算法(ICW)的飞行 ad hoc 网络智能聚类方案。首先,每个无人机根据相邻链路的寿命来指定自己的hello间隔,以保证ICW对FANET的适应性。然后,使用鲸鱼优化算法(WOA)进行集中聚类,以找到网络上的最佳聚类中心。为了确定每架无人机在一个簇中的成员资格,ICW 采用了一种新的标准,即亲近比,从而使每架无人机加入亲近比最佳的簇。此外,对每条鲸鱼的评估是基于一个合适度函数进行的,该函数由三个部分组成,即孤立簇的数量、簇间距离与簇内距离之比以及簇的大小。然后,根据得分值为每个簇选择簇头。这个分数取决于四个指标的加权和,即剩余能量、每个无人机与其邻居之间的平均链路寿命、邻居度以及每个无人机与其邻居之间的平均距离。最后,在 FANET 中引入了两个路由过程,即集群内路由和集群间路由。然后,通过 NS2 模拟器对 ICW 进行评估和实现。完成仿真过程后,将 ICW 与 MWCRSF、DCM 和 GWO 进行比较,并将评估结果分为两种情况,即聚类过程中的网络评估和路由过程中的网络评估。因此,在第一种情况下,ICW 的聚类时间短,聚类寿命长。在第二种情况下,与其他方法相比,ICW 优化了能量消耗、网络寿命、数据包交付率、路由开销和延迟。不过,ICW 的吞吐量比 MWCRSF 低约 3.9%。
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An intelligent clustering scheme based on whale optimization algorithm in flying ad hoc networks

Due to the progress of unmanned aerial vehicles (UAVs), this new technology is widely applied in military and civilian areas. Multi-UAV networks are often known as flying ad hoc networks (FANETs). Due to these applications, FANET must ensure communication stability and have high scalability. These goals are achieved by presenting clustering techniques in FANETs. However, the characteristics of these networks, like high-mobility nodes, limited energy, and dynamic topology, have created great challenges in two important processes of clustering protocols, namely cluster construction and the selection of cluster heads. In this paper, an intelligent clustering scheme based on the whale optimization algorithm called ICW is suggested in flying ad hoc networks. Firstly, each UAV specifies its hello interval based on the lifespan of adjacent links to guarantee the adaptability of ICW to FANET. Then, a centralized clustering process is done using a whale optimization algorithm (WOA) to find the best cluster centers on the network. To determine the membership of each UAV in a cluster, ICW employs a new criterion, i.e. closeness ratio, so that each UAV joins a cluster with the best closeness ratio. In addition, the evaluation of each whale is carried out based on a fitness function, consisting of three components, namely the number of isolated clusters, the ratio of inter-cluster distance to intra-cluster distance, and cluster size. Then, a cluster head is selected for each cluster based on a score value. This score is dependent on the weighted sum of four metrics, namely remaining energy, the average link lifespan between each UAV and its neighbors, neighbor degree, and the average distance between each UAV and its neighbors. In the last step, two routing processes, namely intra-cluster routing and inter-cluster routing, are introduced in FANET. Then, the evaluation and implementation of ICW is performed through the NS2 simulator. After completing the simulation process, ICW is compared to MWCRSF, DCM, and GWO, and the evaluation results are presented in two scenarios, namely network evaluation in the clustering process and network evaluation in the routing process. Accordingly, in the first scenario, ICW has low clustering time and a high cluster lifetime. In the second scenario, ICW optimizes energy consumption, network longevity, packet delivery rate, routing overhead, and delay compared to other approaches. However, throughput in ICW is about 3.9% lower than that in MWCRSF.

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来源期刊
Vehicular Communications
Vehicular Communications Engineering-Electrical and Electronic Engineering
CiteScore
12.70
自引率
10.40%
发文量
88
审稿时长
62 days
期刊介绍: Vehicular communications is a growing area of communications between vehicles and including roadside communication infrastructure. Advances in wireless communications are making possible sharing of information through real time communications between vehicles and infrastructure. This has led to applications to increase safety of vehicles and communication between passengers and the Internet. Standardization efforts on vehicular communication are also underway to make vehicular transportation safer, greener and easier. The aim of the journal is to publish high quality peer–reviewed papers in the area of vehicular communications. The scope encompasses all types of communications involving vehicles, including vehicle–to–vehicle and vehicle–to–infrastructure. The scope includes (but not limited to) the following topics related to vehicular communications: Vehicle to vehicle and vehicle to infrastructure communications Channel modelling, modulating and coding Congestion Control and scalability issues Protocol design, testing and verification Routing in vehicular networks Security issues and countermeasures Deployment and field testing Reducing energy consumption and enhancing safety of vehicles Wireless in–car networks Data collection and dissemination methods Mobility and handover issues Safety and driver assistance applications UAV Underwater communications Autonomous cooperative driving Social networks Internet of vehicles Standardization of protocols.
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