论文标题
关于无人机网络中区域覆盖的连通性意识分布式移动模型
On Connectivity-Aware Distributed Mobility Models for Area Coverage in Drone Networks
论文作者
论文摘要
近年来,无人机网络正在越来越流行,并且在许多应用程序中使用它们,例如区域覆盖,交付系统,军事运营等。区域覆盖范围是广泛的应用程序,其中一组连接的无人机共同访问了整个区域或部分地区以实现特定目标,并且经过广泛研究。因此,已经设计了不同的移动性模型来定义参与无人机的运动规则。但是,他们中的大多数人都不认为关键的网络连接性,加上许多模型缺乏对无人机网络很重要的优先级和优化策略。因此,在这项研究中,相对分析了三个已知的连通性迁移率模型。如果有的话,已经进一步实施了两个非连接性感知的移动性模型,以捕获安慰剂效应。根据有关移动性模型,覆盖率,连通性水平和消息流量的详细实验。研究表明,分布式信息素驱动器(DPR)模型提供了不错的覆盖范围性能,而基于连接的模型和连接的覆盖模型则提供了更好的连接性和通信质量。
Drone networks are becoming increasingly popular in recent years and they are being used in many applications such as area coverage, delivery systems, military operations, etc. Area coverage is a broad family of applications where a group of connected drones collaboratively visit the whole or parts of an area to fulfill a specific objective and is widely being researched. Accordingly, different mobility models have been designed to define the rules of movements of the participating drones. However, most of them do not consider the network connectivity which is crucial, plus many models lack the priorities and optimization strategies that are important for drone networks. Therefore within this study, three known connectivity-aware mobility models have been analyzed comparatively. Two non-connectivity-aware mobility models have further been implemented to catch the placebo effect if any. Per the detailed experiments on the mobility models, coverage rates, connectivity levels, and message traffic have been evaluated. The study shows that the Distributed Pheromone Repel (DPR) model provides a decent coverage performance, while the Connectivity-based model and the Connected Coverage model provide better connectivity and communication quality.