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.
翻译:近年来,无人驾驶飞机网络越来越受欢迎,并被用于许多应用领域,如区域覆盖、运载系统、军事行动等。 区域覆盖是一个广泛的应用领域,其中一组连接的无人驾驶飞机为达到具体目标而合力访问某一区域的全部或部分地区,并正在对此进行广泛研究。因此,设计了不同的机动模式,以确定参与的无人驾驶飞机的移动规则。然而,大多数无人驾驶飞机并不认为网络连接至关重要,而且许多模式缺乏对无人驾驶飞机网络十分重要的优先事项和优化战略。因此,在本研究中,对三种已知的具有连通性的流动模式进行了比较分析。还进一步采用了两种不连通性机动模型,以取得胎盘效应(如果有的话)。对移动模型、覆盖率、连通水平和信息流量的详细实验进行了评估。研究表明,分布的Pheromone Repel(DPR)模型提供了体面的覆盖性表现,而基于连通性模型和连接覆盖模型则提供了更好的连接和通信质量。