A Distance Distribution-Based Modeling and Analysis for Autonomous Aerial Vehicle Networks
Autonomous aerial vehicles (AAVs) networks, combining AAVs with mobile communication technology, can promote the rational utilization of airspace resources and produce enormous economic value. Due to the complex effects of network deployment areas (NDAs), AAV mobility, and channel fading characteristics, the received signal strength at the AAV exhibits randomness and is susceptible to eavesdropping. However, existing research commonly ignores AAVs’ mobility and only considers the communications and movements within regularly-shaped NDAs. To solve these limitations, we propose a distance distribution-based modeling and analysis framework considering both node randomness and mobility under arbitrarily-shaped convex NDAs. More concretely, this paper focuses on a AAV network for a low-altitude data collection scenario, in which the mobile AAVs serve as an aerial base station to collect the information from the ground randomly distributed Internet of Things (IoT) devices. To involve both the randomness of IoT devices and mobility of AAVs, we propose a method combining random waypoint mobility model and kinematic measure method to derive the distributions of two types of distances for arbitrarily-shaped convex NDAs, including the distance between a random IoT device and a mobile AAV (referred to as R2M) and that between two mobile AAVs (referred to as M2M). Based on the obtained R2M and M2M distance distributions, the communication, coverage, and security performance are derived and analyzed for single-AAV, multi-AAV, and eavesdropping scenarios. The accuracy and effectiveness of the proposed framework are evaluated by extensive numerical studies.