Design and Performance Evaluation of a Pseudo-Flocking Unmanned Aerial Vehicles Mobility Model for Surveillance Applications

Abstract

This paper introduces pseudo-flocking, a bio-inspired hybridization scheme that profits from both characteristics of random path planning and birds flocking algorithms to produce multiple-unmanned-aerial-vehicle (multiple-UAV) mobility model (MM) for a superior tradeoff between the area coverage and the swarm’s connectivity. The designed path-based hybridization algorithm using the UAV’s path radius produces intrinsically hybrid movement patterns that respect surveillance application requirements, which implies delivering unpredictable movement patterns and efficiently dealing with the present conflict between the area coverage and the network connectivity in large-scale homogenous UAV deployments. A set of adaptations is provided for motion control concerning the UAV’s belief system and behaviors applied on a state-of-the-art surveillance-oriented MM to act as a reference. Extensive simulations tests are provided in this work to reveal the variation pattern of the delivered quality of service according to the UAV’s path radius along with the swarm density. The results confirmed that our proposed MM provides the best tradeoff among three competitors’ path-based MMs, where its highest impact touches more the network connectivity than the area coverage with an improvement that passes 20% compared with its reference MM.