Secure and Efficient Context-Aware Localization of Drones in Urban Scenarios

Abstract

Drone swarming involves multiple unmanned aerial vehicles (UAVs), which are able to manoeuver autonomously, providing a vast range of surveillance applications such as traffic evaluations, driver monitoring, disaster management, temporary network support, and pedestrian tracking. All these applications are an integral part of urban surveillance. In other words, cooperation between multiple drones can facilitate urban surveillance while being able to provide high-quality 3D visualization of the surroundings. This 3D representation is driven by the initiation of accurate and non-overlapping waypoints. Such efficient localization depends on the type of context and its security validation by the receiving entities. For example, a highly burdened context with negligible security is of limited use. This article presents a novel solution that is capable of securing the context information for sharing 3D waypoints between UAVs. The proposed approach achieves optimal localization through hierarchical context-aware aspect-oriented Petri nets while being powered by a novel drone context-exchange protocol for security validations.