Trust Quantification in a Collaborative Drone System with Intelligence-driven Edge Routing

Collaborative Drone systems (CDS) have the potential to benefit a variety of application areas such as agriculture, military operations, surveillance, and disaster response. At the same time, CDS can pose challenges due to their limited flight time impacted by battery capacities, and constrained edge computation capabilities on-board the drones. Furthermore, an understudied subject relates to when drones in a CDS trust each other to accomplish a task, resulting in new vulnerabilities that can be exploited via cyber attacks. In this paper, we propose a novel trust quantification methodology in a CDS with intelligence-driven edge routing, which can help detect malicious nodes in a CDS that compromise communication and disrupt the functionality of packet forwarding. Our approach for trust quantification is guided by a CDS vulnerability analysis that characterizes impact due to the presence of two malicious threat agents viz., flooder node and faker node. Detection of these threat agents in a CDS is aided by trust quantification in the form of trust scores obtained by using a Bayesian Network model that allows for decision-making on CDS nodes’ trust levels. We validate our trust quantification methodology in ns-3 based simulation experiments and show how we can categorize nodes based on different thresholds of trust scores with varying sensitivities, which helps in the detection of CDS threat agents.