The growing adoption of unmanned aerial vehicles (UAVs) for tasks such as eCommerce, aerial surveillance, and environmental monitoring introduces the need for new safety mechanisms in an increasingly cluttered airspace. In our work we thus emphasize safety issues that emerge at the intersection of infrastructures responsible for controlling the airspace, and the diverse UAVs operating in their space. We build on safety assurance cases (SAC) - a state-of-the-art solution for reasoning about safety - and propose a novel approach based on interlocking SACs. The infrastructure safety case (ISAC) specifies assumptions upon UAV behavior, while each UAV demonstrates compliance to the ISAC by presenting its own (pluggable) safety case (pSAC) which connects to the ISAC through a set of interlock points. To collect information on each UAV we enforce a “trust but monitor” policy, supported by runtime monitoring and an underlying reputation model. We evaluate our approach in three ways: first by developing ISACs for two UAV infrastructures, second by running simulations to evaluate end-to-end effectiveness, and finally via an outdoor field-study with physical UAVs. The results show that interlocking SACs can be effective for identifying, specifying, and monitoring safety-related constraints upon UAVs flying in a controlled airspace.

With the advent of vehicle-to-everything (V2X) communications, many new applications will be enabled, including safety and traffic optimization. However, initial proposals of V2X communication protocols raise privacy concerns about the ability to track vehicles without the owners' consent. While solutions have been proposed to mitigate this tracking, they have focused on a single protocol. We propose a new passive vehicle tracking technique, which we refer to as Vehicle Ensemble Tracking, or VETing. This approach uses traces from secondary radios with long-term identifiers to de-anonymize pseudonyms used by V2X radios. Using a simulator with a network of listeners, we show that VETing can reconstruct high precision paths of vehicles traveling through a city.

Unmanned aerial vehicles (UAVs) are becoming increasingly pervasive in everyday life, supporting diverse use cases such as aerial photography, delivery of goods, or disaster reconnaissance and management. UAVs are cyber-physical systems (CPS): they integrate computation (embedded software and control systems) with physical components (the UAVs flying in the physical world). UAVs in particular and CPS in general require monitoring capabilities to detect and possibly mitigate erroneous and safety-critical behavior at runtime. Existing monitoring approaches mostly do not adequately address UAV CPS characteristics such as the high number of dynamically instantiated components, the tight int elements, and the massive amounts of data that need to be processed. In this paper we report results of a case study on monitoring in UAVs. We discuss CPS-specific monitoring challenges and present a prototype we implemented by extending \reminds, a framework for software monitoring so far mainly used in the domain of metallurgical plants. Additionally, we demonstrate the applicability and scalability of our approach by monitoring a real control and management system for UAVs in simulations with up to 30 drones flying in an urban area.