Research Areas
Exploring innovative approaches to secure and efficient unmanned aerial vehicle traffic management.
Security Protocols
Our research in security protocols focuses on developing robust communication and authentication mechanisms to ensure the integrity and confidentiality of UAV operations. We investigate novel cryptographic approaches tailored to the unique constraints of aerial vehicles, including limited computational resources and intermittent connectivity.
Current projects include the development of lightweight authentication protocols for UAV swarms, secure handover mechanisms for cross-domain UAV operations, and threat modeling for next-generation aerial communication networks.
Key Focus Areas
- • Lightweight cryptographic primitives
- • Secure communication channels
- • Authentication and authorization
- • Secure firmware updates
- • Threat modeling and vulnerability assessment
XR Integration for UTM systems
We are exploring the use of eXtended Reality (XR) to develop innovative methods that will enhance how UAVs can be observed, managed, and controlled by the appointed authorities and departments.
We have proposed an XR system that will provide Law Enforcement Officers (LEOs) information in real-time for surveillance and monitoring of the drone ecosystem in their vicinity. Furthermore, we are working on integrating it with the open-source UTM backend by XTM alliance. By utilizing XR, users can interface with UTM services, providing a seamless and intelligent experience to intuitively understand the environment around them.
Key Focus Areas
- • XR for UAV management
- • Real to virtual world sync
- • Consensus algorithms
- • Fault-tolerant systems
- • Distributed coordination protocols
Distributed Systems for UTM
Our distributed systems research focuses on creating resilient, scalable infrastructures for managing UAV traffic across wide geographical areas. We explore decentralized approaches that can maintain operational integrity even in the presence of network partitions or node failures.
Current research includes developing distributed ledger technologies for UTM, consensus algorithms for airspace allocation, and edge computing architectures that minimize latency for time-critical decisions in UAV traffic management.
Key Focus Areas
- • Blockchain for airspace management
- • Edge computing architectures
- • Consensus algorithms
- • Fault-tolerant systems
- • Distributed coordination protocols
AI and Machine Learning
Our AI research focuses on developing intelligent algorithms for coordinating multiple UAVs in shared airspace. We investigate machine learning approaches for trajectory prediction, anomaly detection, and dynamic resource allocation in UTM systems.
Current projects include reinforcement learning for collision avoidance, federated learning for privacy-preserving UAV coordination, and explainable AI for transparent decision-making in critical UTM operations.
Key Focus Areas
- • Tracking UAVs in real-time
- • Anomaly detection in UAV behavior
- • Federated learning for UAV swarms
- • Computer vision for obstacle detection
- • Explainable AI for critical systems
Interested in Collaborating?
We're always looking for research partners and collaborators who share our vision for secure and efficient UAV traffic management.