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1. Development of a Laboratory Platform for UAV Cybersecurity Education

   Jiang, Yushan; Yuan, Jiawei; Sun, Lulu; Song, Houbing (January 2021, 2021 ASEE Virtual Annual Conference)

   null (Ed.)

   There is an increasing need to fly unmanned aerial vehicles (UAVs) to enable a wide variety of beneficial applications such as emergency/disaster response, observation and study of weather phenomena including severe storms. However, UAVs are subject to cybersecurity threats stemming from increasing reliance on computer and communication technologies. There is a need to foster a robust workforce with integrated UAV and cybersecurity competencies. In addition to technique challenges, current UAV cybersecurity education also faces two significant non-technical challenges: first, there are federal or state rules and regulations on UAV flights; second, the number of designated UAV test sites is limited. A three years NSF SaTC funded project in 2020 will specifically address these challenges. We propose to develop a laboratory platform for UAV cybersecurity education. To be specific, our platform integrates software simulation with hardware-in-the-loop (HIL) simulation to simulate different UAV scenarios, on the top of which cybersecurity components are developed for hands-on practicing. We use a firmware for UAV system development, Pixhawk with related open-source software packages, as the basic simulation framework. On the top of the simulation environment, a series of hands-on exercise modules will be developed to cover UAV cybersecurity issues. Motivated by different types of cybersecurity threats to UAVs, we will adopt the scenario based design and set up several categories of exercise modules including common threats in UAV and additional modules for newly identified threats with corresponding actors, goals, actions, and events. In such a manner offense and defense tasks can be further developed. The proposed platform has the potential to be adopted by universities with limited resources to UAV cybersecurity. It will help educate future workforce with integrated UAV and cybersecurity competencies, towards secure and trustworthy cyberspace around UAVs. 

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2. Unmanned Aerial Vehicles‐Based Mission Reliability in Collaborative, Heterogeneous, and Dynamic Environments

   https://doi.org/10.1002/qre.70068  

   Ren, Junxing; Xing, Liudong; Lin, Yi‐Kuei (September 2025, Quality and Reliability Engineering International)

   ABSTRACT Unmanned aerial vehicles (UAVs) are revolutionizing a wide range of military and civilian applications. Since mission failures caused by malfunctions of UAVs can incur significant economic losses, modeling and ensuring the reliability of UAV‐based mission systems is a crucial area of research with challenges posed by multiple dependent phases of operations and collaborations among heterogeneous UAVs. The existing reliability models are mostly applicable to single‐UAV or homogeneous multi‐UAV systems. This paper advances the state of the art by proposing a new analytical modeling method to assess the reliability of a multi‐phased mission performed by heterogeneous collaborative UAVs. The proposed method systematically integrates an integral‐based Markov approach with a binary decision diagram‐based combinatorial method, addressing inter‐ and intraphase collaborations as well as phase‐dependent configurations of heterogeneous UAVs for accomplishing different tasks. As demonstrated by a detailed analysis of a two‐phase rescue mission performed by six UAVs, the proposed method has no limitations on UAV's time‐to‐failure and time‐to‐detection distributions. Another contribution is to formulate and solve UAV allocation problems, achieving a balance between mission success probability and total cost. Given the uncertainties inherent in the mission scenario, the random phase duration problem is also examined. 

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3. Counter UAV Swarms: Challenges, Considerations, and Future Directions in UAV Warfare

   https://doi.org/10.1109/MWC.003.2400047  

   Sherman, Michelle; Shao, Sihua; Sun, Xiang; Zheng, Jun (January 2024, IEEE Wireless Communications)

   Modern advances in unmanned aerial vehicle (UAV) technology have widened the scope of commercial and military applications. However, the increased dependency on wireless communications exposes UAVs to potential attacks and introduces new threats, especially from UAVs designed with the malicious intent of targeting vital infrastructures. Significant efforts have been made from researchers and other United States (U.S.) Department of Defense (DoD) agencies for developing countermeasures for detection, interception, or destruction of the malicious UAVs. One promising countermeasure is the use of a counter UAV (CUAV) swarm to detect, track, and neutralize the malicious UAV. This paper aims to recognize the state-of-the-art swarm intelligence algorithms for achieving cooperative capture of a mobile target UAV. The major design and implementation challenges for swarm control, algorithm architecture, and safety protocols are considered. A prime challenge for UAV swarms is a robust communication infrastructure to enable accurate data transfer between UAVs for efficient path planning. A multi-agent deep reinforcement learning approach is applied to train a group of CUAVs to intercept a faster malicious UAV, while avoiding collisions among other CUAVs and non-cooperating obstacles (i.e. other aerial objects maneuvering in the area). The impact of the latency incurred through UAV-to-UAV communications is showcased and discussed with preliminary numerical results. 

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4. uavEE: A Modular, Power-Aware Emulation Environment for Rapid Prototyping and Testing of UAV

   M. Theile, O. Dantsker (January 2018, Proceedings of IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA))

   State of the art design and testing of avionics for unmanned aircraft is an iterative process that involves many test flights, interleaved with multiple revisions of the flight management software and hardware. To significantly reduce flight test time and software development costs, we have developed a real-time UAV Emulation Environment (uavEE) using ROS that interfaces with high fidelity simulators to simulate the flight behavior of the aircraft. Our uavEE emulates the avionics hardware by interfacing directly with the embedded hardware used in real flight. The modularity of uavEE allows the integration of countless test scenarios and applications. Furthermore, we present an accurate data driven approach for modeling of propulsion power of fixed-wing UAVs, which is integrated into uavEE. Finally, uavEE and the proposed UAV Power Model have been experimentally validated using a fixed-wing UAV testbed. 

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5. JCopter: Reliable UAV Software Through Managed Languages

   https://doi.org/10.1109/IROS51168.2021.9636617  

   Czerniejewski, Adam; Burns, John Henry; Ghanei, Farshad; Dantu, Karthik; Liu, Yu David; Ziarek, Lukasz (September 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   UAVs are deployed in various applications including disaster search-and-rescue, precision agriculture, law enforcement and first response. As UAV software systems grow more complex, the drawbacks of developing them in low-level languages become more pronounced. For example, the lack of memory safety in C implies poor isolation between the UAV autopilot and other concurrent tasks. As a result, the most crucial aspect of UAV reliability-timely control of the flight-could be adversely impacted by other tasks such as perception or planning. We introduce JCopter, an autopilot framework for UAVs developed in a managed language, i.e., a high-level language with built-in safe memory and timing management. Through detailed simulation as well as flight testing, we demonstrate how JCopter retains the timeliness of C-based autopilots while also providing the reliability of managed languages. 

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