Emergent distributed and autonomous systems

Our research focuses on designing robust algorithms and protocols that enable autonomous agents to securely share information, coordinate movements, and navigate complex environments.

We are currently investigating the security and resilience of swarm of drones. We aim to improve security, privacy, dependability, and efficiency of swarms of drones by employing cutting-edge technologies namely, as machine learning, sensor fusion, and distributed computing.

emergent-one

We are working on developing novel algorithms for secure communication, privacy preserving data storage and processing ensuring secure, safe and resilient aerial flight operations. We will develop robust frameworks for autonomous aerial systems in critical applications such as surveillance, disaster response, and smart cities.

emergent-drones

Crazyflie Drone (left), Tello talent Drone (right)

Join us

Join our group if you are interested in exploring the exciting possibilities of drone swarm technology and its potential applications in areas such as disaster response, environmental monitoring, and smart cities. You may can also find information on our Computer Science web pages about how to join us for academic visits.

For PhD or other research opportunities, please direct enquiries to Nadia Kanwal (n.kanwal@keele.ac.uk) or Aisha Junejo (a.junejo@keele.ac.uk).

Publications

Junejo, A. K., Breza, M., & McCann, J. A. (2023). Threat modeling for communication security of IoT-enabled digital logistics. Sensors, 23(23), Article 9500. https://doi.org/10.3390/s23239500
Tahir, M., Qiao, Y., Kanwal, N., Lee, B., & Asghar, M. N. (2023). VidSearch: Privacy-by-design video search and retrieval system for large-scale CCTV data. 2023 International Conference on Machine Learning and Applications (ICMLA), 2182–2187. https://ieeexplore.ieee.org/document/10460003
Junejo, A. K., Jokhio, I. A., & Jan, T. (2022). A multi-dimensional and multi-factor trust computation framework for cloud services. Electronics, 11(13), 1932. https://doi.org/10.3390/electronics11131932
Tahir, M., Qiao, Y., Kanwal, N., Lee, B., & Asghar, M. N. (2023). Privacy-preserved video summarization of road traffic events for IoT smart cities. Cryptography, 7(1), 7. https://doi.org/10.3390/cryptography7010007
Junejo, A. K., Benkhelifa, F., Wong, B., & McCann, J. A. (2022). LoRa-LiSK: A lightweight shared secret key generation scheme for LoRa networks. IEEE Internet of Things Journal, 9(6), 4110–4124. https://doi.org/10.1109/JIOT.2021.3103009
Junejo, A. K., Komninos, N., Sathiyanarayanan, M., & Chowdhry, B. S. (2021). Trustee: A trust management system for fog-enabled cyber-physical systems. IEEE Transactions on Emerging Topics in Computing, 9(4), 2030–2041. https://ieeexplore.ieee.org/document/8922644
Junejo, A. K., Komninos, N., & McCann, J. A. (2021). A secure integrated framework for fog-assisted Internet-of-Things systems. IEEE Internet of Things Journal, 8(8), 6840–6852. https://ieeexplore.ieee.org/document/9247121
Junejo, A. K., & Komninos, N. (2020). A lightweight attribute-based security scheme for fog-enabled cyber-physical systems. Wireless Communications and Mobile Computing, 2020, 2145829. https://doi.org/10.1155/2020/2145829
Kanwal, N., Asghar, M. N., Ansari, M. S., Fleury, M., Lee, B., Herbst, M., & Qiao, Y. (2020). Preserving chain-of-evidence in surveillance videos for authentication and trust-enabled sharing. IEEE Access, 8, 153413–153424. https://doi.org/10.1109/ACCESS.2020.3016211