Secure and privacy-aware control of swarm of drones

   Faculty of Natural Sciences

  ,  Wednesday, May 29, 2024  Funded PhD Project (UK Students Only)

About the Project

Drone swarm technology allows groups of drones work together without direct human intervention. They are widely employed for diverse use cases such as monitoring wildfires and civil infrastructures, traffic, logistics monitoring, and last-mile delivery. However, this technology might also bring up concerns about safety, cybersecurity, and privacy.

To protect these systems against hackers, we need robust and resilient software and hardware solutions.

The project aims to design and develop novel solutions for a swarm of drones that facilitates secure and privacy-aware data capture, storage, and communication. The swarm of drones are vulnerable to security and privacy attacks including, location spoofing, GPS and GNSS jamming, data corruption, and identification of personal information. If these challenges left unaddressed, they could have commercial, personal, and legal, implications. To address these challenges, new security and multi-level privacy features need to be devised. The security features involve data protection techniques to be applied at the source as per data security and privacy law requirements. Furthermore, privacy protection techniques will also be developed for drone data including landscape identification and the level of privacy required. It is important to have multi-level privacy protection as the drones that are used to monitor some non-residential areas or structural objects.

The supervision of the research will be conducted by two proficient professionals, namely Dr. Aisha Junejo, specializing in cybersecurity, and Dr. Nadia Kanwal, specializing in Computer Vision and Machine Learning, both affiliated with Keele University. The combined expertise of these supervisors will assist the student in incorporating security and privacy measures not only for communication data but also for vision-related data acquired through drone technology.

Eligibiity Criteria: Bachelors or Masters in relevant discipline (Computer Science, Cyber Security). The funding is only available for home students. 

For full details and to submit an application please go to

Please quote FNS_AJNK_May 24 on your application.

Keele University is renowned for its exciting approach to higher education and research, beautiful campus, strong community spirit and excellent student life. The University has the UK’s largest campus with 617 acres of landscaped parkland, fields, woodlands and lakes. Keele University runs its own day nursery for infants from 3 months to 5 years and is committed to equality and diversity.

Computer Science (8)

Funding Notes

Fully funded 100% home tuition fees for 3 years. (£4712 per annum in 23/24). 3 years' stipend support at UKRI rates (£18622 per annum in 23/24). Funding: Keele University
Students are also provided with access to Faculty research training funds for research related expenses including - but not limited to - conference attendance, external training courses and UK fieldwork.


Useful References:
• A. K. Junejo, N. Komninos and J. A. McCann, "A Secure Integrated Framework for Fog-Assisted Internet-of-Things Systems," in IEEE Internet of Things Journal, vol. 8, no. 8, pp. 6840-6852, 15 April15, 2021, doi: 10.1109/JIOT.2020.3035474
• I Aribilola, MN Asghar, N Kanwal, M Fleury, B Lee. “SecureCam: Selective Detection and Encryption Enabled Application for Dynamic Camera Surveillance Videos.” IEEE transactions on consumer electronics 69.2 (2023): 156–169. Web
• Cleary, A., Yoo, K., Samuel, P., George, S., Sun, F. and Israel, S.A. (2020). Machine Learning on Small UAVs. 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). doi:10.1109/aipr50011.2020.9425090
• A. Coletta et al., "A2-UAV: Application-Aware Content and Network Optimization of Edge-Assisted UAV Systems," IEEE INFOCOM 2023 - IEEE Conference on Computer Communications, New York City, NY, USA, 2023, pp. 1-10, doi: 10.1109/INFOCOM53939.2023.10229096

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