Security, privacy and safety evaluation of dynamic and static fleets of drones

Akram, Raja Naeem; Markantonakis, Konstantinos; Mayes, Keith; Habachi, Oussama; Sauveron, Damien; Steyven, Andreas; Chaumette, Serge

Abstract

Interconnected everyday objects, either via public or private networks, are gradually becoming reality in modern life -- often referred to as the Internet of Things (IoT) or Cyber-Physical Systems (CPS). One stand-out example are those systems based on Unmanned Aerial Vehicles (UAVs). Fleets of such vehicles (drones) are prophesied to assume multiple roles from mundane to high-sensitive applications, such as prompt pizza or shopping deliveries to the home, or to deployment on battlefields for battlefield and combat missions. Drones, which we refer to as UAVs in this paper, can operate either individually (solo missions) or as part of a fleet (group missions), with and without constant connection with a base station. The base station acts as the command centre to manage the drones' activities; however, an independent, localised and effective fleet control is necessary, potentially based on swarm intelligence, for several reasons: 1) an increase in the number of drone fleets; 2) fleet size might reach tens of UAVs; 3) making time-critical decisions by such fleets in the wild; 4) potential communication congestion and latency; and 5) in some cases, working in challenging terrains that hinders or mandates limited communication with a control centre, e.g. operations spanning long period of times or military usage of fleets in enemy territory. This self-aware, mission-focused and independent fleet of drones may utilise swarm intelligence for a), air-traffic or flight control management, b) obstacle avoidance, c) self-preservation (while maintaining the mission criteria), d) autonomous collaboration with other fleets in the wild, and e) assuring the security, privacy and safety of physical (drones itself) and virtual (data, software) assets. In this paper, we investigate the challenges faced by fleet of drones and propose a potential course of action on how to overcome them.

Citation

Akram, R. N., Markantonakis, K., Mayes, K., Habachi, O., Sauveron, D., Steyven, A., & Chaumette, S. (2017). Security, privacy and safety evaluation of dynamic and static fleets of drones. In 2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC),. https://doi.org/10.1109/dasc.2017.8101984

• Conference Name: 2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC)
• Start Date: Sep 17, 2017
• End Date: Sep 21, 2017
• Acceptance Date: May 1, 2017
• Online Publication Date: Nov 9, 2017
• Publication Date: Nov 9, 2017
• Deposit Date: Oct 11, 2017
• Publicly Available Date: Oct 12, 2017
• Publisher: Institute of Electrical and Electronics Engineers
• Series Title: Conference Proceedings
• Series ISSN: 2155-7209
• Book Title: 2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC),
• Chapter Number: NA
• ISBN: 9781538603659
• DOI: https://doi.org/10.1109/dasc.2017.8101984
• Keywords: Drones, Unmanned Aerial Vehicles, Artificial Intelligence, Swarm Intelligence, Fleet of Drones, Swarm of Drones, Security, Privacy, Safety, Autonomous Drones, Self-aware Drones, Independent Drones
• Public URL: http://researchrepository.napier.ac.uk/Output/985284