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.

2017. 1-12 Paper presented at The 36th IEEE/AIAA Digital Avionics Systems Conference , St. Petersburg, United States.

Research output: Contribution to conferencePaperpeer-review

Published

Standard

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.

2017. 1-12 Paper presented at The 36th IEEE/AIAA Digital Avionics Systems Conference , St. Petersburg, United States.

Research output: Contribution to conferencePaperpeer-review

Harvard

Akram, RN, 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', Paper presented at The 36th IEEE/AIAA Digital Avionics Systems Conference , St. Petersburg, United States, 17/09/17 - 21/09/17 pp. 1-12. https://doi.org/10.1109/DASC.2017.8101984

APA

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. 1-12. Paper presented at The 36th IEEE/AIAA Digital Avionics Systems Conference , St. Petersburg, United States. https://doi.org/10.1109/DASC.2017.8101984

Vancouver

Akram RN, Markantonakis K, Mayes K, Habachi O, Sauveron D, Steyven A et al. Security, privacy and safety evaluation of dynamic and static fleets of drones. 2017. Paper presented at The 36th IEEE/AIAA Digital Avionics Systems Conference , St. Petersburg, United States. https://doi.org/10.1109/DASC.2017.8101984

Author

Akram, Raja Naeem ; Markantonakis, Konstantinos ; Mayes, Keith ; Habachi, Oussama ; Sauveron, Damien ; Steyven, Andreas ; Chaumette, Serge. / Security, privacy and safety evaluation of dynamic and static fleets of drones. Paper presented at The 36th IEEE/AIAA Digital Avionics Systems Conference , St. Petersburg, United States.12 p.

BibTeX

@conference{7ebf9cac787e48ee901b423bd26b7757,
title = "Security, privacy and safety evaluation of dynamic and static fleets of drones",
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.",
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",
author = "Akram, {Raja Naeem} and Konstantinos Markantonakis and Keith Mayes and Oussama Habachi and Damien Sauveron and Andreas Steyven and Serge Chaumette",
year = "2017",
month = nov,
day = "9",
doi = "10.1109/DASC.2017.8101984",
language = "English",
pages = "1--12",
note = "The 36th IEEE/AIAA Digital Avionics Systems Conference , DASC'17 ; Conference date: 17-09-2017 Through 21-09-2017",
url = "http://2017.dasconline.org",

}

RIS

TY - CONF

T1 - Security, privacy and safety evaluation of dynamic and static fleets of drones

AU - Akram, Raja Naeem

AU - Markantonakis, Konstantinos

AU - Mayes, Keith

AU - Habachi, Oussama

AU - Sauveron, Damien

AU - Steyven, Andreas

AU - Chaumette, Serge

N1 - Conference code: 36

PY - 2017/11/9

Y1 - 2017/11/9

N2 - 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.

AB - 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.

KW - Drones

KW - Unmanned Aerial Vehicles

KW - Artificial Intelligence

KW - Swarm Intelligence

KW - Fleet of Drones

KW - Swarm of Drones

KW - Security

KW - Privacy

KW - Safety

KW - Autonomous Drones

KW - Self-aware Drones

KW - Independent Drones

U2 - 10.1109/DASC.2017.8101984

DO - 10.1109/DASC.2017.8101984

M3 - Paper

SP - 1

EP - 12

T2 - The 36th IEEE/AIAA Digital Avionics Systems Conference

Y2 - 17 September 2017 through 21 September 2017

ER -