Testbed of QoS Ad-Hoc Network Designed for Cooperative Multi-drone Tasks

Thanks to technological advances in information and communications, Unmanned Aerial Vehicles (UAVs) (aka drones) technology has become one of the most important service delivery inventions these days. Equipped with sensors and cameras, these technologies can perform much on-demand critical application ranging from military and environmental to rescue operations. These UAVs devices, sometimes tiny, that can replace human and manned aircraft in several tasks, have been utilized to perform various types of services and applications more efciently. However, the deployment side of such emerging technology is still facing many issues and challenges. Using multi-drones or swarm of drones, together to accomplish one operation is one of these recent challenges. Such a system requires a high level of delicacy and cooperation to achieve the required autonomy and reduce human interaction as possible. Communication is one of the biggest challenges in these systems, since the devices should keep exchanging various types of messages with different Quality of Service (QoS) requirements. In this paper, a collaborative autonomous system of swarm drones based on deep learning has been proposed and a testbed of cooperative UAVs' mission to validate the performance of the dedicated QoS communication system using Paparazzi drones The proposed system is aware of the drones' requirements in term of QoS and able to meet with their dynamic demands.

[1]  Azzedine Boukerche,et al.  An Energy-efficient UAV-based Data Aggregation Protocol in Wireless Sensor Networks , 2018, DIVANet'18.

[2]  Mitch Campion,et al.  A Review and Future Directions of UAV Swarm Communication Architectures , 2018, 2018 IEEE International Conference on Electro/Information Technology (EIT).

[3]  Lihua Xie,et al.  Decentralized Multi-UAV Flight Autonomy for Moving Convoys Search and Track , 2017, IEEE Transactions on Control Systems Technology.

[4]  Thar Baker,et al.  Drone forensics: examination and analysis , 2019, Int. J. Electron. Secur. Digit. Forensics.

[5]  Fabien Garcia,et al.  Ad hoc network QoS architecture for cooperative Unmanned Aerial Vehicles (UAVs) , 2013, 2013 IFIP Wireless Days (WD).

[6]  Roger Zimmermann,et al.  Dynamic Urban Surveillance Video Stream Processing Using Fog Computing , 2016, 2016 IEEE Second International Conference on Multimedia Big Data (BigMM).

[7]  Azzedine Boukerche,et al.  Crowd Management: The Overlooked Component of Smart Transportation Systems , 2019, IEEE Communications Magazine.

[8]  Yaser Jararweh,et al.  Data and Service Management in Densely Crowded Environments: Challenges, Opportunities, and Recent Developments , 2019, IEEE Communications Magazine.

[9]  Fadi Al-Turjman,et al.  Enhanced Deployment Strategy for the 5G Drone-BS Using Artificial Intelligence , 2019, IEEE Access.

[10]  Guy Pujolle,et al.  A new approach to realize drone swarm using ad-hoc network , 2017, 2017 16th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net).

[11]  Mikolaj Dobski,et al.  A Testbed for Investigating the UAV Swarm Command and Control Problem Using DDDAS , 2013, ICCS.

[12]  Lav Gupta,et al.  Survey of Important Issues in UAV Communication Networks , 2016, IEEE Communications Surveys & Tutorials.

[13]  Azzedine Boukerche,et al.  Performance modeling and analysis of a UAV path planning and target detection in a UAV-based wireless sensor network , 2018, Comput. Networks.

[14]  Vijay Kumar,et al.  OpenUAV: A UAV Testbed for the CPS and Robotics Community , 2018, 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS).

[15]  Burak Kantarci,et al.  On the Feasibility of Deep Learning in Sensor Network Intrusion Detection , 2019, IEEE Networking Letters.

[16]  Mohsen Guizani,et al.  Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey and Future Directions , 2018, IEEE Communications Surveys & Tutorials.

[17]  Yaser Jararweh,et al.  A Mobility Management Architecture for Seamless Delivery of 5G-IoT Services , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[18]  Ouns Bouachir,et al.  A mobility model for UAV ad hoc network , 2014, 2014 International Conference on Unmanned Aircraft Systems (ICUAS).

[19]  Eric W. Frew,et al.  Airborne Communication Networks for Small Unmanned Aircraft Systems , 2008, Proceedings of the IEEE.

[20]  Riri Fitri Sari,et al.  Performance Evaluation of AODV, AODV-UU, and AODV with Malicious Attack Mode on Vehicular Ad-Hoc Network , 2017 .

[21]  Yaser Jararweh,et al.  Low-latency vehicular edge: A vehicular infrastructure model for 5G , 2020, Simul. Model. Pract. Theory.

[22]  Hung Manh La,et al.  Reinforcement Learning for Autonomous UAV Navigation Using Function Approximation , 2018, 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).