ARNET
Airborne Relaying Networks for Reliable and Secure Mobile Communications
| POCI-01-0145-FEDER-029074 PTDC/EEI-COM/29074/2017 47 months (Jul 2018 to Jun 2022) | |
| Summary: | ARNET aims to design wireless communication protocols to enhance network reliability and security in Airborne Relaying NETworks (ARNET) for Long-Term Evolution (LTE) users on the ground. In particular, the UAV carries computation power, a lightweight LTE base station, and a onboard data processor. The UAVs are autonomously interconnected to each other and are building a multi-hop wireless network. Moreover, the UAVs are essentially mobile access points that provide network coverage and bring high data rates to challenging locations where cellular network layout may be underprovisioned. At a high level, the project will perform cutting-edge research across the following streams: i) Develop an understanding of the wireless characteristics of multi-hop communication links (UAV-to-UAV and UAVto-ground LTE users) with heterogeneous data, e.g., text, image, voice, and video. ii) Develop novel communication protocols for hierarchically organised cooperative UAVs to enhance airborne relaying network reliability with guaranteed network connectivity. iii) Develop practical and secure approaches for the airborne relaying network to evade targeted wireless communication attacks, e.g., jamming and spoofing. iv) Build an airborne relaying network testbed to operationally validate reliability and security with the proposed protocols. The final outcome will be a reliable and secure airborne relaying network, which enables real-time data relay for LTE users on the ground. The proposed protocols will be comprehensively evaluated on the testbed in a real-world environment. It will integrate advanced multi-hop wireless communication for relaying heterogeneous data of LTE users while ensuring service area coverage. Consequently, this project is a significant step towards realising the vision of an airborne relaying network. |
| Funding: | Global: 233KEUR, CISTER: 233KEUR |
| Sponsors: |     |
| Partners: | |
| Contact Person at CISTER: | Kai Li |
17, Jun, 2022
Best Paper Award at IWCMC 2022
28, Oct, 2020
CISTER project enhances reliability and security in UAV data networks for IoT devices
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Journal Papers
3D convolutional neural networks based automatic modulation classification in the presence of channel noise CISTER-TR-210805
Rahim Khan, Qiang Yang, Inam Ullah, Ateeq Ur Rehman, Ahsan Bin Tufail, Alam Noor, Abdul Rehman, Korhan CengizIET Communications (IETC), WILEY. 31, Aug, 2021, Volume 2021, pp 1-14.
Rahim Khan, Qiang Yang, Inam Ullah, Ateeq Ur Rehman, Ahsan Bin Tufail, Alam Noor, Abdul Rehman, Korhan CengizIET Communications (IETC), WILEY. 31, Aug, 2021, Volume 2021, pp 1-14.
PDFClassification of Digital Modulated COVID-19 Images in the Presence of Channel Noise Using 2D Convolutional Neural Networks CISTER-TR-210703
Alam NoorWireless Communications and Mobile Computing (WCMC), WILEY and Hindawi Collaboration. 12, Jul, 2021, Volume 2021, Issue 5539907, pp 1-15.
Alam NoorWireless Communications and Mobile Computing (WCMC), WILEY and Hindawi Collaboration. 12, Jul, 2021, Volume 2021, Issue 5539907, pp 1-15.
Integration and Applications of Fog Computing and Cloud Computing Based on the Internet of Things for Provision of Healthcare Services at Home CISTER-TR-210705
Muhammad Ijaz, Gang Li, Ling Lin, Omar Cheikhrouhou, Habib Hamam, Alam NoorElectronics (Electronics), Article No 9, MDPI. 2, May, 2021, Volume 10, pp 1-12.
Muhammad Ijaz, Gang Li, Ling Lin, Omar Cheikhrouhou, Habib Hamam, Alam NoorElectronics (Electronics), Article No 9, MDPI. 2, May, 2021, Volume 10, pp 1-12.
Online Velocity Control and Data Capture of Drones for the Internet-of-Things: An Onboard Deep Reinforcement Learning Approach CISTER-TR-201106
Kai Li, Wei Ni, Eduardo Tovar, Abbas JamalipourIEEE Vehicular Technology Magazine, IEEE. Mar 2021, Volume 16, Issue 1, pp 49-56.
Kai Li, Wei Ni, Eduardo Tovar, Abbas JamalipourIEEE Vehicular Technology Magazine, IEEE. Mar 2021, Volume 16, Issue 1, pp 49-56.
Continuous Maneuver Control and Data Capture Scheduling of Autonomous Drone in Wireless Sensor Networks CISTER-TR-210101
Kai Li, Wei Ni, Falko DresslerIEEE Transactions on Mobile Computing (TMC), IEEE. 5, Jan, 2021.Early Access
Kai Li, Wei Ni, Falko DresslerIEEE Transactions on Mobile Computing (TMC), IEEE. 5, Jan, 2021.Early Access
Conference or Workshop Papers/Talks
Deep Reinforcement Learning for Persistent Cruise Control in UAV-aided Data Collection CISTER-TR-211008
Harrison Kurunathan, Kai Li, Wei Ni, Eduardo Tovar, Falko DresslerThe IEEE Local Computer Networks conference (LCN). 4, Oct, 2021. Edmonton, Canada.
Harrison Kurunathan, Kai Li, Wei Ni, Eduardo Tovar, Falko DresslerThe IEEE Local Computer Networks conference (LCN). 4, Oct, 2021. Edmonton, Canada.
Federated Learning for Energy-balanced Client Selection in Mobile Edge Computing CISTER-TR-210305
Jingjing Zheng, Kai Li, Eduardo Tovar, Mohsen Guizani17th International Wireless Communications & Mobile Computing Conference (IWCMC 2021). 28, Jun to 2, Jul, 2021. Harbin, China.Jingjing Zheng, Kai Li, Eduardo Tovar, Mohsen Guizani
Jingjing Zheng, Kai Li, Eduardo Tovar, Mohsen Guizani17th International Wireless Communications & Mobile Computing Conference (IWCMC 2021). 28, Jun to 2, Jul, 2021. Harbin, China.Jingjing Zheng, Kai Li, Eduardo Tovar, Mohsen Guizani
A Hybrid Deep Learning Model for UAVs Detection in Day and Night Dual Visions CISTER-TR-211103
Alam Noor, Kai Li, Adel Ammar, Anis Koubâa, Bilel Benjdira, Eduardo TovarThe Third IEEE International Conference on Cognitive Machine Intelligence (CogMI). 2021, Intelligent CPS. Pittsburgh, U.S.A..
Alam Noor, Kai Li, Adel Ammar, Anis Koubâa, Bilel Benjdira, Eduardo TovarThe Third IEEE International Conference on Cognitive Machine Intelligence (CogMI). 2021, Intelligent CPS. Pittsburgh, U.S.A..