TY - GEN
T1 - Towards the Future of Edge Computing in the Sky
T2 - 17th Annual International Conference on Distributed Computing in Sensor Systems, DCOS 2021
AU - Pacheco, Lucas
AU - Oliveira, Helder
AU - Rosario, Denis
AU - Zhao, Zhongliang
AU - Cerqueira, Eduardo
AU - Braun, Torsten
AU - Mendes, Paulo
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - In modern 5G and Beyond (B5G) networks, the number of users and devices consuming highly-demanding services in terms of latency and throughput. Due to their high dynamicity and fine-grainess, such services must be supported by a joint management and integration effort between technologies such as Mobile Edge Computing (MEC), Unmanned Aerial Vehicles (UAVs), and novel radio and energy transfer techniques. The notion of Flying Edge Computing (FEC) arises as a prominent solution to provide a deeper level of integration and capabilities to UAV networks in collaboration with traditional edge computing and B5G infrastructure. FEC constitutes a highly elastic computation layer in modern networks, which can quickly adapt to surges in demand. This paper dives into FEC's main opportunities and motivations in modern scenarios and presents some of the important design aspects of FEC. Experimental results show that the coupling of traditional MEC with FEC can deliver significantly better Quality of Service (QoS), improve service availability, and user satisfaction. Furthermore, FEC can adapt to user mobility patterns more efficiently, delivering contents and services.
AB - In modern 5G and Beyond (B5G) networks, the number of users and devices consuming highly-demanding services in terms of latency and throughput. Due to their high dynamicity and fine-grainess, such services must be supported by a joint management and integration effort between technologies such as Mobile Edge Computing (MEC), Unmanned Aerial Vehicles (UAVs), and novel radio and energy transfer techniques. The notion of Flying Edge Computing (FEC) arises as a prominent solution to provide a deeper level of integration and capabilities to UAV networks in collaboration with traditional edge computing and B5G infrastructure. FEC constitutes a highly elastic computation layer in modern networks, which can quickly adapt to surges in demand. This paper dives into FEC's main opportunities and motivations in modern scenarios and presents some of the important design aspects of FEC. Experimental results show that the coupling of traditional MEC with FEC can deliver significantly better Quality of Service (QoS), improve service availability, and user satisfaction. Furthermore, FEC can adapt to user mobility patterns more efficiently, delivering contents and services.
KW - B5G
KW - Edge Computing
KW - FEC
KW - UAV
UR - https://www.scopus.com/pages/publications/85123318922
U2 - 10.1109/DCOSS52077.2021.00045
DO - 10.1109/DCOSS52077.2021.00045
M3 - 会议稿件
AN - SCOPUS:85123318922
T3 - Proceedings - 17th Annual International Conference on Distributed Computing in Sensor Systems, DCOS 2021
SP - 220
EP - 227
BT - Proceedings - 17th Annual International Conference on Distributed Computing in Sensor Systems, DCOS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 July 2021 through 16 July 2021
ER -