TY - CONF
T1 - UAV reconnaissance using bio-inspired algorithms
T2 - 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC)
AU - Usman, Muhammad Rehan
AU - Usman, Muhammad Arslan
AU - Yaq, Muhammad Azfar
AU - Shin, Soo Young
N1 - Note: Published in: Proceedings of the 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC). Piscataway, U.S. : Institute of Electrical and Electronics Engineers, Inc. ISBN 9781538655542
Organising Body: Institute of Electrical and Electronics Engineers
PY - 2019/1/12
Y1 - 2019/1/12
N2 - Importance of bio-inspired algorithms (BIAs) is increasing exponentially because of their capability to optimization different problems in various fields like artificial intelligence, and medicine etc. Because of the autonomous nature, BIAs are now being used in unmanned aerial vehicles (UAVs) for applications areas like internet of things (IoT), autonomous area searching and reconnaissance etc. This paper presents a UAV reconnaissance strategy jointly using the particle swarm optimization (PSO) algorithm and a few attributes of the penguin search optimization algorithm (PeSOA). The strategy mainly focuses on finding the rescue targets utilizing UAVs by optimizing their movement using combined PSO and PeSOA attributes. The results are analyzed in terms of grouping and non-grouping scenarios with and without using PeSOA attributes.
AB - Importance of bio-inspired algorithms (BIAs) is increasing exponentially because of their capability to optimization different problems in various fields like artificial intelligence, and medicine etc. Because of the autonomous nature, BIAs are now being used in unmanned aerial vehicles (UAVs) for applications areas like internet of things (IoT), autonomous area searching and reconnaissance etc. This paper presents a UAV reconnaissance strategy jointly using the particle swarm optimization (PSO) algorithm and a few attributes of the penguin search optimization algorithm (PeSOA). The strategy mainly focuses on finding the rescue targets utilizing UAVs by optimizing their movement using combined PSO and PeSOA attributes. The results are analyzed in terms of grouping and non-grouping scenarios with and without using PeSOA attributes.
KW - Electrical and electronic engineering
U2 - 10.1109/CCNC.2019.8651831
DO - 10.1109/CCNC.2019.8651831
M3 - Paper
Y2 - 11 January 2019 through 14 January 2019
ER -