TY - JOUR
T1 - Multiple-Objective Packet Routing Optimization for Aeronautical Ad-Hoc Networks
AU - Zhang, Jiankang
AU - Liu, Dong
AU - Chen, Sheng
AU - Ng, Soon Xin
AU - Maunder, Robert G.
AU - Hanzo, Lajos
N1 - Publisher Copyright:
© 1967-2012 IEEE.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - Providing Internet service above the clouds is of ever-increasing interest and in this context aeronautical ad-hoc networking (AANET) constitutes a promising solution. However, the optimization of packet routing in large ad hoc networks is quite challenging. In this article, we develop a discrete multi-objective genetic algorithm (DMOGA) for jointly optimizing the end-to-end latency, the end-to-end spectral efficiency (SE), and the path expiration time (PET) that specifies how long the routing path can be relied on without re-optimizing the path. More specifically, a distance-based adaptive coding and modulation (ACM) scheme specifically designed for aeronautical communications is exploited for quantifying each link's achievable SE. Furthermore, the queueing delay at each node is also incorporated into the multiple-objective optimization metric. Our DMOGA assisted multiple-objective routing optimization is validated by real historical flight data collected over the Australian airspace on two selected representative dates.
AB - Providing Internet service above the clouds is of ever-increasing interest and in this context aeronautical ad-hoc networking (AANET) constitutes a promising solution. However, the optimization of packet routing in large ad hoc networks is quite challenging. In this article, we develop a discrete multi-objective genetic algorithm (DMOGA) for jointly optimizing the end-to-end latency, the end-to-end spectral efficiency (SE), and the path expiration time (PET) that specifies how long the routing path can be relied on without re-optimizing the path. More specifically, a distance-based adaptive coding and modulation (ACM) scheme specifically designed for aeronautical communications is exploited for quantifying each link's achievable SE. Furthermore, the queueing delay at each node is also incorporated into the multiple-objective optimization metric. Our DMOGA assisted multiple-objective routing optimization is validated by real historical flight data collected over the Australian airspace on two selected representative dates.
KW - adaptive coding and modulation
KW - aeronautical ad-hoc network
KW - Aircraft mobility model
KW - multiple-objective optimization
KW - routing
UR - https://www.scopus.com/pages/publications/85137587894
U2 - 10.1109/TVT.2022.3202689
DO - 10.1109/TVT.2022.3202689
M3 - 文章
AN - SCOPUS:85137587894
SN - 0018-9545
VL - 72
SP - 1002
EP - 1016
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 1
ER -