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Deep learning aided packet routing in aeronautical ad-hoc networks relying on real flight data: From single-objective to near-pareto multi-objective optimization

Deep learning aided packet routing in aeronautical ad-hoc networks relying on real flight data: From single-objective to near-pareto multi-objective optimization
Deep learning aided packet routing in aeronautical ad-hoc networks relying on real flight data: From single-objective to near-pareto multi-objective optimization
Data packet routing in aeronautical ad-hoc networks (AANETs) is challenging due to their high-dynamic topology. In this paper, we invoke deep learning (DL) to assist routing in AANETs. We set out from the single objective of minimizing the end-to-end (E2E) delay. Specifically, a deep neural network (DNN) is conceived for mapping the local geographic information observed by the forwarding node into the information required for determining the optimal next hop. The DNN is trained by exploiting the regular mobility pattern of commercial passenger airplanes from historical flight data. After training, the DNN is stored by each airplane for assisting their routing decisions during flight relying solely on local geographic information. Furthermore, we extend the DL-aided routing algorithm to a multi-objective scenario, where we aim for simultaneously minimizing the delay, maximizing the path capacity and maximizing the path lifetime. Our simulation results based on real flight data show that the proposed DL-aided routing outperforms existing position-based routing protocols in terms of its E2E delay, path capacity as well as path lifetime, and it is capable of approaching the Pareto front that is obtained using global link information.
AANET, Ad hoc networks, Delays, Measurement, Network topology, Routing, Routing protocols, Topology, deep learning, multi-objective optimization., routing
2327-4662
Liu, Dong
109283d8-37ca-4939-a26c-b00f186704de
Zhang, Jiankang
6add829f-d955-40ca-8214-27a039defc8a
Cui, Jingjing
a22f70a1-9a4b-446c-adeb-3ecafdfdf50a
Ng, Soon Xin
e19a63b0-0f12-4591-ab5f-554820d5f78c
Maunder, Robert
76099323-7d58-4732-a98f-22a662ccba6c
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Liu, Dong
109283d8-37ca-4939-a26c-b00f186704de
Zhang, Jiankang
6add829f-d955-40ca-8214-27a039defc8a
Cui, Jingjing
a22f70a1-9a4b-446c-adeb-3ecafdfdf50a
Ng, Soon Xin
e19a63b0-0f12-4591-ab5f-554820d5f78c
Maunder, Robert
76099323-7d58-4732-a98f-22a662ccba6c
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Liu, Dong, Zhang, Jiankang, Cui, Jingjing, Ng, Soon Xin, Maunder, Robert and Hanzo, Lajos (2021) Deep learning aided packet routing in aeronautical ad-hoc networks relying on real flight data: From single-objective to near-pareto multi-objective optimization. IEEE Internet of Things Journal. (doi:10.1109/JIOT.2021.3105357).

Record type: Article

Abstract

Data packet routing in aeronautical ad-hoc networks (AANETs) is challenging due to their high-dynamic topology. In this paper, we invoke deep learning (DL) to assist routing in AANETs. We set out from the single objective of minimizing the end-to-end (E2E) delay. Specifically, a deep neural network (DNN) is conceived for mapping the local geographic information observed by the forwarding node into the information required for determining the optimal next hop. The DNN is trained by exploiting the regular mobility pattern of commercial passenger airplanes from historical flight data. After training, the DNN is stored by each airplane for assisting their routing decisions during flight relying solely on local geographic information. Furthermore, we extend the DL-aided routing algorithm to a multi-objective scenario, where we aim for simultaneously minimizing the delay, maximizing the path capacity and maximizing the path lifetime. Our simulation results based on real flight data show that the proposed DL-aided routing outperforms existing position-based routing protocols in terms of its E2E delay, path capacity as well as path lifetime, and it is capable of approaching the Pareto front that is obtained using global link information.

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Accepted/In Press date: 13 August 2021
Published date: 16 August 2021
Additional Information: Publisher Copyright: IEEE Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Keywords: AANET, Ad hoc networks, Delays, Measurement, Network topology, Routing, Routing protocols, Topology, deep learning, multi-objective optimization., routing

Identifiers

Local EPrints ID: 452202
URI: http://eprints.soton.ac.uk/id/eprint/452202
ISSN: 2327-4662
PURE UUID: 5bf70b4e-a60a-4b73-b43a-9b6b29716aa8
ORCID for Jiankang Zhang: ORCID iD orcid.org/0000-0001-5316-1711
ORCID for Soon Xin Ng: ORCID iD orcid.org/0000-0002-0930-7194
ORCID for Robert Maunder: ORCID iD orcid.org/0000-0002-7944-2615
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 29 Nov 2021 17:34
Last modified: 28 Apr 2022 02:02

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Contributors

Author: Dong Liu
Author: Jiankang Zhang ORCID iD
Author: Jingjing Cui
Author: Soon Xin Ng ORCID iD
Author: Robert Maunder ORCID iD
Author: Lajos Hanzo ORCID iD

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