Multiple-Objective Packet Routing Optimization for Aeronautical Ad-Hoc Networks

Authors: Zhang, J., Liu, D., Chen, S., Ng, S.X., Maunder, R.G. and Hanzo, L.

Journal: IEEE Transactions on Vehicular Technology

Volume: 72

Issue: 1

Pages: 1002-1016

eISSN: 1939-9359

ISSN: 0018-9545

DOI: 10.1109/TVT.2022.3202689

Abstract:

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.

https://eprints.bournemouth.ac.uk/37383/

Source: Scopus

KNNENS: A k-Nearest Neighbor Ensemble-Based Method for Incremental Learning Under Data Stream With Emerging New Classes.

Authors: Zhang, J., Wang, T., Ng, W.W.Y. and Pedrycz, W.

Journal: IEEE Trans Neural Netw Learn Syst

Volume: 34

Issue: 11

Pages: 9520-9527

eISSN: 2162-2388

DOI: 10.1109/TNNLS.2022.3149991

Abstract:

In this brief, we investigate the problem of incremental learning under data stream with emerging new classes (SENC). In the literature, existing approaches encounter the following problems: 1) yielding high false positive for the new class; i) having long prediction time; and 3) having access to true labels for all instances, which is unrealistic and unacceptable in real-life streaming tasks. Therefore, we propose the k -Nearest Neighbor ENSemble-based method (KNNENS) to handle these problems. The KNNENS is effective to detect the new class and maintains high classification performance for known classes. It is also efficient in terms of run time and does not require true labels of new class instances for model update, which is desired in real-life streaming classification tasks. Experimental results show that the KNNENS achieves the best performance on four benchmark datasets and three real-world data streams in terms of accuracy and F1-measure and has a relatively fast run time compared to four reference methods. Codes are available at https://github.com/Ntriver/KNNENS.

https://eprints.bournemouth.ac.uk/37383/

Source: PubMed

Multiple-Objective Packet Routing Optimization for Aeronautical Ad-Hoc Networks

Authors: Zhang, J., Liu, D., Chen, S., Ng, S.X., Maunder, R.G. and Hanzo, L.

Journal: IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY

Volume: 72

Issue: 1

Pages: 1002-1016

eISSN: 1939-9359

ISSN: 0018-9545

DOI: 10.1109/TVT.2022.3202689

https://eprints.bournemouth.ac.uk/37383/

Source: Web of Science (Lite)

Remaining Useful Life Prediction of Lithium-Ion Battery With Adaptive Noise Estimation and Capacity Regeneration Detection

Authors: Zhang, J., Jiang, Y., Li, X., Luo, H., Yin, S. and Kaynak, O.

Journal: IEEE-ASME TRANSACTIONS ON MECHATRONICS

Volume: 28

Issue: 2

Pages: 632-643

eISSN: 1941-014X

ISSN: 1083-4435

DOI: 10.1109/TMECH.2022.3202642

https://eprints.bournemouth.ac.uk/37383/

Source: Web of Science (Lite)

KNNENS: A k-Nearest Neighbor Ensemble-Based Method for Incremental Learning Under Data Stream With Emerging New Classes

Authors: Zhang, J., Wang, T., Ng, W.W.Y. and Pedrycz, W.

Journal: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

Volume: 34

Issue: 11

Pages: 9520-9527

eISSN: 2162-2388

ISSN: 2162-237X

DOI: 10.1109/TNNLS.2022.3149991

https://eprints.bournemouth.ac.uk/37383/

Source: Web of Science (Lite)

Multiple-Objective Packet Routing Optimization for Aeronautical ad-hoc Networks

Authors: Zhang, J., Liu, D., Chen, S., Ng, S.X., Maunder, R.G. and Hanzo, L.

Journal: IEEE Transactions on Vehicular Technology

Publisher: IEEE

ISSN: 0018-9545

https://eprints.bournemouth.ac.uk/37383/

Source: Manual

KNNENS: A k-Nearest Neighbor Ensemble-Based Method for Incremental Learning Under Data Stream With Emerging New Classes.

Authors: Zhang, J., Wang, T., Ng, W.W.Y. and Pedrycz, W.

Journal: IEEE transactions on neural networks and learning systems

Volume: 34

Issue: 11

Pages: 9520-9527

eISSN: 2162-2388

ISSN: 2162-237X

DOI: 10.1109/tnnls.2022.3149991

Abstract:

In this brief, we investigate the problem of incremental learning under data stream with emerging new classes (SENC). In the literature, existing approaches encounter the following problems: 1) yielding high false positive for the new class; i) having long prediction time; and 3) having access to true labels for all instances, which is unrealistic and unacceptable in real-life streaming tasks. Therefore, we propose the k -Nearest Neighbor ENSemble-based method (KNNENS) to handle these problems. The KNNENS is effective to detect the new class and maintains high classification performance for known classes. It is also efficient in terms of run time and does not require true labels of new class instances for model update, which is desired in real-life streaming classification tasks. Experimental results show that the KNNENS achieves the best performance on four benchmark datasets and three real-world data streams in terms of accuracy and F1-measure and has a relatively fast run time compared to four reference methods. Codes are available at https://github.com/Ntriver/KNNENS.

https://eprints.bournemouth.ac.uk/37383/

Source: Europe PubMed Central

Multiple-Objective Packet Routing Optimization for Aeronautical ad-hoc Networks

Authors: Zhang, J., Liu, D., Chen, S., Ng, S.X., Maunder, R.G. and Hanzo, L.

Journal: IEEE Transactions on Vehicular Technology

Volume: 72

Issue: 1

Pages: 1002-1016

Publisher: IEEE

ISSN: 0018-9545

Abstract:

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 paper, we develop a discrete ε multiobjective genetic algorithm (ε-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 ε-DMOGA assisted multiple-objective routing optimization is validated by real historical flight data collected over the Australian airspace on two selected representative dates.

https://eprints.bournemouth.ac.uk/37383/

Source: BURO EPrints