Computational Intelligence for Optimizing UAV Positioning and Task Scheduling in UAV-Assisted MEC Systems

Meng Yi; Vincent C.S. Lee; Yifan Zhang; Peisong Li; Peng Yang

doi:10.1007/978-981-96-6585-3_5

Computational Intelligence for Optimizing UAV Positioning and Task Scheduling in UAV-Assisted MEC Systems

Meng Yi, Vincent C.S. Lee, Yifan Zhang, Peisong Li, Peng Yang

Department of Data Science & AI

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

Abstract

The integration of Unmanned Aerial Vehicles (UAVs) and Mobile Edge Computing (MEC) enhances the coverage and performance of communication networks. However, achieving full communication coverage and efficient task offloading with a minimal number of UAVs remains challenging due to their limited range and energy. To address this issue, we propose a UAV-assisted two-stage task scheduling model to optimize the costs of the MEC system, focusing on both UAV positioning and task scheduling. Accordingly, we design a UAV-assisted two-stage intelligent collaborative method, which includes two algorithms: the enhanced particle swarm optimization algorithm and the deep reinforcement learning algorithm, to find the optimal solution. Simulation results show that the proposed method converges well and outperforms three classical reinforcement learning algorithms in terms of reducing latency and energy consumption.

Original language	English
Title of host publication	Neural Information Processing - 31st International Conference, ICONIP 2024 Auckland, New Zealand, December 2–6, 2024 Proceedings, Part IV
Editors	Mufti Mahmud, Maryam Doborjeh, Kevin Wong, Andrew Chi Sing Leung, Zohreh Doborjeh, M. Tanveer
Place of Publication	Singapore Singapore
Publisher	Springer
Pages	59-73
Number of pages	15
ISBN (Electronic)	9789819665853
ISBN (Print)	9789819665846
DOIs	https://doi.org/10.1007/978-981-96-6585-3_5
Publication status	Published - 2025
Event	International Conference on Neural Information Processing 2024 - Auckland, New Zealand Duration: 2 Dec 2024 → 6 Dec 2024 Conference number: 31st https://link.springer.com/book/10.1007/978-981-96-6585-3 (Proceedings) https://iconip2024.org/ (Website)

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	15289
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Neural Information Processing 2024
Abbreviated title	ICONIP 2024
Country/Territory	New Zealand
City	Auckland
Period	2/12/24 → 6/12/24
Internet address	https://link.springer.com/book/10.1007/978-981-96-6585-3 (Proceedings) https://iconip2024.org/ (Website)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Computational intelligence
Deep reinforcement learning
Mobile edge computing
Task scheduling
UAV positioning

Access to Document

10.1007/978-981-96-6585-3_5

Cite this

Yi, M., Lee, V. C. S., Zhang, Y., Li, P., & Yang, P. (2025). Computational Intelligence for Optimizing UAV Positioning and Task Scheduling in UAV-Assisted MEC Systems. In M. Mahmud, M. Doborjeh, K. Wong, A. C. S. Leung, Z. Doborjeh, & M. Tanveer (Eds.), Neural Information Processing - 31st International Conference, ICONIP 2024 Auckland, New Zealand, December 2–6, 2024 Proceedings, Part IV (pp. 59-73). (Lecture Notes in Computer Science; Vol. 15289). Springer. https://doi.org/10.1007/978-981-96-6585-3_5

Yi, Meng ; Lee, Vincent C.S. ; Zhang, Yifan et al. / Computational Intelligence for Optimizing UAV Positioning and Task Scheduling in UAV-Assisted MEC Systems. Neural Information Processing - 31st International Conference, ICONIP 2024 Auckland, New Zealand, December 2–6, 2024 Proceedings, Part IV. editor / Mufti Mahmud ; Maryam Doborjeh ; Kevin Wong ; Andrew Chi Sing Leung ; Zohreh Doborjeh ; M. Tanveer. Singapore Singapore : Springer, 2025. pp. 59-73 (Lecture Notes in Computer Science).

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abstract = "The integration of Unmanned Aerial Vehicles (UAVs) and Mobile Edge Computing (MEC) enhances the coverage and performance of communication networks. However, achieving full communication coverage and efficient task offloading with a minimal number of UAVs remains challenging due to their limited range and energy. To address this issue, we propose a UAV-assisted two-stage task scheduling model to optimize the costs of the MEC system, focusing on both UAV positioning and task scheduling. Accordingly, we design a UAV-assisted two-stage intelligent collaborative method, which includes two algorithms: the enhanced particle swarm optimization algorithm and the deep reinforcement learning algorithm, to find the optimal solution. Simulation results show that the proposed method converges well and outperforms three classical reinforcement learning algorithms in terms of reducing latency and energy consumption.",

keywords = "Computational intelligence, Deep reinforcement learning, Mobile edge computing, Task scheduling, UAV positioning",

author = "Meng Yi and Lee, \{Vincent C.S.\} and Yifan Zhang and Peisong Li and Peng Yang",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; International Conference on Neural Information Processing 2024, ICONIP 2024 ; Conference date: 02-12-2024 Through 06-12-2024",

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Yi, M, Lee, VCS, Zhang, Y, Li, P & Yang, P 2025, Computational Intelligence for Optimizing UAV Positioning and Task Scheduling in UAV-Assisted MEC Systems. in M Mahmud, M Doborjeh, K Wong, ACS Leung, Z Doborjeh & M Tanveer (eds), Neural Information Processing - 31st International Conference, ICONIP 2024 Auckland, New Zealand, December 2–6, 2024 Proceedings, Part IV. Lecture Notes in Computer Science, vol. 15289, Springer, Singapore Singapore, pp. 59-73, International Conference on Neural Information Processing 2024, Auckland, New Zealand, 2/12/24. https://doi.org/10.1007/978-981-96-6585-3_5

Computational Intelligence for Optimizing UAV Positioning and Task Scheduling in UAV-Assisted MEC Systems. / Yi, Meng; Lee, Vincent C.S.; Zhang, Yifan et al.
Neural Information Processing - 31st International Conference, ICONIP 2024 Auckland, New Zealand, December 2–6, 2024 Proceedings, Part IV. ed. / Mufti Mahmud; Maryam Doborjeh; Kevin Wong; Andrew Chi Sing Leung; Zohreh Doborjeh; M. Tanveer. Singapore Singapore: Springer, 2025. p. 59-73 (Lecture Notes in Computer Science; Vol. 15289).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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AU - Yang, Peng

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AB - The integration of Unmanned Aerial Vehicles (UAVs) and Mobile Edge Computing (MEC) enhances the coverage and performance of communication networks. However, achieving full communication coverage and efficient task offloading with a minimal number of UAVs remains challenging due to their limited range and energy. To address this issue, we propose a UAV-assisted two-stage task scheduling model to optimize the costs of the MEC system, focusing on both UAV positioning and task scheduling. Accordingly, we design a UAV-assisted two-stage intelligent collaborative method, which includes two algorithms: the enhanced particle swarm optimization algorithm and the deep reinforcement learning algorithm, to find the optimal solution. Simulation results show that the proposed method converges well and outperforms three classical reinforcement learning algorithms in terms of reducing latency and energy consumption.

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Yi M, Lee VCS, Zhang Y, Li P, Yang P. Computational Intelligence for Optimizing UAV Positioning and Task Scheduling in UAV-Assisted MEC Systems. In Mahmud M, Doborjeh M, Wong K, Leung ACS, Doborjeh Z, Tanveer M, editors, Neural Information Processing - 31st International Conference, ICONIP 2024 Auckland, New Zealand, December 2–6, 2024 Proceedings, Part IV. Singapore Singapore: Springer. 2025. p. 59-73. (Lecture Notes in Computer Science). doi: 10.1007/978-981-96-6585-3_5