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Minimizing Maximum Latency of Task Offloading for Multi-UAV-Assisted Maritime Search and Rescue

  • Shuang QI
  • , Bin LIN*
  • , Yiqin DENG
  • , Xianhao CHEN
  • , Yuguang FANG
  • *Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

Unmanned Aerial Vehicles (UAVs) play a crucial role in Maritime Search and Rescue (MSAR), contributing to the improvement of rescue efficiency and reduction of casualties. Typically, UAVs equipped with cameras collect data from disaster areas and transmit it to the shore-based rescue command centers. By deploying Mobile Edge Computing (MEC) servers, UAVs can pre-process video footage to reduce data transmission volume, thus reducing transmission delays. However, the limited computational capacity and energy of UAVs pose significant challenges to the efficiency of UAV-assisted MSAR systems. To address these problems, in this paper, we investigate a multi-UAV assisted MSAR system consisting of multiple Surveillance UAVs (S-UAVs) and a Relay UAV (R-UAV). Then, we formulate a joint optimization problem to minimize the maximum total latency among all S-UAVs via jointly making the computing offloading decisions, R-UAV deployment, and the association between a S-UAV and rescue targets while ensuring that all targets are monitored by S-UAVs. Since the formulated optimization problem is typically hard to solve due to its non-convexity, we propose an effective iterative algorithm by breaking it into three sub-problems. Numerical simulation results show the effectiveness of the proposed algorithm with various performance parameters.
Original languageEnglish
Pages (from-to)13625-13638
Number of pages14
JournalIEEE Transactions on Vehicular Technology
Volume73
Issue number9
Early online date3 Apr 2024
DOIs
Publication statusPublished - Sept 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 1967-2012 IEEE.

Funding

The work of Yiqin Deng was supported in part by the National Natural Science Foundation of China under Grant 62301300. The work of Yuguang Fang was supported in part by the Hong Kong SAR Government through the Global STEM Professorship and in part by the Hong Kong Jockey Club through JC STEM Lab of Smart City.

Keywords

  • computing offloading
  • disaster area surveillance
  • edge computing
  • UAV

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