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

TL;DR

This work tackles the challenge of minimizing delay in multi-UAV maritime search and rescue by jointly optimizing offloading decisions, R-UAV placement, and S-UAV–target associations within an MEC-enabled MSAR system. The authors formulate a nonconvex MINLP objective to minimize the maximum latency among S-UAVs and propose an iterative algorithm that decomposes the problem into offloading, positioning, and association subproblems, using convex relaxation, SCA, and Branch-and-Bound. Key contributions include a three-subproblem decomposition, an adaptive S-UAV position adjustment to maintain monitoring integrity, and a convergence-guaranteed algorithm with complexity insights, validated by simulations showing lower latency and reduced delay variation compared with baseline schemes. The results demonstrate practical impact for real-time MSAR operations, enabling faster, fairer video surveillance across multiple UAVs with feasible energy budgets; future work may extend to energy-efficient optimization and multi-hop transmission schemes.

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.

Figures (13)

• Figure 1: The MSAR procedure.
• Figure 2: The multi-UAV assisted MSAR system.
• Figure 3: The illustration of the position of S-UAV adjustment process.
• Figure 4: The distribution of S-UAVs, R-UAV and rescue targets. (a) locations of S-UAVs, R-UAV and rescue targets; (b) top view.
• Figure 5: The minimum maximum latency versus the number of video chunks.