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

TL;DR

This paper proposes an optimization framework for multi-UAV-assisted maritime search and rescue, aiming to minimize maximum latency through joint offloading, deployment, and association strategies, enhancing rescue efficiency.

Contribution

It introduces a novel joint optimization model for UAV-assisted MSAR systems, addressing computational and energy constraints with an effective iterative solution.

Findings

Proposed algorithm reduces maximum latency effectively.

Simulation demonstrates improved rescue monitoring efficiency.

Joint optimization outperforms baseline methods.

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.