Task Offloading Optimization for Multi-objective Based on Cloud-Edge-End Collaboration in Maritime Networks

In recent years, global maritime activities have surged, yet maritime networks face significant limitations in capacity. To address this challenge, integrating mobile edge computing into maritime networks has emerged as a solution, enabling the offloading of computation-intensive tasks to the edge to enhance system performance. However, existing research often narrowly focuses on either system cost or Quality of Service (QoS), failing to optimize both concurrently. This study aims to bridge this research gap by proposing a novel approach that optimizes both system cost and QoS simultaneously through collaborative computing among terminals, edge servers, and a cloud server in a maritime network environment. We leverage the Improved Coati Optimization Algorithm (ICOA) to optimize transmission power for vessel users, and subsequently, we apply Binary Particle Swarm Optimization (BPSO) to make task offloading decisions that consider both system cost and QoS. Experimental results demonstrate that our proposed approach significantly outperforms existing benchmark algorithms in balancing system cost and QoS in cloud-edge-end collaborative scenarios.