Dependency-Aware Task Offloading and Resource Allocation for Maritime Ultrareliable Applications

Compared to terrestrial networks, maritime networks lack centralized infrastructure, and tasks within maritime ultrareliable applications are interdependent, complicating task offloading and resource allocation. Additionally, in the public area of maritime networks, task offloading processes are vulnerable to malicious attacks. To ensure real-time and reliable performance while mitigating the risks of miscalculations, this paper proposes a multi-agent deep reinforcement learning algorithm for task offloading. The proposed algorithm features a real-time alerting mechanism and reliability constraints, focusing on minimizing system costs, including latency and energy consumption. Maritime applications are modeled as directed acyclic graphs, and the offloading problem is framed as a decentralized Markov decision process. We have developed an enhanced actor-critic architecture by transforming conventional fully connected networks into tailored parallel sequence-to-sequence networks, which improves task feature extraction. This approach allows the system to adapt more effectively to the dynamic challenges of marine environments. Experimental results show that the proposed algorithm outperforms existing solutions, demonstrating significant improvements in task offloading and resource allocation for interdependent tasks.