Distributed Optimization in Mobile Edge Intelligence for Energy-Efficient Maritime IoT Networks

Maritime Internet of Things (IoT) is an emerging paradigm for applications like environmental monitoring and autonomous vessels but faces challenges due to limited energy and computational resources. Mobile Edge Intelligence (MEI) addresses this by leveraging edge computing and AI to offload complex tasks to nearby servers. Distributed optimization technology further helps balance computational load and energy consumption efficiently, making maritime IoT networks more sustainable. By distributing tasks among edge nodes, distributed optimization can bring high performance and energy efficiency to maritime IoT systems. In this article, we propose an architecture for maritime IoT networks and analyze the challenges faced in real-time data collection and computational analysis within this framework. Also, we provide a use case to validate the effectiveness of using the Alternating Direction Method of Multipliers (ADMM) for computation offloading and Unmanned Aerial Vehicle (UAV) trajectory planning in maritime IoT networks.