Joint Computation Offloading and Resource Allocation for Maritime MEC with Energy Harvesting

2025-11-02

AI Summary

To address the high-throughput and energy-sustainability requirements of dynamic vessel tracking, maritime incident forensics, and anti-pollution monitoring in maritime multi-access edge computing (MEC) networks, this paper proposes a joint computation offloading and resource allocation optimization framework for energy-harvesting maritime monitoring nodes. Leveraging Lyapunov optimization theory, we design an asymptotically optimal stochastic scheduling algorithm that decouples spatiotemporally coupled variables, enabling coordinated decisions on task offloading, subchannel assignment, edge computing resource allocation, and inter-base-station task migration—while guaranteeing task queue stability. Compared with state-of-the-art approaches, the proposed framework achieves significant improvements in long-term average throughput, energy efficiency, and system stability. It thus provides a scalable, edge-intelligence-enabling infrastructure for low-latency, high-reliability maritime intelligent sensing.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

In this paper, we establish a multi-access edge computing (MEC)-enabled sea lane monitoring network (MSLMN) architecture with energy harvesting (EH) to support dynamic ship tracking, accident forensics, and anti-fouling through real-time maritime traffic scene monitoring. Under this architecture, the computation offloading and resource allocation are jointly optimized to maximize the long-term average throughput of MSLMN. Due to the dynamic environment and unavailable future network information, we employ the Lyapunov optimization technique to tackle the optimization problem with large state and action spaces and formulate a stochastic optimization program subject to queue stability and energy consumption constraints. We transform the formulated problem into a deterministic one and decouple the temporal and spatial variables to obtain asymptotically optimal solutions. Under the premise of queue stability, we develop a joint computation offloading and resource allocation (JCORA) algorithm to maximize the long-term average throughput by optimizing task offloading, subchannel allocation, computing resource allocation, and task migration decisions. Simulation results demonstrate the effectiveness of the proposed scheme over existing approaches.

Problem

• Optimizing computation offloading and resource allocation for maritime edge computing
• Maximizing long-term network throughput under dynamic environmental constraints
• Developing energy-efficient algorithm for real-time maritime monitoring applications

Innovation

• Joint optimization of computation offloading and resource allocation
• Lyapunov technique for stochastic optimization with constraints
• JCORA algorithm maximizes throughput via multi-dimensional decisions