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

TL;DR

The paper tackles the challenge of real-time maritime edge computing with energy harvesting by formulating a stochastic optimization problem to maximize long-term network throughput under queue stability and energy constraints. It introduces JCORA, a joint computation offloading and resource allocation framework based on Lyapunov optimization, which decouples decision-making into per-slot subproblems for task offloading, subchannel assignment, task migration, and computing resource allocation. The authors prove an asymptotic near-optimality bound for JCORA, establish a throughput–queue length tradeoff with a tunable parameter $V$, and analyze computational complexity. Simulation results on a two-tier maritime MEC network with EH demonstrate that JCORA outperforms several benchmarks in both throughput and latency while respecting energy constraints, indicating strong practical potential for offshore MEC-enabled networks.

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.

Figures (12)

• Figure 1: Network model.
• Figure 2: The average throughput and average latency versus $V$.
• Figure 3: The energy queue length versus time slot.
• Figure 4: The average energy consumption versus energy queue length
• Figure 5: The average throughput versus number of TUs

Theorems & Definitions (2)

• Theorem 1
• Theorem 2