Year: 2025
Type: article
Abstract: Maritime Internet of Things (IoT) with unmanned surface vessels (USVs) faces tight onboard computing and sparse wireless links. Compute-intensive vision and sensing workloads often exceed latency budgets, which undermines timely decisions. In this paper, we propose a novel distributed computation offloading framework for maritime IoT scenarios. By leveraging the limited computational resources of USVs within a device-to-device (D2D)-assisted edge network and the mobility advantages of UAV-assisted edge computing, we design a breadth-first search (BFS)-based distributed computation offloading game. Building upon this, we formulate a global latency minimization problem that jointly optimizes UAV hovering coordinates and arrival times. This problem is solved by decomposing it into subproblems addressed via a joint Alternating Direction Method of Multipliers (ADMM) and Successive Convex Approximation (SCA) approach, effectively reducing the time between UAV arrivals and hovering coordinates. Extensive simulations verify the effectiveness of our framework, demonstrating up to a 49.6% latency reduction compared with traditional offloading schemes.
Source: Sensors
DOI: 10.3390/s25185820
Authors: Baiyi Li, Jian Zhao, Tingting Yang
Institutions: Dalian Maritime University, Peng Cheng Laboratory
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Award: G1360296863
OpenAlex ID: W4414330835