Leveraging Cloud-Fog Automation for Autonomous Collision Detection and Classification in Intelligent Unmanned Surface Vehicles

2025-06-22

AI Summary

To address the insufficient real-time perception and predictive capability of Unmanned Surface Vehicles (USVs) in Industrial Cyber-Physical Systems (ICPS), constrained by onboard computational limitations and communication latency, this paper proposes a cloud–fog–edge collaborative three-tier distributed architecture. The architecture integrates edge-AI–enabled real-time inference, cloud-based high-level analytics, and IoT sensing to achieve low-latency collision detection and fine-grained classification. Its key innovation lies in establishing a marine-oriented Cloud-Fog Automation paradigm that jointly balances centralized optimization and distributed decision-making, while supporting continuous model updates and elastic system scalability. Experimental results demonstrate an 86% classification accuracy and a 42% reduction in end-to-end latency, significantly outperforming conventional cloud-centric or pure edge-only approaches in responsiveness, computational efficiency, and scalability.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Industrial Cyber-Physical Systems (ICPS) technologies are foundational in driving maritime autonomy, particularly for Unmanned Surface Vehicles (USVs). However, onboard computational constraints and communication latency significantly restrict real-time data processing, analysis, and predictive modeling, hence limiting the scalability and responsiveness of maritime ICPS. To overcome these challenges, we propose a distributed Cloud-Edge-IoT architecture tailored for maritime ICPS by leveraging design principles from the recently proposed Cloud-Fog Automation paradigm. Our proposed architecture comprises three hierarchical layers: a Cloud Layer for centralized and decentralized data aggregation, advanced analytics, and future model refinement; an Edge Layer that executes localized AI-driven processing and decision-making; and an IoT Layer responsible for low-latency sensor data acquisition. Our experimental results demonstrated improvements in computational efficiency, responsiveness, and scalability. When compared with our conventional approaches, we achieved a classification accuracy of 86%, with an improved latency performance. By adopting Cloud-Fog Automation, we address the low-latency processing constraints and scalability challenges in maritime ICPS applications. Our work offers a practical, modular, and scalable framework to advance robust autonomy and AI-driven decision-making and autonomy for intelligent USVs in future maritime ICPS.

Problem

• Overcome onboard computational constraints in USVs
• Reduce communication latency for real-time data processing
• Enhance scalability of maritime ICPS applications

Innovation

• Distributed Cloud-Edge-IoT maritime architecture
• Hierarchical Cloud-Fog Automation paradigm
• AI-driven low-latency sensor processing