Toward Optimal MEC-Based Collision Avoidance System for Cooperative Inland Vessels: A Federated Deep Learning Approach

Cooperative collision avoidance between inland waterway ships is among the envisioned services on the Internet of Ships. Such a service aims to support safe navigation while optimizing ships’ trajectories. However, to deploy it, timely and accurate prediction of ships’ positioning with real-time reactions is needed to anticipate collisions. In such a context, ships positions are usually predicted using advanced Machine Learning (ML) techniques. Traditionally, ML schemes require that the data be processed in a centralized way, e.g., a cloud data center managed by a third party. However, these schemes are not suitable for the collisions avoidance service due to the inaccessibility of ships’ positioning data by this third party, and allowing connected ships to get access to sensitive information. Therefore, in this paper, the authors design a new cooperative collision avoidance system for inland ships, while ensuring data security and privacy. Our system is based on deep federated learning to collaboratively build a model of ship positioning prediction, while avoiding sharing their private data. In addition, it is deployed at multi-access edge computing (MEC) level to provide low-latency communication to ensure fast responses during collision detection. Furthermore, it relies on Blockchain and smart contracts to ensure trust and valid communications between ships and MEC nodes. They evaluate the proposed system using a generated dataset representing ships mobility in France. The results, which demonstrate the accuracy of our prediction model, prove the effectiveness of our cooperative collision avoidance system in ensuring timely and reliable communications and avoiding collisions between ships.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2022.3154158
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2023, IEEE.
• Authors:
  • Hammedi, Wided
  • Brik, Bouziane
  • Senouci, Sidi Mohammed
• Publication Date: 2023-2

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 2525-2537
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 24
  • Issue Number: 2
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Cooperation; Crash avoidance systems; Inland waterways vessels; Machine learning; Positioning; Predictive models
• Subject Areas: Data and Information Technology; Marine Transportation; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01876264
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 21 2023 9:27AM