Akademik Veri Yönetim Sistemi
Wireless Networks, cilt.31, sa.6, ss.4339-4362, 2025 (SCI-Expanded)
The Automatic Identification System (AIS) plays a vital role in vessel traffic services (VTS), supporting functions such as traffic control, shipment monitoring, vessel tracking, navigation, and collision avoidance. Due to the limited horizontal range of AIS signals, satellite communications have become essential for maritime monitoring in open seas, leading to the development of Satellite-Based AIS (S-AIS). However, S-AIS networks face significant challenges, notably inconsistent vessel tracking caused by the intermittent presence of vessels within satellite coverage areas. This issue is particularly critical for passenger vessels and is further exacerbated during emergencies or mission-critical operations. To address these limitations, we propose a novel distributed clustering approach that enables vessels to collaborate in transmitting AIS signals to satellites based on prioritized needs. This clustering method facilitates the analysis of signals beyond satellite footprints and supports effective pre-processing techniques to improve satellite transmission efficiency. We implement the proposed Distributed Vessel Clustering Algorithm (DVCA) in realistic maritime environments in the Mediterranean and Black Sea regions. Furthermore, we introduce optimization strategies to enhance network performance. Our experimental results demonstrate that vessel clustering and cooperation substantially improve the transmission of AIS signals to satellites, thereby enhancing the traceability and reliability of maritime monitoring systems.