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Against the backdrop of global trade growth and marine economy expansion, waterway navigation safety in the Yangtze River Delta (YRD) has emerged as a critical priority due to its profound economic and ecological significance. Traditional Automatic Identification System (AIS) monitoring faces substantial challenges, including accuracy limitations and illegal shutdowns, which hinder effective management of illegal vessels and equipment failures. To address these issues, this paper proposes an improved PEGASIS (Power-Efficient Gathering in Sensor Information Systems) algorithm for wireless sensor networks (WSNs), aiming to enhance the efficiency and reliability of maritime intelligent monitoring systems in the YRD. The improved algorithm optimizes chain-structured topology by deploying dual-node groups on both sides of straits and introducing a time-slot based dual next-hop transmission mechanism. This design reduces packet loss rate and transmission delay through redundant data paths and hierarchical fusion. Simulation experiments in a 40 -node network model demonstrate remarkable performance improvements: the average packet loss rate decreases from 9.33 % to 7.47 %, and the average transmission delay is reduced from 34.80 units to 27.84 units, outperforming the classic PEGASIS algorithm. This research provides critical technical support for improving maritime safety and management efficiency in the YRD. Future work will focus on integrating machine learning for dynamic optimization, expanding to largescale heterogeneous networks, and developing multi-sensor fusion frameworks.