{"title":"基于混合聚类和强化学习的水下机器人辅助数据采集","authors":"Yanxia Chen , Rongxin Zhu , Azzedine Boukerche , Qiuling Yang","doi":"10.1016/j.adhoc.2025.103877","DOIUrl":null,"url":null,"abstract":"<div><div>Underwater Acoustic Sensor Networks (UASNs) have garnered increasing attention for applications such as environmental monitoring, disaster response, and marine resource exploration. Despite their advantages, including self-organization and flexible deployment, UASNs face significant challenges in the underwater environment, such as energy constraints, propagation delays, and limited bandwidth. Addressing these challenges requires efficient methods to optimize energy usage and data transmission. In this work, we propose ACRL, a clustering and reinforcement learning-based approach for underwater data collection. ACRL combines a hybrid Fuzzy C Means (FCM) and Firefly Algorithm (FA) to optimize clustering and cluster head selection, reducing energy consumption and workload while maintaining efficient data collection. Additionally, ACRL leverages Q-learning to refine Autonomous Underwater Vehicle (AUV) trajectory planning. Extensive simulations demonstrate that ACRL achieves reduced energy consumption and data collection delay, outperforming existing methods under various scenarios.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103877"},"PeriodicalIF":4.8000,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AUV-Assisted data collection using hybrid clustering and reinforcement learning in underwater acoustic sensor networks\",\"authors\":\"Yanxia Chen , Rongxin Zhu , Azzedine Boukerche , Qiuling Yang\",\"doi\":\"10.1016/j.adhoc.2025.103877\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Underwater Acoustic Sensor Networks (UASNs) have garnered increasing attention for applications such as environmental monitoring, disaster response, and marine resource exploration. Despite their advantages, including self-organization and flexible deployment, UASNs face significant challenges in the underwater environment, such as energy constraints, propagation delays, and limited bandwidth. Addressing these challenges requires efficient methods to optimize energy usage and data transmission. In this work, we propose ACRL, a clustering and reinforcement learning-based approach for underwater data collection. ACRL combines a hybrid Fuzzy C Means (FCM) and Firefly Algorithm (FA) to optimize clustering and cluster head selection, reducing energy consumption and workload while maintaining efficient data collection. Additionally, ACRL leverages Q-learning to refine Autonomous Underwater Vehicle (AUV) trajectory planning. Extensive simulations demonstrate that ACRL achieves reduced energy consumption and data collection delay, outperforming existing methods under various scenarios.</div></div>\",\"PeriodicalId\":55555,\"journal\":{\"name\":\"Ad Hoc Networks\",\"volume\":\"178 \",\"pages\":\"Article 103877\"},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2025-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ad Hoc Networks\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1570870525001258\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ad Hoc Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1570870525001258","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
AUV-Assisted data collection using hybrid clustering and reinforcement learning in underwater acoustic sensor networks
Underwater Acoustic Sensor Networks (UASNs) have garnered increasing attention for applications such as environmental monitoring, disaster response, and marine resource exploration. Despite their advantages, including self-organization and flexible deployment, UASNs face significant challenges in the underwater environment, such as energy constraints, propagation delays, and limited bandwidth. Addressing these challenges requires efficient methods to optimize energy usage and data transmission. In this work, we propose ACRL, a clustering and reinforcement learning-based approach for underwater data collection. ACRL combines a hybrid Fuzzy C Means (FCM) and Firefly Algorithm (FA) to optimize clustering and cluster head selection, reducing energy consumption and workload while maintaining efficient data collection. Additionally, ACRL leverages Q-learning to refine Autonomous Underwater Vehicle (AUV) trajectory planning. Extensive simulations demonstrate that ACRL achieves reduced energy consumption and data collection delay, outperforming existing methods under various scenarios.
期刊介绍:
The Ad Hoc Networks is an international and archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in ad hoc and sensor networking areas. The Ad Hoc Networks considers original, high quality and unpublished contributions addressing all aspects of ad hoc and sensor networks. Specific areas of interest include, but are not limited to:
Mobile and Wireless Ad Hoc Networks
Sensor Networks
Wireless Local and Personal Area Networks
Home Networks
Ad Hoc Networks of Autonomous Intelligent Systems
Novel Architectures for Ad Hoc and Sensor Networks
Self-organizing Network Architectures and Protocols
Transport Layer Protocols
Routing protocols (unicast, multicast, geocast, etc.)
Media Access Control Techniques
Error Control Schemes
Power-Aware, Low-Power and Energy-Efficient Designs
Synchronization and Scheduling Issues
Mobility Management
Mobility-Tolerant Communication Protocols
Location Tracking and Location-based Services
Resource and Information Management
Security and Fault-Tolerance Issues
Hardware and Software Platforms, Systems, and Testbeds
Experimental and Prototype Results
Quality-of-Service Issues
Cross-Layer Interactions
Scalability Issues
Performance Analysis and Simulation of Protocols.