基于混合聚类和强化学习的水下机器人辅助数据采集

IF 4.8 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yanxia Chen , Rongxin Zhu , Azzedine Boukerche , Qiuling Yang
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引用次数: 0

摘要

水声传感器网络在环境监测、灾害响应和海洋资源勘探等方面的应用越来越受到人们的关注。尽管具有自组织和灵活部署等优势,但uasn在水下环境中面临着巨大的挑战,如能量限制、传播延迟和有限的带宽。解决这些挑战需要有效的方法来优化能源使用和数据传输。在这项工作中,我们提出了ACRL,一种基于聚类和强化学习的水下数据收集方法。ACRL结合了混合模糊C均值(FCM)和萤火虫算法(FA)来优化聚类和簇头选择,在保持高效数据收集的同时降低能耗和工作量。此外,ACRL还利用Q-learning来优化自主水下航行器(AUV)的轨迹规划。大量的仿真表明,ACRL在各种场景下实现了更低的能耗和数据收集延迟,优于现有方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
Ad Hoc Networks
Ad Hoc Networks 工程技术-电信学
CiteScore
10.20
自引率
4.20%
发文量
131
审稿时长
4.8 months
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 The Ad Hoc Networks considers original, high quality and unpublished contributions addressing all aspects of ad hoc and sensor networks.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.
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