{"title":"基于学习自动机的协同MIMO编队优化水下磁感应声传感器网络的能耗和覆盖","authors":"Qingyan Ren;Yanjing Sun;Sizhen Bian;Michele Magno","doi":"10.1109/TNSE.2025.3561751","DOIUrl":null,"url":null,"abstract":"Underwater Wireless Sensor Networks (UWSNs) offer promising exploration capabilities in challenging underwater environments, necessitating a focus on reducing energy consumption while guaranteeing monitoring coverage. Underwater magnetic induction (MI)-assisted acoustic cooperative multiple-input–multiple-output (MIMO) WSNs have shown advantages over traditional UWSNs in various aspects due to the seamless integration of sensor networks and communication technology. However, as an emerging topic, a critical gap exists, as they often overlook the vital considerations of monitoring coverage requirements and the dynamic nature of the unknown underwater environment. Moreover, these advantages can be further enhanced by harnessing the collaborative potential of multiple independent underwater nodes. This paper introduces a significant advancement to the field of MI-assisted Acoustic Cooperative MIMO WSNs leveraging the innovative Confident Information Coverage (CIC) and a reinforcement learning paradigm known as Learning Automata (LA). The paper presents the LA-based Cooperative MIMO Formation (LACMF) algorithm designed to minimize communication energy consumption in sensors while concurrently maximizing coverage performance. Experimental results demonstrate the LACMF considerably outperforms other schemes in terms of energy consumption, and network coverage to satisfy the imposed constraints, the CIC can be improved up to by an additional 52%, 11% reduction in energy consumption.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"3527-3540"},"PeriodicalIF":7.9000,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimizing Energy Consumption and Coverage in Underwater Magnetic Induction-Assisted Acoustic WSNs Using Learning Automata-Based Cooperative MIMO Formation\",\"authors\":\"Qingyan Ren;Yanjing Sun;Sizhen Bian;Michele Magno\",\"doi\":\"10.1109/TNSE.2025.3561751\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Underwater Wireless Sensor Networks (UWSNs) offer promising exploration capabilities in challenging underwater environments, necessitating a focus on reducing energy consumption while guaranteeing monitoring coverage. Underwater magnetic induction (MI)-assisted acoustic cooperative multiple-input–multiple-output (MIMO) WSNs have shown advantages over traditional UWSNs in various aspects due to the seamless integration of sensor networks and communication technology. However, as an emerging topic, a critical gap exists, as they often overlook the vital considerations of monitoring coverage requirements and the dynamic nature of the unknown underwater environment. Moreover, these advantages can be further enhanced by harnessing the collaborative potential of multiple independent underwater nodes. This paper introduces a significant advancement to the field of MI-assisted Acoustic Cooperative MIMO WSNs leveraging the innovative Confident Information Coverage (CIC) and a reinforcement learning paradigm known as Learning Automata (LA). The paper presents the LA-based Cooperative MIMO Formation (LACMF) algorithm designed to minimize communication energy consumption in sensors while concurrently maximizing coverage performance. Experimental results demonstrate the LACMF considerably outperforms other schemes in terms of energy consumption, and network coverage to satisfy the imposed constraints, the CIC can be improved up to by an additional 52%, 11% reduction in energy consumption.\",\"PeriodicalId\":54229,\"journal\":{\"name\":\"IEEE Transactions on Network Science and Engineering\",\"volume\":\"12 5\",\"pages\":\"3527-3540\"},\"PeriodicalIF\":7.9000,\"publicationDate\":\"2025-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Network Science and Engineering\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11134544/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Network Science and Engineering","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11134544/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
Optimizing Energy Consumption and Coverage in Underwater Magnetic Induction-Assisted Acoustic WSNs Using Learning Automata-Based Cooperative MIMO Formation
Underwater Wireless Sensor Networks (UWSNs) offer promising exploration capabilities in challenging underwater environments, necessitating a focus on reducing energy consumption while guaranteeing monitoring coverage. Underwater magnetic induction (MI)-assisted acoustic cooperative multiple-input–multiple-output (MIMO) WSNs have shown advantages over traditional UWSNs in various aspects due to the seamless integration of sensor networks and communication technology. However, as an emerging topic, a critical gap exists, as they often overlook the vital considerations of monitoring coverage requirements and the dynamic nature of the unknown underwater environment. Moreover, these advantages can be further enhanced by harnessing the collaborative potential of multiple independent underwater nodes. This paper introduces a significant advancement to the field of MI-assisted Acoustic Cooperative MIMO WSNs leveraging the innovative Confident Information Coverage (CIC) and a reinforcement learning paradigm known as Learning Automata (LA). The paper presents the LA-based Cooperative MIMO Formation (LACMF) algorithm designed to minimize communication energy consumption in sensors while concurrently maximizing coverage performance. Experimental results demonstrate the LACMF considerably outperforms other schemes in terms of energy consumption, and network coverage to satisfy the imposed constraints, the CIC can be improved up to by an additional 52%, 11% reduction in energy consumption.
期刊介绍:
The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.