CAREER: ACOustic Underwater Sensor NETwork (ACOUSNET) -- Multi-Level Adaptations

职业：声学水下传感器网络（ACOUSNET）——多级适应

• 批准号: 0845722
• 负责人: Liuqing Yang
• 金额: $ 45万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-05-01 至 2011-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0845722&HistoricalAwards=false
• 关键词: CAREER; ACOustic; Underwater; Sensor; NETwork

Underwater sensor networks have extensive applications ranging from natural ecosystem to security monitoring. The basic task of a sensor network is to facilitate collection of the sensed data. To this end, existing works assume a centralized strategy where the sensed data is immediately forwarded to a central sink from individual sensors. This strategy faces formidable challenges due to the intricacies of underwater acoustic channels. Based on qualitative analyses and quantitative calculations, we introduce the idea of distributed storage and collection for underwater sensor networks. In the ACOustic Underwater Sensor NETwork (ACOUSNET) project, we establish the transforming framework of distributed storage and collection of the sensed data. This transform entails multi-level adaptations to the unique underwater acoustic channels, physical limitations, desirable network features and network heterogeneity. Our foremost objective is to establish distributed data storage and collection mechanisms to optimize the ACOUSNET performance. Due to the very different properties of deep water and shallow water, we also put different emphasis and design requirements on them. The backbone of our ACOUSNET is a suite of physical and data link layer solutions for efficient data transmission. Compared with existing studies on these issues, our endeavor is unique by taking into account the battery nonlinearity and the network heterogeneity. Sea trials are essential for underwater networks since the ocean is very difficult to emulate due to its vast scale and complexity. The proposed research in this project will be significantly informed by the data collected in sea experiments under various conditions.

从自然生态系统到安全监测，水下传感器网络有着广泛的应用。传感器网络的基本任务是促进对传感数据的收集。为此，现有的工作假定采用集中式策略，即从单个传感器将所感知的数据立即转发到中央接收器。由于水声信道的复杂性，这一战略面临着严峻的挑战。在定性分析和定量计算的基础上，提出了水下传感器网络分布式存储与采集的思想。在水声水下传感器网络(ACOUSNET)项目中，我们建立了分布式存储和采集传感数据的转换框架。这种转变需要对独特的水声信道、物理限制、期望的网络特性和网络异构性进行多层次的适应。我们的首要目标是建立分布式数据存储和收集机制，以优化ACOUSNET的性能。由于深水和浅水的性质非常不同，我们对它们也提出了不同的侧重点和设计要求。我们ACOUSNET的主干是一套用于高效数据传输的物理层和数据链路层解决方案。与已有的研究相比，考虑了电池的非线性和网络的异构性，本文的研究是独一无二的。由于海洋的巨大规模和复杂性，海上试验对于水下网络是必不可少的，因为海洋非常难以模拟。该项目的拟议研究将从各种条件下的海洋试验中收集的数据中获得重要的信息。