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

水下传感器网络具有广泛的应用范围，从自然生态系统到安全监测。传感器网络的基本任务是方便地收集感测数据。为此，现有的工作采用集中式策略，其中感测数据立即从单个传感器转发到中央接收器。由于水声信道的复杂性，这种策略面临着巨大的挑战。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和采集的思想。在声学水下传感器网络（ACOustic Underwater Sensor NETwork， 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。这种转换需要对独特的水声信道、物理限制、理想的网络特性和网络异质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，我们对它们的侧重点和设计要求也不同。我们的acoustic net的骨干是一套物理和数据链路层解决方案，用于有效的数据传输。与已有的研究相比，我们的研究是独特的，因为我们考虑了电池的非线性和网络的异质性。海上试验对于水下网络至关重要，因为海洋由于其巨大的规模和复杂性而很难模拟。在不同条件下的海洋实验中收集的数据将为本项目提出的研究提供重要的信息。

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.