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

水下传感器网络在自然生态系统和安全监测等领域有着广泛的应用。传感器网络的基本任务是促进感测数据的收集。为此，现有的作品假设一个集中的策略，其中感测到的数据立即转发到一个中央汇从各个传感器。由于水声信道的复杂性，这一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。在水声传感器网络（ACOUSNET）项目中，我们建立了传感数据分布式存储和采集的转换框架。这种转换需要多层次的适应独特的水声信道，物理限制，理想的网络功能和网络的异构性。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。由于深水区和浅水区的性质有很大的不同，所以对它们的重视程度和设计要求也有所不同。 我们的ACOUSNET骨干网是一套物理和数据链路层解决方案，可实现高效的数据传输。与现有的研究相比，这些问题，我们的奋进是独特的，考虑到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，因为海洋由于其庞大的规模和复杂性而非常难以模拟。该项目中拟议的研究将在各种条件下收集的海上实验数据中得到重要信息。

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.