NeTS: Small: Adaptive Data Preservation in Intermittently Connected Sensor Networks: A Unified Storage-Energy Optimization Approach

该项目的研究目标是创建一个框架，以有效地保存在具有挑战性的环境中运行的传感器网络应用程序中生成的数据。这些应用包括视觉和声学传感器网络，海洋地震或水下传感器网络，以及火山和冰川监测。在这种具有挑战性的环境中，数据上传的机会将是不可预测和罕见的，这使得与基站的网络连接本质上是间歇性的，因此必须在网络中存储数据。具体而言，本项目1)发明了一系列节能和存储高效的数据保存算法，自适应克服导致数据丢失的所有关键原因，包括能量耗尽、存储耗尽、传感器节点硬件故障、全网整体存储溢出等。提出的数据保存技术包括在网络内分发、重分发、复制和聚合感知数据；2)采用统一存储-能量优化方法，将存储空间和电池能量这两个传感器网络中最严格的资源视为传感器网络中同一统一资源的两个子组件。通过上述数据保存技术，利用存储和能量的协同作用，优化数据保存的联合分配。该项目的成果包括间歇性连接传感器网络的基本架构、理论、算法和协议。该项目将对许多基于传感器网络的科学应用产生重大影响，包括自然灾害预警和气候变化监测，其中许多应用在具有挑战性的环境中运行，同时随着时间的推移产生大量数据。pi计划开发接口算法设计和传感器网络的研究生/本科生课程，从而教育学生算法思维的重要性，同时向他们展示最新的网络技术。

The research objective of this project is to create a framework to effectively preserve data generated in sensor network applications that operate in challenging environments. These applications include visual and acoustic sensor networks, ocean seismic or underwater sensor networks, and volcanic and glacial monitoring. In such challenging environments, the data uploading opportunities would be unpredictable and rare, making the network connectivity to the base station inherently intermittent and storing data inside the network necessary.In particular, this project 1) Invents a series of energy- and storage-efficient data preservation algorithms to adaptively overcome all the key causes of data loss, including energy depletion, storage depletion, hardware failure of sensor nodes, and overall storage overflow in the entire network. The proposed data preservation techniques include distributing, redistributing, replicating, and aggregating the sensed data inside the network; 2) Takes a unified storage-energy optimization approach, in which storage space and battery energy, the two most stringent resources in sensor networks, are viewed as two sub-components of the same unified resource in the sensor network. The joint allocation of storage and energy is optimized for data preservation by exploiting their synergies via aforesaid data preservation techniques.The outcomes of this project include basic architectures, theories, algorithms, and protocols for intermittently connected sensor networks. This project would have significant impact on many sensor network-based scientific applications, including natural disaster warning and climate change monitoring, many of which operate in challenging environments while generating large amounts of data over time. The PIs plan to develop graduate/undergraduate courses on interfacing algorithm design and sensor networks, thus educating students the importance of algorithmic thinking while exposing them the latest networking technologies.