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

NeTS：小型：间歇连接传感器网络中的自适应数据保存：统一的存储能量优化方法

• 批准号: 1248315
• 负责人: Bin Tang
• 金额: $ 20.77万
• 依托单位: Azusa Pacific University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-08 至 2014-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1248315&HistoricalAwards=false
• 关键词: NeTS; Small; Adaptive; Data; Preservation

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.

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。在这种充满挑战的环境中，数据上传的机会是不可预测的和罕见的，这使得网络到基站的连接固有地间歇性，并将数据存储在网络内部是必要的。特别是，本项目1）发明了一系列能量和存储高效的数据保存算法，以自适应地克服所有导致数据丢失的关键原因，包括能量耗尽，存储耗尽，传感器节点的硬件故障以及整个网络中的总体存储溢出。提出的数据保存技术包括在网络内部分发、重新分发、复制和聚合传感器数据; 2）采用统一的存储能量优化方法，将传感器网络中最严格的两种资源存储空间和电池能量视为传感器网络中同一统一资源的两个子组成部分。通过上述数据保存技术，利用存储和能量的协同作用，优化存储和能量的联合分配以实现数据保存。本项目的成果包括间歇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计划开发关于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。