Energy-aware Sparse Sensing

能量感知稀疏传感

• 批准号: 1405403
• 负责人: Jingxian Wu
• 金额: $ 36.24万
• 依托单位: University of Arkansas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1405403&HistoricalAwards=false
• 关键词: Energy; aware; Sparse; Sensing

Enabled by the widespread deployment of wireless sensor networks and the ubiquity of smart phones, large-scale sensing has played a critical role in many aspects of our life, such as environment monitoring, post disaster assessment, and biomedical sensing, etc. Such applications usually generate astronomical volumes of data, which impose formidable challenges for efficient processing, storage, and transmission of data. This project will address these challenges by developing a new paradigm of energy-aware sparse sensing scheme. The proposed scheme can significantly reduce the amount of data to be collected and processed in sensor networks powered by extremely limited energy sources, such as devices harvesting energy from light, vibration, or heat. The sparse sensing is enabled by the fact that some information is more important than others, thus the amount of collected information can be significantly reduced by sampling only the most essential information. In addition, the sparse sensing scheme can also be used for extracting useful information from an ocean of data that have already been collected by a system. This is especially important for crowd-sensing applications where a huge number of users voluntarily contribute sensing data with their smart phones or tablet computers. The proposed research will accelerate the wide range deployment of large-scale participatory sensing and energy-harvesting sensor networks. Results obtained from this project can be applied to a wide range of applications, such as disaster relief, stock market analysis, surveillance, and pollution monitoring, etc. This research will contribute to public safety, improve homeland security, and promote the social and economic development of the United States. The goal of this project is to conquer the "big data" challenge in large-scale energy-constrained sensing applications through a new paradigm of energy-aware sparse sensing, which dynamically and sparsely samples a random field by adapting to the energy availability of sensors and the time-varying nature of the monitored objects. The energy-aware sparse sensing scheme can significantly reduce the amount of data to be collected and processed, bridge the gap between energy supplies and energy demands in energy-constrained systems, and provide energy-efficient and scalable solutions to large-scale sensing applications. The specific research thrusts leading to this goal include: 1) Create a new paradigm of dynamic sparse sensing. The dynamic sparse sensing is enabled by "information diversity", i.e., data collected at different space locations and time instants have different impacts on a certain design objective. Thus the number of required samples can be significantly reduced by sampling only the most informative data, which can be adaptively identified by learning through previous sensing results. 2) Design energy-aware sparse sensing techniques for systems powered by energy harvesting devices such as photovoltaic or thermal devices. For systems with energy harvesting devices, there is energy diversity due to non-uniform energy supplies across the sensors. The energy-aware sparse sensing is achieved by aligning energy diversity with information diversity, so that the limited energy resources are used to collect the most informative data.

随着无线传感器网络的广泛应用和智能手机的普及，大规模传感在环境监测、灾后评估、生物医学传感等领域发挥着重要作用。这些应用通常会产生大量的数据，这对数据的有效处理、存储和传输提出了严峻的挑战。该项目将通过开发一种新的能量感知稀疏感知方案来解决这些挑战。所提出的方案可以显着减少在由极其有限的能源供电的传感器网络中收集和处理的数据量，例如从光、振动或热中收集能量的设备。稀疏感测是通过一些信息比其他信息更重要的事实来实现的，因此可以通过仅对最重要的信息进行采样来显著减少所收集的信息的量。此外，稀疏感测方案还可以用于从已经由系统收集的数据的海洋中提取有用的信息。这对于人群感应应用尤其重要，在人群感应应用中，大量用户自愿用他们的智能手机或平板电脑贡献感应数据。这项研究将加速大规模参与式传感和能量收集传感器网络的广泛部署。该项目的研究成果可应用于救灾、股市分析、监控、污染监测等领域，将为公共安全、改善国土安全、促进美国社会经济发展做出贡献。该项目的目标是通过一种新的能量感知稀疏感知范式来克服大规模能量受限传感应用中的“大数据”挑战，该范式通过适应传感器的能量可用性和监测对象的时变性质来动态和稀疏地对随机场进行采样。能量感知稀疏感知方案可以显著减少要收集和处理的数据量，弥合能量受限系统中能量供应和能量需求之间的差距，并为大规模传感应用提供节能和可扩展的解决方案。具体的研究方向包括：1）创建一个新的动态稀疏感知范式。动态稀疏感测通过“信息分集”来实现，即，在不同的空间位置和时刻收集的数据对某一设计目标具有不同的影响。因此，所需的样本的数量可以显着减少采样，只有最翔实的数据，这可以通过学习，通过以前的感测结果自适应地识别。 2)为由能量收集设备（如光伏或热设备）供电的系统设计能量感知稀疏感测技术。对于具有能量收集设备的系统，由于传感器之间的非均匀能量供应而存在能量多样性。能量感知的稀疏感知是通过将能量分集与信息分集相结合来实现的，使得有限的能量资源被用于收集最多的信息数据。