IDBR: An End-to-End Sensor Based System for Environmental Monitoring

约翰霍普金斯大学的Katalin Szlavecz博士、Andreas Terzis博士和Alexander Szalay博士获得了一笔拨款，用于设计和开发端到端基于传感器的环境监测系统。最近出现的网络传感器从根本上改变了我们处理许多科学问题的方式，其中一些在过去是完全难以解决的。这些网络，基于新颖的低功耗无线传感器平台，允许设计经济实惠，非侵入性，大规模的监测系统。然而，目前的系统还远远不能提供应用科学家可以部署的现成解决方案。为了延长现场部署的电池寿命，需要复杂的算法来同步网络内的通信。将各种传感器连接到网络需要定制软件。在较大距离的现场传感器群之间的通信需要远程无线电桥接。为生物学家提供端到端系统需要一个动态加载的数据库，其中包含精心校准数据的高级视图，该数据库是根据原始测量自动构建的。根据他们之前的工作，PI？美国将发展一个？用户友好?针对环境科学应用的无线网络基础设施。该网络的新特征将包括为特定传感器配置网络节点的高级语言、模块化远程通信选项和具有端到端数据流的系统。作为巴尔的摩生态系统研究城市长期生态研究的一部分，端到端系统将首先在两个城市站点进行测试：(1)巴尔的摩市的城市森林；(2)巴尔的摩县的典型郊区社区。重点研究了土地利用管理和城市热岛效应对异质性城市土壤生态系统的影响。理解人类今天面临的许多环境问题并预测我们行为的结果需要对许多环境因素进行长期监测。建立和规划的环境观测站（LTER、NEON、MOOS）反映了这种监测需求。来自环境传感器网络的高分辨率数据有可能改变我们对生态系统的理解。我们的传感器网络将有能力监测原位土壤生态系统。软件组件的开发将使生态学家能够完全控制他们自己的实验，并轻松地重新配置传感器布局，而无需繁琐的重新编程。这本身将对实验的设计和部署产生巨大的影响。传感器网络为科学教育提供了丰富的环境。对于K-12和大学科学课程来说，它们是一种廉价的真实、最新数据来源。为了给这些课程提供资源，pi将开发基于网络的工具和教育项目，在约翰霍普金斯大学和其他地方的生物和环境科学课程中使用传感器。此外，pi将提供在线存储和数据分析服务，领域科学家将上传、分析和共享他们通过传感器网络实验收集的数据。

A grant has been awarded to Drs Katalin Szlavecz, Andreas Terzis and Alexander Szalay at the Johns Hopkins University to design and develop end-to-end sensor-based systems for environmental monitoring. The recent emergence of networked sensors fundamentally changes the way we can approach many scientific problems, some of them completely intractable in the past. These networks, based on novel low-power wireless sensor platforms allow the design of affordable, non-invasive, large-scale monitoring systems. However, current systems are far from providing an off-the-shelf solution that application scientists can deploy. A long battery life for field deployment requires sophisticated algorithms to synchronize communication within the network. Attaching a variety of sensors to the network requires custom software. Communication between clusters of sensors in the field at larger separation needs long-range radio bridges. Providing an end-to-end system for the biologists requires a dynamically loaded database with high level views of carefully calibrated data, built from the raw measurements automatically.Based upon their previous work the PI?s will develop a ?user friendly? wireless network infrastructure targeted at environmental science applications. The novel features of the network will consist of a high level language to configure the network nodes for the particular sensors, modular long-range communication options, and a system with an end-to-end data flow. The end-to-end system will be first tested in two urban sites as part of the Baltimore Ecosystem Study Urban Long-Term Ecological Research: (1) an urban forest in Baltimore City and (2) in a typical suburban neighborhood in Baltimore County. This deployment focuses on the effects of land use and management and the urban heat island effect on the heterogeneous urban soil ecosystem. Understanding the many environmental problems humans face today and predicting outcomes of our actions requires long-term monitoring of many environmental factors. Established and planned environmental observatories (LTER, NEON, MOOS) reflect this need for monitoring. The high resolution data from the environmental sensor networks has the potential to transform our understanding of ecological systems. Our sensor networks will have the ability to monitor the soil ecosystem in situ. The development of the software components will enable ecologists to be in full control of their own experiments, and easily reconfigure the sensor layout without cumbersome reprogramming. This in itself will have a tremendous impact on how the experiments are designed and deployed. Sensor networks offer a rich environment for science education. They can be an inexpensive source of real, current data for K-12 and college science courses. To provide resources for such courses, the PIs will develop web-based tools and educational projects that use sensors in biology and environmental science courses at Johns Hopkins University and elsewhere. Moreover, the PIs will offer an online storage and data analysis service in which domain scientists will upload, analyze, and share the data collected by their sensor network experiments.