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The Intelligent River#x00A9;: Implementation of Sensor Web Enablement technologies across three tiers of system architecture: Fabric, middleware, and application

机译:智能河流&#x00a9 ;:在三层系统架构中实现传感器网络启用技术:织物,中间件和应用

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Population growth, energy demand, and climate change are placing an unprecedented strain on water resources, requiring a fundamental shift in how these resources are managed. More precisely, resource management programs must embrace a new paradigm, one with realtime environmental monitoring at its core. The Intelligent River© is an environmental and hydrological observation system engineered to support research and management of water resources at watershed scales. The system architecture is comprised of three primary tiers: (i) a field-deployed sensor fabric and uplink infrastructure, (ii) real-time data streaming middleware, and (iii) repository, presentation, and web services. Sensor Web Enablement (SWE) adoption decisions revolve around balancing efficiency concerns and implementation time with capability and standards compliance. In this context, our team has examined, applied, and evaluated SWE technologies to enable data archival, access, and discovery. We have found varying levels of success with SWE adoption across the three tiers. At the fabric layer, platform configurability and ease-of-integration have been important engineering drivers. SensorML arose as a natural candidate solution; however, its resource requirements are largely incompatible with our target hardware platforms. At the middleware layer, recent efforts have focused on the use of SensorML and a metadata catalog to perform metadata annotation. This solution appends SensorML elements onto incoming observations, supporting data processing and semantic resolution. During early development of middleware technologies, we linked sensor platforms with web services using the transactional profile of the Sensor Observation Service (SOS) to perform data insertion and retrieval queries. At the application level, SOS is used to support data discovery and access, and Sensor Alert Service (SAS) is used to provide near-real time notifications of sensor status and QA/QC failures. In this paper, we - - report on our experiences, both positive and negative, and outline potential solutions to some of the most important obstacles we have encountered.
机译:人口增长,能源需求和气候变化在水资源上处于前所未有的压力,需要对这些资源进行管理的根本转变。更准确地说,资源管理计划必须采用新的范例,一个具有实时环境监测的核心。智能河流©是一种环境和水文观测系统,以支持流域鳞片的水资源研究和管理。系统架构由三个主要层组成:(i)现场部署的传感器结构和上行链路基础架构,(ii)实时数据流中间件,(iii)存储库,演示文稿和Web服务。传感器Web启用(SWE)采用决策围绕平衡效率问题和实施时间,具有能力和标准合规性。在此背景下,我们的团队已审查,应用和评估SWE技术,以启用数据档案,访问和发现。我们发现各种各样的成功水平与跨越三层的SWE采用。在织物层,平台可配置性和易于集成是重要的工程司机。传感器作为天然候选解决方案而产生;但是,其资源需求与目标硬件平台很不兼容。在中间件层,最近的努力专注于使用SensorML和元数据目录来执行元数据注释。该解决方案将visorml元素附加到输入观测上,支持数据处理和语义分辨率。在中间件技术的早期开发期间,我们使用传感器观测服务(SOS)的事务简档将传感器平台与Web服务联系起来执行数据插入和检索查询。在应用程序级别,SOS用于支持数据发现和访问,传感器警报服务(SAS)用于提供传感器状态和QA / QC故障的近实时通知。在本文中,我们 - - 报告我们的经验,积极和消极,以及我们遇到的一些最重要的障碍的概述潜在解决方案。

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