一种新型的传感器WEB标准一传感器WEB整合框架

传感器网络有着广阔的应用前景,是目前的研究热点之一。但是由于传感器网络的异构性,将它们互联在一起,达到资源有效分配、共享的目的比较困难。SWE提出了解决该问题的有效方法,并且成为了事实上的工业标准。本文分析了SWE的产生背景,阐述了SWE的组件和体系结构,重点讨论了SWE的信息模型,分析和总结了SWE的重点研究问题以及关键技术的解决思路,介绍了两个典型的研究实例。     

无线传感器网络抗毁性

无线传感器网络常因能量耗尽、硬件故障或者遭遇入侵等原因导致节点失效,使得原本连通的网络拓扑分割,甚至导致全局网络受损.因此,研究抗毁性对解决无线传感器网络规模化应用瓶颈具有重要的理论价值.目前有关无线传感器网络抗毁性的研究主要关注以下3个方面:(1)造成无线传感器网络节点失效与网络受损的原因有哪些;(2)如何准确度量无线传感器网络抗毁性;(3)如何有效提升无线传感器网络抗毁性能.文中有针对性地分别从失效成因、测度、提升策略与故障检测和修复等4个方面对有关研究进行归纳和分类,重点分析无线传感器网络抗毁性优化方法,包括路由控制、网络重构和拓扑演化.最后,指出无线传感器网络抗毁性研究未来发展所面临的技术挑战,并就其发展趋势进行探讨.     

新颖的无线传感器网络组网算法

为有效解决无线传感器网络的网络维护困难性问题,研究性能更好的网络组网算法是一种有效的解决手段．本文给出了无线传感器网络体系结构模型及其连通性定义,在此基础上本文提出了一个具有网络连通性能好的无线传感器网络组网算法并进行了仿真分析,由该算法构成的无线传感器网络不仅具有连通性能好,而且具有保护网络节点能量和控制网络功率的优良特点,所以在传感器网络实际应用中有着光明的前景．     

三维无线传感器网络综述

针对三维无线传感器网络中的基础性问题,分析和总结了近年来相关的主要研究方向和重要研究成果.同时,深入讨论了三维无线传感器网络研究所面临的挑战和亟须解决的问题,为今后的研究提供了方向.     

无线传感器网络远程代码更新技术研究进展

高可用性是无线传感器网络的设计目标之一,远程代码更新可增加或更新运行在传感器节点上的软件,是提高无线传感器网络可用性的重要支撑。分析和总结了传感器网络远程代码更新研究领域的研究成果,阐述了主要远程代码更新机制及其待解决的问题,最后探讨了今后应研究的问题,指明了下一步研究的重点和难点。     

无线传感器网络

无线传感器网络被誉为21世纪四大高新技术之一。文章对无线传感器网络与传统网络的区别、技术特点和体系结构进行了探讨,重点针对无线传感器网络的现有各层协议进行了分析比较,最后,针对无线传感器网络发展所需的关键技术,提出来来我们重点研究的几个亟待解决的技术问题。     

无线传感器网络中的地址分配协议

地址用来标识节点,使能网络通信协议,在无线传感器网络中扮演重要角色.由于无线传感器网络中节点众多,再考虑其网络动态性,手动地为每个节点分配地址是一件繁琐甚至无法完成的工作,于是,地址分配协议成为必需.由于无线传感器网络自身所具有的特点,传统的DHCP协议和ad hoc网络的地址分配协议对其不再适用.分析了无线传感器网络地址分配协议的必要性,总结了地址分配协议需要解决的问题和所面临的挑战,并对已有地址分配协议进行了分类.介绍和比较了当前有代表性的地址分配协议,并指出了它们的问题,分析了地址分配协议下一步研究的重点.     

TCP/IP在无线传感器网中的应用研究

介绍了一种传感器网络结构.针对传感器网络的特点对TCP/IP协议进行了分析,总结出TCP/IP的运用在传感器网络中的缺陷,并给出了相应的解决方法.提出了一种三维IP地址的分配方法,并在路由协议中使用了一种传感器网络传输控制协议.最后,提出了未来传感器网络传输控制协议的研究方向和重点.     

无线传感器网络安全策略分析与设计

无线传感器网络面临安全性差的问题,本文详细分析了无线传感器网络的安全需求及约束,并对现有安全技术进行分析与比较,有针对性地选择并设计了一套有效的安全策略,从而解决无线传感器网络安全问题.     

基于SWE的无线传感器网络数据目录服务研究与实现

SWE (Sensor Web Enablement)是基于OGC组织的web服务框架及信息模型提出的针对无线传感器网络资源的框架,通过提供统一的接口及编码实现了异构网络中资源的管理.为实现传感资源的有效整合,在应用SWE基础上扩展了基于ebRIM模型的OGC CSW规范,提出以OGC的SWE框架内SOS服务和CSW规范的融合的方法,并从系统实现角度,讨论了系统设计及数据格式以及实现验证等关键问题.未来的传感应用依赖于海量传感数据处理,对于传感资源的整合需求也应覆盖SWE的全部服务.这也是下一步的努力方向.     

Sensor Web的SOS服务研究与实现

Sensor Web是NASA/Jet Propulsion Laboratory(JPL)的Kevin Delin首先提出并将其定义为被部署用来监控和探测新环境,内部能够相互通信的分布式传感器组成的网络系统。自OGC提出Sensor Web的相关标准以来,传感器监测服务SOS就是最基本最核心的服务规范。文章首先对SWE的传感器观测服务SOS核心操作进行介绍,然后设计了传感器观测服务SOS架构,并对每层作了说明,利用Java、Tomcat等技术对SOS的架构进行部署和测试。利用所部署的SOS架构中的核心操作可获取实时或历史的观测数据。     

I3WSN: Industrial Intelligent Wireless Sensor Networks for indoor environments

Sensor Web Enablement (SWE) technologies have been successfully applied to a great variety of outdoor scenarios but, in practical terms, little effort has been applied for indoor environments, and even less in the field of industrial applications. This article presents an intelligent SWE application for an indoor and industrial scenario, with the aim of improving and increasing the levels of human safety. The base low-level architecture is built on top of wireless sensor networks (WSN) connected to a Sensor Observation Service (SOS). Higher layers in the architecture include services that make real time decisions based on the collected data. Both simulation and experimental results are presented. The paper shows the viability of our approach in terms of performance, scalability, modularity and safety.     

A comparison of 18 winter seasons of in situ and passive microwave-derived snow water equivalent estimates in Western Canada

The Meteorological Service of Canada (MSC) has developed an operational snow water equivalent (SWE) retrieval algorithm suite for western Canada that can be applied to both Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave/Imager (SSM/I) data. Separate algorithms derive SWE for open environments, deciduous, coniferous, and sparse forest cover. A final SWE value represents the area-weighted average based on the proportional land cover within each pixel. The combined SSM/I and SMMR time series of dual polarized, multichannel, spaceborne passive microwave brightness temperatures extends back to 1978, providing a lengthy time series for algorithm assessment. In this study, 5-day average (pentad) passive microwave-derived SWE imagery for 18 winter seasons (December, January, February 1978/79 through 1995/96) was compared to SWE estimates taken from a distributed network of surface measurements throughout western Canada.Results indicated both vegetative and snowpack controls on the performance of MSC algorithms. In regions of open and low-density forest cover, the in situ and passive microwave SWE data exhibited both strong agreement and similar levels of interannual variability. In locations where winter season SWE typically exceeded 75 mm, and/or dense vegetative cover was present, dataset agreement weakened appreciably, with little interannual variability in the passive microwave SWE retrievals. These results have important implications for extending the SWE monitoring capability of the MSC algorithm suite to northern regions such as the Mackenzie River basin.     

异构感知数据的动态适配接入方法

开放地理空间信息联盟的传感器网整合框架（ SWE）定义了异构传感器统一描述标准,却未定义底层的异构数据接入该框架的过程,导致不同类型的感知数据接入时需要人工提供不同的数据接入方式。针对以上问题,设计了异构传感器信息模型（ HSIM）,描述了传感器的自身属性、观测能力等特征,同时概括了传感器数据结构和数据传输协议的特征;提出了一种异构感知数据动态适配接入方法,该方法通过动态生成适配器实现异构感知数据的自动解析和接入SWE的过程。气象异构数据适配接入系统表明：该方法能够在较少的人工干预下实现物理传感器与虚拟传感器异构感知数据的自动解析与接入,为异构感知数据动态集成提供了高效率的解决方案。     

Visualizing gridded time series data with self organizing maps: An application to multi-year snow dynamics in the Northern Hemisphere

Gridded time-series data are increasingly available for climatology research. Microwave imagery of snow water equivalent (SWE) has been accumulated at daily basis for over two decades, but complex spatial-temporal patterns in SWE dataset pose great challenges for exploration and interpretation. This paper introduces the use of several perspectives from a tri-space conceptualization of a time series of SWE grids combined with dimensionality reduction via the self-organizing map (SOM) method. While SOM has been predominantly viewed as a clustering mechanism within climatology research, we expand the visual-analytic potential of SOM for climate research with a series of conceptual, computational, and visual transformations. Specifically, we apply a medium-resolution SOM to an SWE dataset covering the Northern Hemisphere over a 20-year period, with high temporal resolution. Through clustering and visualization a number of distinct SWE patterns are identified, including mountainous, coastal, and continental regions. A subset of cells from six areas are selected for transition analysis, including mountainous (Sierra Nevada, Western Himalaya, Eastern Himalaya) and continental (central Siberia, western Russia and Midwest United States) regions. By combining with trajectory analysis, this SOM documents notable transitions in seasonal SWE accumulation and melt patterns in mountain ranges, suggesting that SWE in some geographic locations alternates between different discrete annual patterns. In the Sierra Nevada, transitions to classes with high SWE are shown to be related to the Southern Oscillation Index, demonstrating that the annual patterns and transitions in SWE regime identified by the SOM correspond to synoptic climate conditions.     

WSN中基于分簇结构的密钥管理方案研究

安全机制中密钥管理方案一直是无线传感器网络的研究热点。在分析无线传感器网络的基础上。具体地描述了分簇式密钥管理方案并分析其优缺点,提出几点基于分簇式密钥管理方案的研究设想。     

Snowpack and runoff generation using AMSR-E passive microwave observations in the Upper Helmand Watershed, Afghanistan

Passive microwave estimates of snow water equivalent (SWE) were examined to determine their usefulness for evaluating water resources in the remote Upper Helmand Watershed, central Afghanistan. SWE estimates from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and the Special Sensor Microwave/Imager (SSM/I) passive microwave data were analyzed for six winter seasons, 2004-2009. A second, independent estimate of SWE was calculated for these same time periods using a hydrologic model of the watershed with a temperature index snow model driven using the Tropical Rainfall Measuring Mission (TRMM) gridded estimates of precipitation. The results demonstrate that passive microwave SWE values from SSM/I and AMSR-E are comparable. The AMSR-E sensor had improved performance in the early winter and late spring, which suggests that AMSR-E is better at detecting shallow snowpacks than SSM/I. The timing and magnitude of SWE values from the snow model and the passive microwave observations were sometimes similar with a correlation of 0.53 and accuracy between 55 and 62%. However, the modeled SWE was much lower than the AMSR-E SWE during two winter seasons in which TRMM data estimated lower than normal precipitation. Modeled runoff and reservoir storage predictions improved significantly when peak AMSR-E SWE values were used to update the snow model state during these periods. Rapid decreases in passive microwave SWE during precipitation events were also well aligned with flood flows that increased base flows by 170 and 940%. This finding supports previous northern latitude studies which indicate that the passive microwave signal's lack of scattering can be used to detect snow melt. The current study's extension to rain on snow events suggests an opportunity for added value for flood forecasting.     

Quantifying the uncertainty in passive microwave snow water equivalent observations

Passive microwave sensors (PM) onboard satellites have the capability to provide global snow observations which are not affected by cloudiness and night condition (except when precipitating events are occurring). Furthermore, they provide information on snow mass, i.e., snow water equivalent (SWE), which is critically important for hydrological modeling and water resource management. However, the errors associated with the passive microwave measurements of SWE are well known but have not been adequately quantified thus far. Understanding these errors is important for correct interpretation of remotely sensed SWE and successful assimilation of such observations into numerical models.This study uses a novel approach to quantify these errors by taking into account various factors that impact passive microwave responses from snow in various climatic/geographic regions. Among these factors are vegetation cover (particularly forest cover), snow morphology (crystal size), and errors related to brightness temperature calibration. A time-evolving retrieval algorithm that considers the evolution of snow crystals is formulated. An error model is developed based on the standard error estimation theory. This new algorithm and error estimation method is applied to the passive microwave data from Special Sensor Microwave/Imager (SSM/I) during the 1990-1991 snow season to produce annotated error maps for North America. The algorithm has been validated for seven snow seasons (from 1988 to 1995) in taiga, tundra, alpine, prairie, and maritime regions of Canada using in situ SWE data from the Meteorological Service of Canada (MSC) and satellite passive microwave observations. An ongoing study is applying this methodology to passive microwave measurements from Scanning Multichannel Microwave Radiometer (SMMR); future study will further refine and extend the analysis globally, and produce an improved SWE dataset of more than 25 years in length by combining SSMR and SSM/I measurements.     

无线多媒体传感器网络的QoS问题研究

无线多媒体传感器网络（WMSNs）是在传统无线传感器网络（WSNs）基础上发展起来的具有音频、视频、图像等多媒体信息感知功能的新型传感器网络．具有广阔的发展前景。WMSNs感知媒体丰富、数据量大和处理任务复杂等显著特点,使其QoS问题的研究极具挑战性。该文分析了WMSNs的QoS需求,并从MAC层和网络层讨论了这一领域的国内外研究现状。最后对QoS亟待解决的问题作了总结。     

A comparison of SNOTEL and AMSR-E snow water equivalent data sets in western US watersheds

It is a consensus among earth scientists that climate change will result in an increased frequency of extreme events (e.g. floods, droughts). Streamflow forecasts and flood/drought analyses, given this high variability in the climatic driver (snowpack), are vital in the western USA. However, the ability to produce accurate forecasts and analyses is dependent upon the quality of these predictors. Run-off and stream volume analysis in the region is currently based upon in situ telemetry snow data products. Recent satellite deployments offer an alternative data source of regional snowpack. The proposed research investigates and compares remotely sensed snow water equivalent (SWE) data sets in western US watersheds in which snowpack is the primary driver of streamflow. Watersheds investigated include the North Platte, Upper Green and Upper Colorado. SWE data sets incorporated are in situ snowpack telemetry (SNOTEL) sites and the advanced microwave scanning radiometer-earth observing system (AMSR-E) aboard NASA's Aqua satellite. The time period analysed is 2003-2008, coincident with the deployment of the NASA Aqua satellite. Bivariate techniques between data sets are performed to provide valuable information on the time series of the snow products. Multivariate techniques including principal component analysis (PCA) and singular value decomposition (SVD) are also applied to determine similarities and differences between the data sets and investigate regional snowpack behaviours. Given the challenges (including costs, operation and maintenance) of deploying SNOTEL stations, the objective of the research is to determine whether remotely sensed SWE data provide a comparable option to in situ data sets. Correlation analysis resulted in only 11 of the 84 SNOTEL sites investigated being significant at 90% or greater with a corresponding AMSR-E cell. Agreement between SWE products was found to increase in lower elevation areas and later in the snowpack season. Two distinct snow regions were found to behave similarly between both data sets using a rotated PCA approach. Additionally, SVD linked both data products with streamflow in the region and found similar behaviour among data sets. However, when comparing SNOTEL data with the corresponding satellite cell, there was a consistent bias in the absolute magnitude (SWE) of the data sets. The streamflow forecasting results conclude regions that have few (or zero) land-based weather stations can incorporate the AMSR-E SWE product into a streamflow forecast model and obtain accurate values.