无线传感器节点节能方法研究

通过分析节点传感器的数据与时间的相关性,采用节点传感器数据预测模型描述节点传感器数据的规律,并建立基于该预测模型的节点数据发送机制,从而减少节点数据发送次数以及节点间的数据通信量,降低节点能耗。实验结果表明,在应用此预测模型后,节点电池的工作寿命能够延长4%左右。     

一种面向监测区域的链路质量和覆盖保证的节点调度算法

为了降低监测区域能耗总开销和减少网络传输时延,保证监测区域网络链路质量、实现网络的全面覆盖和延长网络生命周期,设计了一种基于扫描线和节点自适应调整苏醒时隙的节点调度方案;首先,定义了系统模型即网络假设和调度目标;然后判断网络是否实现当完全覆盖,当不能全面覆盖时,通过调整部分节点的感知半径来实现网络的全面覆盖;当链路质量过差导致传输延迟过大时,通过设计从源节点到目标节点的增加节点苏醒时隙,并根据节点的剩余能量和传输延迟阈值来减少数据传输次数以降低传输延迟;在NS2环境下进行实验,结果表明:文中方法能有效地实现传感器网络监测区域的全面覆盖,降低网络的传输延迟和提高网络的生命周期,与其他节点调度相比,具有很强的优越性和实用性.     

适用于水声传感网络的多链路传输MAC协议

针对水声传感网络存在的高时延、低信道利用率等问题,提出一种新的多链路传输介质访问控制协议。该协议节点通过RTS/CTS握手协议交互时延信息和传输计划,实现多条链路传输数据。在汇聚节点接收多个节点的数据时,汇聚节点根据节点的时延和接收的数据帧大小规划节点之间的发送顺序,避免传输冲突造成的能量损失和低信道利用率,同时为得到最大网络吞吐量,给出最优退避窗口值的理论表达式。仿真结果表明,与RC-FAMA、S-FAMA等协议相比,该协议能有效提高水声传感网络的吞吐量,降低传输冲突,从而延长水下节点的工作时间。     

基于信任机制的无线传感器网络可靠传输路由协议

在无线传感器网络中,针对异常事件监测时会出现大量数据发送引起网络拥塞、数据包无法可靠传输的问题,提出一种基于信任机制的可靠传输路由协议（RTRPT）。该协议提出了梯度分簇模型以选取簇头节点。在无异常事件发生时,通过转发能量阈值找出传输数据的邻居节点,进行数据传输。同时对这些节点进行转发概率与历史信任度计算得到直接信任度,再通过这些节点交互间接评价得到间接信任度。将直接信任度、间接信任度、距离度量和剩余能量作为评价指标,量化后通过熵权法得到邻居节点的综合信任。在异常事件发生时,节点仅需选择综合信任度最大的邻居节点作为转发节点,建立路由路径。使用OMNeT++进行仿真验证,RTRPT与TSRP、ESRT相比,在传输大量数据包时节点丢包率分别降低69.4%、52.7%;节点传输延迟分别降低53.6%、34.8%;网络生命周期分别延长32.5%、15.7%。仿真结果表明,RTRPT能有效减少丢包、降低传输延迟、延长生命周期。     

无线局域网中TXOP动态分配方法

为解决802.11e网络的公平性问题,提出一种TXOP动态分配方法,在节点传输前,根据碰撞次数估计碰撞程度,根据估计得到的网络负载情况调整节点的TXOP,使不同优先级、不同传输速率的业务获得相应的TXOP值。理论分析和仿真结果表明,该方法在采用不同发送速率的业务间,能保证吞吐量的公平性,并减少分组重传次数,降低分组传输平均时延。     

Ad Hoc网络中的动态反馈功率控制方法

受移动节点能量的限制,传统Ad Hoc网络使用最大的固定功率传送数据,从而严重影响节点及网络的生存时间。针对该问题,通过观测接收节点信干比的线性变化情况,计算发送节点所需的发送功率,发送节点以此为依据动态地调整传输功率。仿真结果表明,该方法能减少节点的能量损失,延长网络生存时间。     

一种应用随机线性网络编码的无线广播重传方法

无线网络广播重传处理中,多个接收节点中的任意一个节点的丢包都要求源节点重传数据包,需要广播发送较多的重传次数.本文将随机线性网络编码技术应用在无线网络广播重传中,提出一种新颖的广播重传方法(RLNCBR).该方法中,源节点记录多个接收节点中丢包最多的接收节点丢包数,再按照随机线性网络编码的方法编码组合该丢包数个线性编码包;源节点广播重传;接收节点采用运算编码线性组合的方法获得信息包数据.数学分析表明,该方法能保证所有接收节点的编码可解性,同时重传次数可达到理论最优性;模拟测试结果表明:与传统重传方法相比,RLNCBR有效地减少了信息包的平均传输次数,提高了传输效率.     

基于线性回归的无线传感器网络分布式数据采集优化策略

事件监测是无线传感器网络中最重要的应用之一,部署在监测区域内的传感器节点通过对感知数据信息的采集、处理和传输等基本操作完成具体的监测任务,在各种操作中,节点之间的数据传输是最消耗能量的.为了减少节点之间的通信数据量,达到降低网络能耗和延长网络生命周期的目的,该文提出了一种能量高效的基于线性回归的无线传感器网络分布式数据采集优化策略,通过应用线性回归分析方法构建感知数据模型,保持感知数据的特征,使节点仅传输回归模型的参数信息,代替传输实际监测的感知数据信息.仿真实验结果表明,文中提出的数据采集优化策略能通过较小的通信量有效地实现事件监测区域感知数据的预测和估计,降低网络的总能量消耗,延长网络的生命周期.     

WSN中能量有效的多簇头层次型路由协议

为改善无线传感器网络中某些节点因能耗太大而过早失效的情况,提出了一种基于分簇的多簇头能量有效算法EHMVA(energy-efficient hierarchical multiple vice-cluster-head algorithm)。在簇头选择机制中引入邻节点距离的概念,采用多副簇头选举分担主簇头能耗的机制,通过发现联结节点和引入误差发送机制来减少数据发送,并通过延长稳定的簇通信时间来降低簇重建的频率。仿真实验表明,该算法有效地平衡了节点间的能量消耗,显著地延长了网络的存活时间。     

机会网络中的自适应转发控制算法*

在分析现有机会网络转发控制策略的基础上,针对采用固定效用值阈值的机会网络转发控制,提出了一种基于节点能力状况的自适应转发控制策略（adaptive forwarding algorithm based on nodal capacity condition,AFNC）。该策略根据节点的能力状况计算阈值控制因子,自适应调整不同网络传输状况以及通信机会下的数据转发条件。仿真实验表明,与现有的几种典型转发控制相比,AFNC在保证较低传输延迟和较高传输成功率的基础上,通过减少节点间的转发次数,有效地降低了网络传输开销。     

Retrieval of daily evolution of soil moisture from satellite-derived land surface temperature and net surface shortwave radiation

Soil moisture is a key parameter in water balance, and it serves as the core and link in atmosphere–vegetation–soil–groundwater systems. Soil moisture directly affects the accuracy of the simulation and prediction conducted by hydrological and atmospheric models. This article aims to develop a new model to retrieve the daily evolution of soil moisture with time series of land surface temperature (LST) and net surface shortwave radiation (NSSR). First, for the time series of soil moisture, LST and NSSR daytime data were simulated by the common land model (CoLM) with different soil types in bare soil areas. Based on these data, the variations between soil moisture and LST-NSSR during the daytime with different soil types were analysed, and a plane function was used to fit the daily evolution of soil moisture and the time series of LST and NSSR data. Further study proved that the coefficients of the soil moisture retrieval model are not sensitive to soil type. Then, a relationship model between the daily evolution of soil moisture and the time series of LST-NSSR was developed and validated using the data simulated by CoLM with different soil types and different atmospheric conditions. To demonstrate the feasibility of the soil moisture retrieval method proposed in this study, it was applied to the African continent with data from the METEOSAT Second Generation Spinning Enhanced Visible and Infrared Imager (MSG–SEVIRI) geostationary satellite. The results show that the variation of soil moisture content can be quantitatively estimated directly by the method at the regional scale with some reasonable assumptions. This study can provide a new method for monitoring the variation of soil moisture, and it also indicates a new direction for deriving the daily variation of soil moisture using the information from the time series of the land surface variables.     

基于TDR-3的土壤水分传感器标定模型研究

无线传感器网络为土壤水分实时采集提供了经济、方便的途径,对分析土壤含水量时空变异和作物生长气候变化意义重大.将无线传感器网络与TDR-3土壤水分传感器结合实现土壤水分实时监测.为了克服TDR-3土壤水分传感器的非线性缺陷,提出利用最小二乘法对土壤水分曲线进行分段线性标定的方法,并采用相关性系数进行精度验证.实验结果表明,分段线性法所建立模型的精确度较高,而且标定模型简单实用、可行.通过标定模型的研究,使得土壤水分的无线传感器网络能够较精确地进行土壤水分的实时监测.     

Pixel-scalesoil Moisture Monitoring Network and Its Preliminary Validation of L-band Soil Moisture Products

Soil moisture is a key variable in the process of crop growth,ground-air water heat exchange and global water cycle,which plays an important role in drought monitoring,hydrological land surface processes and climate change.Passive microwave remote sensing has become the main means of monitoring soil moisture with the sensitivity to soil moisture.In this study,the authenticity test of SMAP（Soil Moisture and Active and Passive） and SMOS（Soil Moisture and Ocean Salinity）passive microwave soil moisture products using the soil moisture sensor network monitoring data carried out against the underlying surface of farmlands in Jilin Province was carried out.The following conclusions were obtained：（1）Compared with the in situ measured data,SMOS L3(ascending and descending overpasses) and SMAP L3 passive microwave soil moisture products generally underestimated the ground data,but With the occurrence of rainfall events,there will be the phenomenon which is the value of soil moisture products is higher than the in situ data; although the unbiased root mean square error (unRMSE) of the two soil moisture products was greater than 0.07 m3/m3,the unRMSE of SMAP passive microwave soil moisture product data which was 0.078 m3/m3 was slightly lower;（2）Since the depth of induction of the L-band is lighter than the depth of detection of the sensor(5cm),and the dryness of the soil surface after rainfall causes the vertical inhomogeneity of soil moisture,which is one of the reasons why SMOS and SMAP passive microwave soil moisture products underestimate soil moisture; （3）SMOS has a higher value than the range of SMAP brightness temperature,which may be caused by radio frequency interference (RFI),which makes the error of soil moisture Retrieval and affects the validation accuracy.The comparison of bright temperature distribution of SMOS and SMAP shows that the effect of RFI on SMOS is more serious due to the influence of electromagnetic radio frequency interference (RFI),which may be the reason why the RMSE of soil moisture product of SMOS is higher than that of passive microwave soil moisture product of SMAP.     

Soil moisture mapping based on ASAR/ENVISAT radar data over a Sahelian region

The analysis of feedbacks between continental surfaces and the atmosphere is one of the key factors to understanding African Monsoon dynamics. For this reason, the monitoring of surface parameters, in particular soil moisture, is very important. Satellite remote sensing appears to be the most suitable means of obtaining data relevant to such parameters. The present paper presents a methodology applied to the mapping and monitoring of surface soil moisture over the Kori Dantiandou region in Niger, using data provided by the ASAR/ENVISAT radar instrument. The study is based on 15 sets of ASAR/ENVISAT C‐band radar data, acquired during the 2004 and 2005 rainy seasons. Simultaneously with radar acquisitions, ground soil moisture measurements were carried out in a large number of test fields. Soil moisture was estimated only for fields with bare soil or low‐density vegetation, using low‐incidence‐angle radar data (IS1 configuration). A mask was developed, using SPOT/HRV data and DTM, for use over areas characterized by high‐density vegetation cover, pools, and areas with high slopes. Soil moisture estimations are based on horizontal‐ and vertical‐polarization radar data. In order to double the temporal frequency of soil moisture estimations, IS2 data were used with IS1 data, with all data normalized to a single incidence angle. A high correlation is observed between in situ measurements and processed radar data. An empirical inversion technique is proposed, to estimate surface soil moisture from dual‐polarization data with a spatial resolution of approximately 1 km. Surface soil moisture maps are presented for all the studied sites, at various dates in 2004 and 2005. Of particular interest, these maps reveal convective precipitation scales associated with strong spatial variations in surface soil moisture.     

Mapping near-surface soil moisture on regional scale using ERS-2 SAR data

Soil moisture is an important hydrologic variable of great consequence in both natural and agricultural ecosystems. Unfortunately, it is virtually impossible to accurately assess the spatial and temporal variability of surface soil moisture using conventional, point measurement techniques. Remote sensing has the potential to provide areal estimates of soil moisture at a variety of spatial scales. This investigation evaluates the use of European Remote Sensing Satellite (ERS-2) C-band, VV polarization, synthetic aperture radar (SAR) data for regional estimates of surface soil moisture. Radar data were acquired for three contiguous ERS-2 scenes in the Southern Great Plains (SGP) region of central Oklahoma from June 1999 to October 2000. Twelve test sites (each approximately 800?m×800?m) were sampled during the ERS-2 satellite overpasses in order to monitor changes in soil moisture and vegetation on the ground. An average radar backscattering coefficient was calculated for each test site. Landsat-5 and -7 Thematic Mapper (TM) scenes of the experimental sites close in time to the ERS-2 acquisition dates were also analysed. The TM scenes were used to monitor land cover changes and to calculate the Normalized Difference Vegetation Index (NDVI). Land cover and ground data were used to interpret the radar-derived soil moisture data. Linear relationships between soil moisture and the backscattering coefficient were established. Using these equations, soil moisture maps of the Little Washita and the El Reno test areas were produced.     

卷积神经网络提取风云影像土壤湿度

目的 时空分辨率较高的土壤湿度数据对于生产实践和科学研究具有重要意义。以国产的风云气象卫星为数据源,利用卷积神经网络自主学习输入变量间深层关联的优势,获取高质量土壤湿度数据,为科学研究和生产实践服务。方法 首先构建了一个土壤湿度提取卷积神经网络（soil moisture convolutional neural network,SMCNN）,SMCNN由温度子网络和土壤湿度子网络构成,每个子网络均包含特征提取器和编码器。特征提取器用于为每个像素生成一个特征向量,其中温度子网络的特征提取器由11个卷积层组成,湿度子网络的特征提取器由9个卷积层组成,卷积层均使用1×1的卷积核。编码器用于将提取到的特征拟合为目标变量。两个子网络均使用平均方差作为损失函数。使用随机梯度下降算法对模型进行训练,最后利用训练好的模型提取区域土壤湿度数据。结果 选择宁夏回族自治区为实验区,利用获取的2016-2019年风云3D影像和相应地面站点数据作为实验数据,选择线性回归模型、BP（back propagation）神经网络模型作为对比模型开展数据实验,选择均方根误差作为评价指标。实验结果表明,SMCNN的均方根误差为0.006 7,优于对比模型,SMCNN模型在从风云影像中提取土壤湿度方面具有优势。结论 本文利用卷积神经网络分别构建用于反演地表温度和土壤湿度的子网络,再组成一个完整的土壤湿度反演网络结构,从风云3D数据中获取数值精度、时空分辨率均较高的土壤湿度数据,满足了科学研究和生产实践对大范围高精度土壤湿度数据的需求。     

一种农田RPL墒情监测网络传输调度的优化算法

在RPL(routing protocol for LLN)协议应用于农田墒情监测传感网络的过程中,保证网络中的节点能够长期有效工作成为关键问题.为此,设计了一种基于间隔更新思想的控制算法IUTA,对在一致性和不一致性事件发生时的处理机制进行了改进.在网络中节点复杂度变化的情况下,通过Cooja实验仿真模拟了网络中节点传递数据节省的功耗以及成功率两者动态变化的过程.结果表明,IUTA有效降低网络中节点传输功耗,同时对数据传输成功率影响甚微.     

重庆市墒情分析评价预测系统的设计与实现

结合重庆市墒情、水雨情等自动监测系统,考虑主要作物种类、分布区域、播种面积、耕作制度、生育期间各生长发育指标,以及不同区域、深度的田间持水量,对已建立土壤墒情监测点的地区,采用土壤相对湿度评估农业墒情;对于尚未建立墒情监测站但已建立雨量监测站点的雨养农业区,采用降水量距平法或连续无雨日数法,进行墒情分析评价,用衰减系数法预测墒情的变化趋势。采用B/S开发模式,利用Flex通过天地图在线服务进行地图显示,采取IIS发布模式,基于Web Services的数据服务模式,设计一套基于Web GIS的墒情监测分析评价预测系统,通过相关评价指标反映农林作物土壤的干旱情况,并能结合天气情况预测未来墒情数据,为安排农业用水提供技术支撑,减少干旱灾害损失。     

微波遥感监测土壤水分的研究初探

在GPS定位的基础上，同步测量土攘水分、土壤后向散射系数，和同步获取的X波段、HH机化SAR图像进行了土攘水分监N.]的徽波遥感试验研究。结果表明，X波段SAR图像的灰度与表层土壤(0~10cm)水分有较好的相关性，35OHH极化的土峨后向散射系数与SAR图像灰度和土攘水分也有较好的相关性，由SAR图像及土攘的后向散射系数估算的土峨水分精度相近，相对误差均为12%左右，因而利用X波段、HH极化的机载SAR图像监浏土壤水分是可行的。雷达图像的穿透力一般在10cm以内，因此探讨了由表层土壤水分推求剖面土壤水分的可能性，并提出以土攘水分计法在浏童精度和速度上改进传统土壤水分测量的方法。     

基于IPv6的无线高原冻土监测系统

设计了一种新型的高原冻土监测系统。采用ARM7内核的MCU进行控制,并基于IPv6协议利用IPv6技术和GPRS技术相结合的方式,建立了可自组网的无线传感器网络,实现了对指定区域环境参数的实时监测。系统使用了多个土壤水分传感器、温度传感器、二氧化碳传感器、氮气传感器对高原冻土环境进行全方位实时观测。各监测点将数据通过基于IPv6的无线网络传送给汇聚节点,汇聚节点再通过GPRS网络将数据传送给监控中心。监控中心可以对数据进行记录和分析,更好地指导环境保护和建设工作。