针对农田数据采集系统有线传输方式常受到地形、安装环境等限制问题,提出了一种基于ZigBee数据采集传输系统。采用CC2530芯片为主搭建无线传感器网络,终端和协调器采用半开源的Z-Stack协议栈进行程序开发,上位机基于Visio Studio 2015...针对农田数据采集系统有线传输方式常受到地形、安装环境等限制问题,提出了一种基于ZigBee数据采集传输系统。采用CC2530芯片为主搭建无线传感器网络,终端和协调器采用半开源的Z-Stack协议栈进行程序开发,上位机基于Visio Studio 2015平台采用C#语言进行软件开发,并配合ACCESS数据库,共同实现了系统的远程检测与本地保存和动态显示。在玉米试验田进行实地测试,终端按采集需要暂时设定为每间隔1 h采集一次数据,其余时间处于休眠状态。经计算,终端预计工作时间为100 d左右;且经过测试,终端与协调器的有效传输距离能达到80 m,数据传输有效率达92%。结果表明:该系统具有低功耗、低成本、系统运行稳定、可扩展性强等特点,满足农业信息化的需求。展开更多
在WSN(Wireless Senor Network,无线传感器网络)中的分级路由算法中,如果簇头仅仅能够进行单跳通信或者多跳通信,都会造成网络负载不均衡以及簇头能量消耗过快的问题出现。针对这一问题,文章提出了一种改进的基于LEACH-C(Low Energy Ada...在WSN(Wireless Senor Network,无线传感器网络)中的分级路由算法中,如果簇头仅仅能够进行单跳通信或者多跳通信,都会造成网络负载不均衡以及簇头能量消耗过快的问题出现。针对这一问题,文章提出了一种改进的基于LEACH-C(Low Energy Adaptive Clustering Hierarchy Centralized,低功耗自适应集中分层型)算法的簇间路由(Cluster Routing based on LEACH-C Algorithm,简称CRLA)算法。该算法通过距离阀值来控制簇头是进行单挑通信还是多跳通信。仿真分析表明,CRLA算法能够实现网络负载的均衡以及减少簇头能量的消耗,从而实现网络生存时间的延长。展开更多
An Adaptive Measurement Scheme (AMS) is investigated with Compressed Sensing (CS) theory in Cognitive Wireless Sensor Network (C-WSN). Local sensing information is collected via energy detection with Analog-to-Informa...An Adaptive Measurement Scheme (AMS) is investigated with Compressed Sensing (CS) theory in Cognitive Wireless Sensor Network (C-WSN). Local sensing information is collected via energy detection with Analog-to-Information Converter (AIC) at massive cognitive sensors, and sparse representation is considered with the exploration of spatial temporal correlation structure of detected signals. Adaptive measurement matrix is designed in AMS, which is based on maximum energy subset selection. Energy subset is calculated with sparse transformation of sensing information, and maximum energy subset is selected as the row vector of adaptive measurement matrix. In addition, the measurement matrix is constructed by orthogonalization of those selected row vectors, which also satisfies the Restricted Isometry Property (RIP) in CS theory. Orthogonal Matching Pursuit (OMP) reconstruction algorithm is implemented at sink node to recover original information. Simulation results are performed with the comparison of Random Measurement Scheme (RMS). It is revealed that, signal reconstruction effect based on AMS is superior to conventional RMS Gaussian measurement. Moreover, AMS has better detection performance than RMS at lower compression rate region, and it is suitable for large-scale C-WSN wideband spectrum sensing.展开更多
文摘在WSN(Wireless Senor Network,无线传感器网络)中的分级路由算法中,如果簇头仅仅能够进行单跳通信或者多跳通信,都会造成网络负载不均衡以及簇头能量消耗过快的问题出现。针对这一问题,文章提出了一种改进的基于LEACH-C(Low Energy Adaptive Clustering Hierarchy Centralized,低功耗自适应集中分层型)算法的簇间路由(Cluster Routing based on LEACH-C Algorithm,简称CRLA)算法。该算法通过距离阀值来控制簇头是进行单挑通信还是多跳通信。仿真分析表明,CRLA算法能够实现网络负载的均衡以及减少簇头能量的消耗,从而实现网络生存时间的延长。
基金Supported by the National Natural Science Foundation of China (No. 61102066, 60972058)the China Postdoctoral Science Foundation (No. 2012M511365)the Scientific Research Project of Zhejiang Provincial Education Department (No. Y201119890)
文摘An Adaptive Measurement Scheme (AMS) is investigated with Compressed Sensing (CS) theory in Cognitive Wireless Sensor Network (C-WSN). Local sensing information is collected via energy detection with Analog-to-Information Converter (AIC) at massive cognitive sensors, and sparse representation is considered with the exploration of spatial temporal correlation structure of detected signals. Adaptive measurement matrix is designed in AMS, which is based on maximum energy subset selection. Energy subset is calculated with sparse transformation of sensing information, and maximum energy subset is selected as the row vector of adaptive measurement matrix. In addition, the measurement matrix is constructed by orthogonalization of those selected row vectors, which also satisfies the Restricted Isometry Property (RIP) in CS theory. Orthogonal Matching Pursuit (OMP) reconstruction algorithm is implemented at sink node to recover original information. Simulation results are performed with the comparison of Random Measurement Scheme (RMS). It is revealed that, signal reconstruction effect based on AMS is superior to conventional RMS Gaussian measurement. Moreover, AMS has better detection performance than RMS at lower compression rate region, and it is suitable for large-scale C-WSN wideband spectrum sensing.
