基于大数据的传感网络链路稳定性检测系统设计
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摘要
传统检测系统在对链路检测过程中,经常受到移动环境的影响,导致检测结果的完整性以及准确率较低,为解决这一问题,提出并设计了基于大数据的传感网络链路稳定性检测系统。对传感网路传输端、数据中心服务器以及检测端三部分进行具体分析,完成系统硬件设计。为尽量减小移动环境的影响,在软件设计部分,采用直接链路和间接链路稳定性相结合的检测方法,共同完成基于大数据的传感网络链路稳定性检测系统设计。实验结果表明,该系统检测结果的完整性较高,且检测准确率平均值可高达93.5%左右,验证了该系统的优越性能。
The traditional detection system is often affected by the mobile environment in the process of link detection,which leads to the low integrity and accuracy of the detection results. Therefore,a sensor network link stability detection system based on big data is proposed and designed. The three parts of the sensor network transmission terminal,data center server and detection terminal are analyzed in detail,so as to complete the hardware design of the system. In the software design part,the detection method of combining the direct link stability with indirect link stability is adopted to complete the design of the sensor network link stability detection system based on big data,so as to minimize the influence of the mobile environment. The experi-mental results show that the system has detection results with high integrity,and the average value of the detection accuracy rates can be up to 93.5%,which verified the superior performance of the system.
The traditional detection system is often affected by the mobile environment in the process of link detection,which leads to the low integrity and accuracy of the detection results. Therefore,a sensor network link stability detection system based on big data is proposed and designed. The three parts of the sensor network transmission terminal,data center server and detection terminal are analyzed in detail,so as to complete the hardware design of the system. In the software design part,the detection method of combining the direct link stability with indirect link stability is adopted to complete the design of the sensor network link stability detection system based on big data,so as to minimize the influence of the mobile environment. The experi-mental results show that the system has detection results with high integrity,and the average value of the detection accuracy rates can be up to 93.5%,which verified the superior performance of the system.
引文
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[3]韩哲,张霞,李鸥,等.面向有损链路的传感网压缩感知数据收集算法[J].软件学报,2017,28(12):3257-3273.HAN Zhe,ZHANG Xia,LI Ou,et al.Data gathering algorithm based on compressive sensing under lossy WSN[J].Journal of software,2017,28(12):3257-3273.
[4]徐艳玲.事件驱动型无线传感网链路质量监控研究[J].电子商务,2015(4):49-50.125XU Yanling.Research on link quality monitoring in event-driven wireless sensor networks[J].E-business journal,2015(4):49-50.
[5]李君伟,李士宁,张羽.具有局部特性的传感网链路质量估计方法[J].计算机科学,2015,42(12):195-200.LI Junwei,LI Shining,ZHANG Yu.Network link quality estimation with local characteristics in WSNs[J].Computer science,2015,42(12):195-200.
[6]胡刚,刘琳岚.无线传感网络链路质量参数研究[J].信息通信,2015(4):30-31.HU Gang,LIU Linlan.Research on link quality parameters of wireless sensor network[J].Information&communications,2015(4):30-31.
[7]杨东芳,李娜.无线传感网随机空置和链路能量联合优化路由[J].计算机工程与设计,2017,38(6):1464-1467.YANG Dongfang,LI Na.Wireless sensor network route based on random vacancy and link energy joint optimization[J].Computer engineering and design,2017,38(6):1464-1467.
[8]张铮,曹守启,李军涛,等.基于能量与链路稳定性感知的按需路由协议[J].半导体光电,2017,38(5):714-718.ZHANG Zheng,CAO Shouqi,LI Juntao,et al.An energy and link-stability aware on-demand routing protocol[J].Semiconductor optoelectronics,2017,38(5):714-718.
[9]戴春妮.基于聚类特征自适应机制的无线传感网异常节点检测算法[J].微电子学与计算机,2017,34(4):76-82.DAI Chunni.Anomaly node searching algorithm of wireless sensor networks based on adaptive mechanism of clustering feature[J].Microelectronics&computer,2017,34(4):76-82.
[10]陆俊,朱炎平,徐志强,等.面向输电线路监测的无线传感网络可靠路由方法研究[J].电网技术,2017,41(2):644-650.LU Jun,ZHU Yanping,XU Zhiqiang,et al.A reliable wireless sensor network routing method for power transmission line monitoring[J].Power system technology,2017,41(2):644-650.