基于时空二向度定型机制的静态网络检测算法研究
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摘要
为了解决当前静态网络检测机制中存在链路抖动检出频率低,数据传输性能不高,且难以实现信道噪声擦除的不足,提出了一种基于时空二向度定型机制的静态网络检测算法。首先,综合考虑静态网络接收信号强度(Received Signal Strength Indication,RSSI)、链路质量指针(Link Quality Indicator,LQI)、信噪比(SIGNAL-NOISE RATIO,SNR)的特点,并结合静态网络节点的拓扑特性,通过移序方法,构建了基于频率漂移包络擦除方案的超参数捕捉方法,有效降低信道噪声对网络抖动性能的影响;随后,考虑到当前静态网络节点的数据流量分布不均匀的特性,基于链路相干协方差的方法,实现链路抖动二向度的误差消除,显著提升了网络抗噪性能,强化了数据传输过程中的误差控制。仿真实验表明,与高斯白噪声信道包络检测机制(Envelope Monitoring Mechanism in GaussWhiteNoiseChannel,E2W-GWNC机制)、拉普拉斯信源联合抖动检测机制(LaplasseSourceJointJitter Detection Mechanism,LS-2JD机制)相比,所提算法具有更高的网络数据传输能力,且有更好的链路抖动问题检出效果。
In order to overcome the shortcomings, such as low detection frequency of link jitter, low performance of data transmission and difficulty to eliminate the channel noise in current static network monitoring mechanism,a static network detection algorithm based on spatiotemporal two-dimensional shaping mechanism was proposed in this paper. Firstly, considering the characteristics of received signal strength, link quality pointer and signal-to-noise ratio in static networks, as well as combining with the topological characteristics of static network nodes, the hyperparametric capture method based on frequency drift envelope erasure scheme was constructed by shifting order mechanism, which can effectively reduce the influence of channel noise on network jitter performance. Then, considering the uneven distribution of data traffic in current static network nodes, the error elimination of link jitter dimension based on covariance of link coherence was realized to improve the anti-noise performance of the network and it strengthens the error control in the process of data transmission. Simulation experiments show that compared with the envelope monitoring mechanism in gauss white noise channel and joint laplacian source jitter detection mechanism, this algorithm can achieve higher capacity of the network data transmission and the better detection effect of link jitter.
In order to overcome the shortcomings, such as low detection frequency of link jitter, low performance of data transmission and difficulty to eliminate the channel noise in current static network monitoring mechanism,a static network detection algorithm based on spatiotemporal two-dimensional shaping mechanism was proposed in this paper. Firstly, considering the characteristics of received signal strength, link quality pointer and signal-to-noise ratio in static networks, as well as combining with the topological characteristics of static network nodes, the hyperparametric capture method based on frequency drift envelope erasure scheme was constructed by shifting order mechanism, which can effectively reduce the influence of channel noise on network jitter performance. Then, considering the uneven distribution of data traffic in current static network nodes, the error elimination of link jitter dimension based on covariance of link coherence was realized to improve the anti-noise performance of the network and it strengthens the error control in the process of data transmission. Simulation experiments show that compared with the envelope monitoring mechanism in gauss white noise channel and joint laplacian source jitter detection mechanism, this algorithm can achieve higher capacity of the network data transmission and the better detection effect of link jitter.
引文
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[2]Hung S M,Givigy S N.A Q-learning Approach to Floc King with UAVs in a Stochastic Environment[J].IEEE Tra-nsactions on Cybernetics,2016,47(1):186-197.
[3]Yan J,He H,Zhong X.Q-learning-Based Vulnerability anAlysis of Smart Grid Against Sequential Topology Attacks[J].IEEE Transactions on Information Forensics&Security,2017,12(1):200-210.
[4]Hu H,Zhang H,Liu Y.Quantitative Method for Network Security Situation Based on Attack Prediction[J].Security&Communication Networks,2017(4):1-19.
[5]Niu B A.Cooperation Stimulation Strategy in Clouds Networks[J].Signal Processing,IEEE Transactions on,2017,59(5):2355-2369.
[6]Liu J,Zhang H Q,Liu Y.Research on Optimal Selection of Moving Target Defense Policy Based on Dynamic Game with Incomplete Information[J].Acta Electronica Sinica,2018,46(1):82-89.
[7]陈艳,王子健,赵泽.传感器网络环境检测时间序列数据的高斯过程建模与多步预测[J].通信学报,2015,36(10):252-262.
[8]Weng L N,Zhang P,Feng Z Y.Short-Term Link Quality Prediction Using non Parametric Time Series Analysis[J].Science China Information Sciences,2015,58(8):1-15.
[9]舒坚,汤津,刘琳岚.基于模糊支持向量回归机的WSNs链路质量预测[J].计算机研究与发展,2015,52(8):1842-1851.
[10]李振刚.基于高斯过程回归的网络流量预测模型[J].计算机应用,2014,34(5):1251-1254.
[11]李军,张友鹏.基于高斯过程的混沌时间序列单步与多步预测[J].物理学报,2011,60(7):143-152.
[12]Prachi S.Intelligent Query-Based Data Aggregation Model and Optimized Query Ordering for Efficient Wireless Sensor Network[J].Wireless Personal Communications,2018,100(4):1405-1425.
[13]Shu J,liu S.Link Quality Prediction for WSNs Based on Dynamic Bayesian Networks[J].Advanced Engineering Sciences,2017,49(2):152-159.