• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    LDM-Satellite: A New Scheme for Packet Loss Classification over LEO Satellite Network

    2022-12-09 09:50:22NingLiQiaodiZhuZhongliangDeng
    China Communications 2022年12期

    Ning Li,Qiaodi Zhu,Zhongliang Deng

    School of Electronic Engineering,Beijing University of Posts and Telecommunications,Beijing 100876,China

    Abstract: The packet loss classification has always been a hot and difficult issue in TCP congestion control research.Compared with the terrestrial network,the probability of packet loss in LEO satellite network increases dramatically.What’s more,the problem of concept drifting is also more serious,which greatly affects the accuracy of the loss classification model.In this paper,we propose a new loss classification scheme based on concept drift detection and hybrid integration learning for LEO satellite networks,named LDM-Satellite,which consists of three modules:concept drift detection,lost packet cache and hybrid integration classification.As far,this is the first paper to consider the influence of concept drift on the loss classification model in satellite networks.We also innovatively use multiple base classifiers and a naive Bayes classifier as the final hybrid classifier.And a new weight algorithm for these classifiers is given.In ns-2 simulation,LDM-Satellite has a better AUC(0.9885)than the single-model machine learning classification algorithms.The accuracy of loss classification even exceeds 98%,higher than traditional TCP protocols.Moreover,compared with the existing protocols used for satellite networks,LDM-Satellite not only improves the throughput rate but also has good fairness.

    Keywords: LEO Satellite Networ;TCP congestion control;concept drift detection;ensemble learning;loss classification

    I.INTRODUCTION

    Satellite communication plays an irreplaceable role in the current and future communication systems [1].Among them,the LEO satellite has become a research hotspot in recent years due to its proximity to the ground and short propagation delay[2].However,the high link error rate in LEO satellite network results in an increasing probability of packet loss,which greatly affects the communication quality[3].

    There have been some methods for classifying the types of packet loss,wireless link errors or congestion.[4] used some network parameters,like RTTs,ROTTs,and IAT to form classification rules.Biaz[5],mBiaz[6],and Statistical Packet Loss Identification(SPLD) [7] selected the packet arrival time interval,Zigzig [8] and Spike [9] used one-way transmission time,and the Vegas predictor [10] chose The round trip time as parameter,then set a decision threshold.However,these methods have limitations because it is difficult to determine the decision threshold.In addition,only one feature cannot obtain high classification accuracy[11,12].Then,researchers proposed to combine multiple features and algorithms in data mining and machine learning.They made decision based on a driving model by learning the correlation between features.In [13],the machine learning classification method was first proposed.[14] used a Naive Bayes model to classify the loss types.However,the classification accuracy of these single model is greatly affected by the number of samples and features.[15]analyzed the classification accuracy of multiple machine learning models,the classification accuracy of the ensembl learning method is the highest.

    Although the ensemble classification model has the best performance,the classification accuracy is greatly affected by the concept drift [16,17].When concept drift occurs,the probability of packet loss increases,and along with the changing data flow distribution,the distribution of lost packets also changes,resulting in a reduction in the accuracy of the current classification model [18].If the occurrence of concept drift can be detected in advance and the classification model adjusted effectively,the cause of packet loss will be confirmed faster [19,20].Gama et al.[21]proposed a drift detection method (DDM) for binary classification.However,this method based on error rate cost high.EDDM [22] is an optimized version of DDM,which is improved based on the average distance and standard deviation between two consecutive errors.[23] proposed a frame detection concept drift(ADWIN)based on a sliding window that changes according to whether concept drift occurs [24].However,existing research based on concept drift only consider ground scenarios where the network environment is stable and the data distribution does not change dynamically.The data distribution in the LEO satellite network changes frequently and the concept drift is greater.

    We proposed a new packet loss classification scheme (LDM-Satellite) for LEO satellite networks.As we know,this is the first article to consider concept drift in the research of loss classification.Base on the distribution characteristics of the data flow in the network and the hybrid ensemble learning framework,with the goal of maximizing the difference of multiple base classifiers,we construct a new weight algorithm,by weighting multiple base classifiers,determining the type of packet loss.

    The rest of the paper is organized as follows: Section II introduces the overall framework of the proposed LDM-Satellite.Section III is the principle and specific implementation of each module.Section IV is the experimental results,including the performance comparison of LDM-Satellite classification accuracy and Goodput.Finally,Section V is summary.

    II.LDM-SATELLITE FRAMEWORK DESIGN

    The framework of LDM-Satellite is shown in Figure 1.It includes three parts: concept drift detection module,packet loss data cache module,and classifier integration module.

    Figure 1.LDM-Satellite architecture.

    Figure 2.D_ cache caches data samples in the most recent time T.

    The data stream first passes the concept drift detection module.If no concept drift occurs,hand over the lost data packet to the classifier in integration module to determine the type of loss,and then update the weight of the corresponding base classifier.If concept drift,it indicates that the network topology has changed,or the communication link has been switched,etc.,causing the distribution characteristics of the data flow changed.Therefore,we can directly update the base classification model without waiting until the accuracy of the ensemble classifier obtained,which greatly improve the classification efficiency of the model.In addition,for updating the model online,every time after classification,here we do not directly discard the collected lost packets,but first move them to the cache.It always remembers the information of lost packets from the current to the past T time.When concept drift,these samples in the cache are used to train new base classifier.The specific introductions of the three modules are in Section III.

    III.LDM-SATELLITE MODULE IMPLEMENTATION

    3.1 Concept Drift Detection Module

    Based on the dual threshold detection method,we proposes a corresponding window adjustment strategy in this paper,which makes the decision more timely and effective.

    After passing through the window,the data stream S is divided into continuous data blocks S={D1,D2,···Dt,Dt+1,···}.The sample in each data block isXt=where(i=1···n)represents n samples features.We use the unidirectional delay of the packet,the time interval between the last normally arrived packet and the first outof-order packet received,and the number of consecutive lost packet to be the features of the lost packet.ytis the class label of the sample at time t.After features are determined,dual thresholds are set according to the minimum classification error ratepminand the minimum standard deviationδmin.Based on these thresholds,we can detect the concept drift precisely.

    The size of sliding window is adjusted according to the detect result.However,if the window is too large,the time point where concept drift occurs may be missed.If the window is too small,the detection is inefficient.In view of the above problems,this paper proposes a segmented window adjustment strategy,which is different from the previous single adjustment method.This strategy sets more accurate and effective window adjustment methods for different judgment results.Details as follows:

    1.Whenpt+δt

    2.Whenpmin+αδmin

    3.Whenpt+δt >βpmin+δmin,concept drift has occurred.We need to compress window,so setwt+1=μwt.At this time,the data distribution in the network environment have changed,so the model is no longer applicable.There needs a new base classifier.

    Whereα,βis weighting factors and they are two constants,α<β.wtis the current window size andwt+1is the window size at the next moment.FurthermoreWbis the base window size.μrepresents the window change factor,Calculated as follows:

    Wherepmaxis the maximum value of classification error rate.

    3.2 Lost Data Cache Module

    When concept drift occurs,a new base classifier needs trained.Considering the time correlation between data samples,a data cache D_cache is set here.Following the ”first in first out” principle,we save the information of the lost packets.When concept drift detected,we use these samples to train the base classifier.

    If the packet lost and the current cache queue Q is not full,adding the information of this lost packet to the Q,as shown in Figure 2.Otherwise,in order to ensure the freshness of the data and the adaptation of the classification model,we removing information of old data before time T from the queue.If there is no packet lost,unnecessary information need not cached,so we directly discard it.The samples that cached in the D_ cache will be used for online training of the base classifier.

    3.3 Hybrid Integration Module of Classifier

    In this paper,we build a hybrid integration framework with multiple base classifiers and a naive Bayes classifier.Moreover,a weight algorithm of base classifier based on incremental learning is proposed.Finally,we combine the weighted classification result of base classifiers and naive Bayes classifier,which is used for online classifying the type of packet loss.The following is the explanation of this module in depth through the offline training of the base classifier and the online update based on concept drift detection.

    1.Offline training phase

    The hybrid integration framework that we proposed is shown in Figure 3,whereDirepresents the current i-th sample data block,Ciis the i-th base classifier,wiis the weight of this classifier,and K represents the number of classifiers that used for ensemble.If the current number of base classifiers is less than K,then useDito construct a new base classifierCiand assign corresponding weights to it.Otherwise,use the most recent K data blocks to construct a naive Bayes classifierC′.For the sample X inDi,in the naive Bayes classifier,the sample category is determined by the maximum posterior probability.The probability that sample X belongs to categoryymis:

    Figure 3.Hybrid integration framework.

    Where P(X)is constant,therefore,formula(2)can be simplified to:

    Where P(Ym)is the prior probability of categoryYm.In Naive Bayes,it is assumed that the features of the sample are independent of each other,so:

    WhereP(χi |Ym) is the conditional probability of the i-th featurexiwhen the sample belongs to theYmclass.According to formula (2) (3) (4),the decision equation of the naive Bayes classifier can be derived:

    The weight allocation algorithm based on incremental learning is as follows: first,the base classifierKiis trained with the data blockDi,and each training sample is given a weightαj,where j is the number of times the sample is processed.The mean square error of the available base classifierKion the sample setDiis:

    Where|Di|is the number of samples in the data setDi.Then assign weights to each base classifier based on the classification results:

    In order to prevent the denominator in Equation(7)from being 0,add a small enough positive valueε.Then update the weightαjof the samples in the data setDi.By increasing the weight of the wrongly classified samples,the base classifier will focus more on the wrongly classified samples.The sample weights are updated as follows:

    Finally,all samples in the data blocksDi?1,Di?2,···,D1,are used as training samples for the base classifierKi.After each training is completed,new weights are assigned to the samples according to Equation (8).The weight ofαjis based on the classification errorδiof the current sample in Equation (6),and the weightwiof each base classifier is updated according to Equation(7).At this point,we obtain the simple Bayesian and base classifiers that required for classification.

    2.Online classification stage

    When distinguishing the type of packet loss online,the classifier in the ensemble module is updated in real time based on the concept drift detection result and the online sample classification result.When a concept drift occurs,the new base classifier is trained,and the classifier with the worst replacement performance is selected based on the weight.Figure 4 is a block diagram of the online update of the classifier.

    Figure 4.Block diagram of online update of classification model.

    When detect concept drift,the data in the cache module is used to train a new base classifierand base on the above formula (6) (7) to set weight.Then update the base classifier queue at the same time.Finally integrate multiple base classifiers with weights and naive Bayes classifiers to get the final classifier:

    The former part of formula (9) is the result of the classifier,whereCiis the i-th base classifier currently ensemble,wiis its weight,through the function sign()and transformation,the final classification result is 1 or 0.That is,packet loss due to congestion or wireless error.The second part is the naive Bayes classifier,which not only eliminates the influence of noisy data,but also weights the classification results of the base classifier to improve the accuracy of packet loss classification.

    IV.EXPERIMENTAL RESULTS

    We use the simulation environment of Figure 5 to evaluate LDM-Satellite.In this scenario,in addition to multiple LEO satellites and inter-satellite links in the satellite network,there are multiple sending and receiving ends connected to the earth station.Each cable and satellite link uses HOL priority scheduling queues.We set the number of user connections N=20,the capacity of the satellite link c=1300 segments/s,corresponding to the TCP field of 1000 bytes,the value is close to 10Mb/s.The buffer size of the satellite uplink is 50 segments,the maximum congestion window size is maxCwnd=64 segments,and the buffer size of each receiving end is recCwnd=512 segments.The value of RTT is set to 50ms.The packet loss rate caused by link errors in the satellite link varies from 10?5to 10?1.

    Figure 5.Simulation scenario of the satellite network.

    Figure 6.Confusion matrix for packet loss classification.

    The training data set of this paper was obtained through the network simulator NS-2[25].In our study,collecting 35,441 lost data packets(22,426 are due to congestion).Figure 6 is the confusion matrix of LDMSatellite’s classification results on this data set,where 1 represents congestion(CL),0 represents wireless error(LE),dark blue represents the result of correct classification,and white represents the result of incorrect classification.

    Figure 7.Comparison of ROC curves of LDM-Satellite and other machine learning methods.

    According to the classification result of Figure 6,the precision rate Precision=0.984 and the recall rate Recall=0.987.In order to evaluate the classification performance better,based on the accuracy rate and recall rate,we calculate the f1_score,which is more than 0.985.Therefore,it is proved that the classification accuracy of LDM-Satellite is high.Moreover,we compare LDM-Satellite with several common classification algorithms such as LogisticRegression,DecisionTree,RandomForest,and GradientBoosting in Figure7.We can see from the ROC curve that LDMSatellite is significantly better than other classification algorithms.

    In order to compare their performance in more detail,we get Table 1.Two new TCP variant protocols,Veno and WestWood are added.As shown,the classification performance of the machine learning algorithms is far superior to the traditional TCP variant protocol.LDM-Satellite and Gradient Boosting these two ensemble methods have a higher AUC value than other single classifier.In terms of time consumption,Decision Tree takes the shortest time,but the misclassification rate is too high.Traditional TCP protocol classify lost packet based on multiple arriving ACKs or certain variables,such as RTT’s single threshold.These variable information need to be obtained before classification,so it takes too long.Moreover,the parameters are affected by many factors,so the final classification result is unstable,and the reason for the loss cannot be accurately got.

    Table 1.Comparison of various classification methods.

    Table 2 is the classification performance under different packet loss rates,from comparing the TCP protocol with the LDM-Satellite and other commonly used TCP protocols,such as TCP Reno,TCP Veno,and TCP WestWood+.The random packet loss rate is 10?5to 10?1.We can see that,as packet loss rate increases,TCP Reno and TCP WestWood+will randomly lose packet.The probability that a packet is misclassified as congestion increases.The classification performance of TCP Veno is closely related to the value ofβ,hereβis 5.For the proposed LDMSatellite,the accuracy rate of loss classification exceeds 98%,which can effectively avoid unnecessary reduction of congestion windows and improve network transmission performance.

    Table 2.Comparison of classification results under different packet loss rates.

    Figure 8 is throughput rates of LDM-Satellite and WestWood+,Veno,and Reno in different scenarios.In (a),the RTT is 50ms and the packet loss rate is set to 2%.It can be seen that the Goodput of all protocols is low when the bandwidth is low.However,as the bandwidth increases,the performance of LDM-Satellite is far superior to the others.In(b),the bandwidth and packet loss rate are 10Mbps and 2%,respectively.As Delay increases,the overall Goodput of TCP variants gradually declines,but Goodput of LDM-Satellite is always higher.Because the traditional TCP protocol adjusts the size of the congestion control window through predefined rules,which is too conservative.LDM-Satellite can quickly adjust the congestion window size based on concept drift detection and high-precision classification.Therefore,LDM-Satellite greatly improves Goodput under different bandwidth and delay scenarios.

    At the end of the experiment,we compare the throughput and fairness of the LDM-Satellite with the TCP protocols that are suitable for satellite networks,such as Hybly,Peach+,etc.The results are shown in Figure 9 and Figure 10.From Figure 9,in the case of low link error rate,Goodput of all protocols is relatively high,among which LDM-Satellite,Hybla and NewReno are closer to the link capacity c=1300 segments/s.In addition,as the packet loss rate increases,according to the adjustment of the congestionwindow,the value of Goodput decreases.However,LDM-Satellite has a better Goodput,especially when the packet loss rate is very high,for example,when the packet loss rate is 10?1,the Goodput of LDM-Satellite can reach 92%with the link capacity c.

    Figure 8.Comparison of throughput rates between LDMSatellite and WestWood+,Veno,Reno in different scenarios.(a)Under different bandwidths RTT is 50ms,loss rate is 2%.(b)Under different delaysbandwidth is 10Mbps,loss rate is 2%.

    Figure 9.Comparison of Goodput with different protocols for LEO.

    Figure 10.Fairness comparison between LDM-Satellite and multiple LEO satellite TCP.

    Figure 10 is a fairness index chart of LDM-Satellite and various TCP protocols for satellite networks.It presents,as the packet loss rate increases,the Fairness value of each version of the TCP protocol increases,and all are close to 1.LDM-Satellite guarantees better fairness and will not occupy network bandwidth too aggressively.

    V.CONCLUSION

    In this article,we propose a new loss classification strategy of TCP packet (LDM-Satellite) for LEO satellite networks,which fully considers the impact of satellite network dynamics on data distribution characteristics.Since the ensemble classification model is greatly affected by concept drift,this is the first paper that innovatively proposes to detect the concept drift of the data stream before classification.By detecting and then classifying,the current packet loss type can be more accurately determined according to the network status.In addition,this paper also builds a hybrid integration framework of base classifiers and a Naive Bayes classifier,and integrates multiple classifiers by a new weight distribution algorithm.In simulation experiments,our method has higher accuracy than other classification algorithms.Under different packet loss rates,LDM-Satellite performs better.Compared with TCP protocols such as cherry,Hybla,and peach+,it effectively improves Goodput.What’s more,LDMSatellite also has better fairness for other protocols and fairness in resource allocation.

    ACKNOWLEDGEMENT

    The authors wish to thank every responsible reviewer for his/her comments.The authors also wish to acknowledge the Wireless Network Positioning and Communication Integration Research Center in BUPT for financial support.

    99在线视频只有这里精品首页| 国产午夜精品论理片| 国产高清不卡午夜福利| 国产伦精品一区二区三区视频9| 亚洲av电影在线观看一区二区三区 | 美女大奶头视频| 亚洲欧洲日产国产| 非洲黑人性xxxx精品又粗又长| 国产成人a区在线观看| 尾随美女入室| 国产成人一区二区在线| 伦精品一区二区三区| 亚洲成色77777| 国产精品综合久久久久久久免费| 白带黄色成豆腐渣| 精品国产露脸久久av麻豆 | 中文字幕人妻熟人妻熟丝袜美| 久久久久久久久久久丰满| 中文字幕免费在线视频6| 精品久久久久久久久久久久久| 精品久久久久久久久久久久久| 老师上课跳d突然被开到最大视频| 少妇的逼水好多| 国产欧美日韩精品一区二区| 亚洲国产日韩欧美精品在线观看| 亚洲在久久综合| 啦啦啦观看免费观看视频高清| 高清视频免费观看一区二区 | 黄色配什么色好看| 日本熟妇午夜| 欧美激情国产日韩精品一区| 欧美区成人在线视频| 欧美日韩一区二区视频在线观看视频在线 | 亚洲经典国产精华液单| 色综合色国产| 日本黄大片高清| 亚洲欧美成人综合另类久久久 | 亚洲不卡免费看| 真实男女啪啪啪动态图| 国产淫语在线视频| 精品少妇黑人巨大在线播放 | 亚洲精品国产成人久久av| 国产精品精品国产色婷婷| 久久韩国三级中文字幕| 亚洲精品国产av成人精品| 男插女下体视频免费在线播放| 春色校园在线视频观看| 能在线免费观看的黄片| 久久精品夜色国产| 日本黄色片子视频| 国产中年淑女户外野战色| 成人二区视频| 特大巨黑吊av在线直播| 啦啦啦啦在线视频资源| 午夜福利成人在线免费观看| 国产成人精品一,二区| 国产日韩欧美在线精品| 欧美潮喷喷水| 搞女人的毛片| 国产乱人视频| 亚洲av成人精品一二三区| 国产亚洲91精品色在线| 亚洲av.av天堂| 国产视频首页在线观看| 天美传媒精品一区二区| 国产精品熟女久久久久浪| 久久精品国产亚洲网站| 亚洲一区高清亚洲精品| 国产精品蜜桃在线观看| 久久人妻av系列| 久久久久久久久中文| 久久鲁丝午夜福利片| 国产极品天堂在线| 国内揄拍国产精品人妻在线| 99久久中文字幕三级久久日本| 亚洲av.av天堂| 免费大片18禁| 99久久精品国产国产毛片| 1000部很黄的大片| 九草在线视频观看| 欧美日韩综合久久久久久| 免费观看在线日韩| 久久99蜜桃精品久久| 国内揄拍国产精品人妻在线| 又粗又硬又长又爽又黄的视频| 97在线视频观看| 精品免费久久久久久久清纯| 大香蕉97超碰在线| 国产精品久久久久久精品电影小说 | 一级毛片电影观看 | 欧美xxxx黑人xx丫x性爽| 99久国产av精品国产电影| 国产淫片久久久久久久久| 欧美3d第一页| 真实男女啪啪啪动态图| 国产精品乱码一区二三区的特点| 亚洲第一区二区三区不卡| 亚洲欧美成人综合另类久久久 | 精品久久久久久成人av| 啦啦啦韩国在线观看视频| 一级毛片aaaaaa免费看小| 看黄色毛片网站| 亚洲精品aⅴ在线观看| 国产极品天堂在线| 久久99热这里只有精品18| 日本av手机在线免费观看| 亚洲国产色片| 久久久久久久国产电影| 国产91av在线免费观看| 午夜精品一区二区三区免费看| 国产探花在线观看一区二区| 中文资源天堂在线| 国产一级毛片七仙女欲春2| 精品久久久久久久久亚洲| 国产成年人精品一区二区| 观看美女的网站| 非洲黑人性xxxx精品又粗又长| 国产综合懂色| 18+在线观看网站| 国产精品.久久久| 久久99蜜桃精品久久| 九九久久精品国产亚洲av麻豆| 久久久久精品久久久久真实原创| 菩萨蛮人人尽说江南好唐韦庄 | 美女黄网站色视频| 国产成人福利小说| 18禁裸乳无遮挡免费网站照片| 国产黄色视频一区二区在线观看 | 久久精品国产自在天天线| 97在线视频观看| av女优亚洲男人天堂| 中文字幕免费在线视频6| 伊人久久精品亚洲午夜| 日日啪夜夜撸| 国产亚洲午夜精品一区二区久久 | 中文字幕av成人在线电影| 最新中文字幕久久久久| 国产精品国产三级国产av玫瑰| 欧美成人a在线观看| 建设人人有责人人尽责人人享有的 | 人妻少妇偷人精品九色| 午夜福利成人在线免费观看| 亚洲,欧美,日韩| 两个人的视频大全免费| 看非洲黑人一级黄片| 日韩欧美三级三区| 舔av片在线| 99久久精品国产国产毛片| 高清毛片免费看| 高清视频免费观看一区二区 | 成人毛片a级毛片在线播放| 中文天堂在线官网| 国产 一区精品| 淫秽高清视频在线观看| 国产乱来视频区| 亚洲性久久影院| 日日啪夜夜撸| 欧美精品国产亚洲| 久久国内精品自在自线图片| 尤物成人国产欧美一区二区三区| 亚洲欧美成人精品一区二区| 国产高潮美女av| 亚洲内射少妇av| 国产美女午夜福利| 观看免费一级毛片| 九草在线视频观看| 久久久午夜欧美精品| 男女边吃奶边做爰视频| 成人av在线播放网站| 观看免费一级毛片| 人体艺术视频欧美日本| 亚洲美女视频黄频| 国语对白做爰xxxⅹ性视频网站| 成人亚洲精品av一区二区| 亚洲欧美成人精品一区二区| 乱码一卡2卡4卡精品| 国产精品1区2区在线观看.| 亚洲成人中文字幕在线播放| АⅤ资源中文在线天堂| 免费观看的影片在线观看| 成人综合一区亚洲| 两性午夜刺激爽爽歪歪视频在线观看| 美女脱内裤让男人舔精品视频| 午夜福利在线在线| 97在线视频观看| 99在线视频只有这里精品首页| 免费看日本二区| 精品久久久久久久久久久久久| 精品国产露脸久久av麻豆 | 欧美一级a爱片免费观看看| av在线亚洲专区| 日产精品乱码卡一卡2卡三| www日本黄色视频网| 国产亚洲午夜精品一区二区久久 | 日韩一本色道免费dvd| 高清av免费在线| 久久久久九九精品影院| 丝袜喷水一区| 老司机影院毛片| 久久久久久伊人网av| 成人无遮挡网站| 级片在线观看| 美女脱内裤让男人舔精品视频| 久久久久久伊人网av| 一本久久精品| 最近最新中文字幕大全电影3| 国产老妇女一区| 亚州av有码| 亚洲欧美日韩高清专用| 久久久久久久久久久免费av| 国产高清国产精品国产三级 | 九九热线精品视视频播放| 99热这里只有精品一区| 欧美又色又爽又黄视频| 亚洲av免费高清在线观看| 伊人久久精品亚洲午夜| 中文字幕熟女人妻在线| 国产免费又黄又爽又色| 成人国产麻豆网| 国产麻豆成人av免费视频| 伊人久久精品亚洲午夜| 日韩强制内射视频| 免费观看精品视频网站| 国产成人免费观看mmmm| 欧美成人精品欧美一级黄| 一区二区三区高清视频在线| 丝袜美腿在线中文| 麻豆成人av视频| 国产男人的电影天堂91| 亚洲人成网站在线播| 日韩人妻高清精品专区| 99久国产av精品国产电影| 亚洲最大成人中文| 最近视频中文字幕2019在线8| 观看免费一级毛片| 久久婷婷人人爽人人干人人爱| 免费在线观看成人毛片| 91久久精品电影网| 毛片一级片免费看久久久久| 一个人看视频在线观看www免费| 亚洲av成人av| 国产又黄又爽又无遮挡在线| 亚洲成人精品中文字幕电影| 99热6这里只有精品| 插逼视频在线观看| 最近中文字幕高清免费大全6| 大又大粗又爽又黄少妇毛片口| 少妇被粗大猛烈的视频| 国产精品一二三区在线看| 国产极品精品免费视频能看的| 精品免费久久久久久久清纯| 中国美白少妇内射xxxbb| 97超视频在线观看视频| 亚洲美女搞黄在线观看| 精品久久久久久久久亚洲| 国产麻豆成人av免费视频| 99视频精品全部免费 在线| 日本av手机在线免费观看| 欧美潮喷喷水| av又黄又爽大尺度在线免费看 | 2022亚洲国产成人精品| 毛片女人毛片| 伦理电影大哥的女人| 亚洲精品国产成人久久av| 最近最新中文字幕大全电影3| av国产免费在线观看| 午夜激情福利司机影院| 在线观看av片永久免费下载| 精品一区二区三区视频在线| 亚洲电影在线观看av| 岛国在线免费视频观看| 伦精品一区二区三区| 久久人人爽人人爽人人片va| 日本三级黄在线观看| 久久99精品国语久久久| 乱系列少妇在线播放| 小说图片视频综合网站| 卡戴珊不雅视频在线播放| 日韩欧美在线乱码| 精品午夜福利在线看| 亚洲国产成人一精品久久久| 美女大奶头视频| 免费黄色在线免费观看| 国产乱人视频| 午夜爱爱视频在线播放| 91久久精品电影网| 国产片特级美女逼逼视频| 黄色日韩在线| 91久久精品国产一区二区成人| 国产成人午夜福利电影在线观看| 国产男人的电影天堂91| 国产日韩欧美在线精品| 亚州av有码| 国产精品永久免费网站| 国产伦精品一区二区三区视频9| 成人二区视频| 精品人妻视频免费看| 日韩 亚洲 欧美在线| 国产极品精品免费视频能看的| 六月丁香七月| 夫妻性生交免费视频一级片| 一二三四中文在线观看免费高清| 亚洲,欧美,日韩| 国产亚洲91精品色在线| 啦啦啦观看免费观看视频高清| 我的女老师完整版在线观看| 亚洲精品456在线播放app| 蜜臀久久99精品久久宅男| 亚洲国产色片| ponron亚洲| 国产精品熟女久久久久浪| 国产老妇女一区| 一个人观看的视频www高清免费观看| 亚洲在久久综合| or卡值多少钱| 97超碰精品成人国产| 男女边吃奶边做爰视频| 天堂av国产一区二区熟女人妻| 亚洲一级一片aⅴ在线观看| 蜜桃久久精品国产亚洲av| av免费观看日本| 啦啦啦啦在线视频资源| 深夜a级毛片| 人妻系列 视频| 中文字幕av成人在线电影| 精品欧美国产一区二区三| 免费观看在线日韩| 国产免费视频播放在线视频 | 美女大奶头视频| 欧美xxxx性猛交bbbb| 国产精品一区二区性色av| 最近手机中文字幕大全| 国产午夜福利久久久久久| 欧美xxxx黑人xx丫x性爽| 99久久九九国产精品国产免费| 国产精品女同一区二区软件| 国产伦一二天堂av在线观看| 一个人免费在线观看电影| 久久久久免费精品人妻一区二区| 国产精品精品国产色婷婷| 看黄色毛片网站| 欧美一区二区国产精品久久精品| 国产在线男女| or卡值多少钱| 国产精品99久久久久久久久| 精品免费久久久久久久清纯| www.av在线官网国产| 91在线精品国自产拍蜜月| 高清视频免费观看一区二区 | 国产一级毛片在线| eeuss影院久久| 色视频www国产| 神马国产精品三级电影在线观看| 韩国av在线不卡| 亚洲激情五月婷婷啪啪| 狂野欧美白嫩少妇大欣赏| 男的添女的下面高潮视频| 亚洲va在线va天堂va国产| 欧美区成人在线视频| 午夜老司机福利剧场| 黄色日韩在线| 久久久久九九精品影院| av在线蜜桃| 麻豆成人午夜福利视频| 日韩精品青青久久久久久| 国产精品美女特级片免费视频播放器| 亚洲激情五月婷婷啪啪| 桃色一区二区三区在线观看| 亚洲成人中文字幕在线播放| 欧美极品一区二区三区四区| 菩萨蛮人人尽说江南好唐韦庄 | www日本黄色视频网| 久热久热在线精品观看| 搡女人真爽免费视频火全软件| 国产视频内射| 大话2 男鬼变身卡| 国产一区二区在线观看日韩| 麻豆久久精品国产亚洲av| 嫩草影院新地址| 国产色婷婷99| 美女黄网站色视频| 国产在线一区二区三区精 | 黑人高潮一二区| 老司机福利观看| 日韩av在线免费看完整版不卡| 久久国内精品自在自线图片| 久久久久久久久久久免费av| 欧美性感艳星| 中文字幕人妻熟人妻熟丝袜美| 男人舔奶头视频| 免费搜索国产男女视频| 小蜜桃在线观看免费完整版高清| 菩萨蛮人人尽说江南好唐韦庄 | 日韩国内少妇激情av| 国产成人精品婷婷| 极品教师在线视频| 久久精品人妻少妇| 国产精品久久久久久av不卡| 国产精品电影一区二区三区| 亚洲欧美成人综合另类久久久 | 午夜日本视频在线| videossex国产| 免费看光身美女| 欧美一区二区国产精品久久精品| 级片在线观看| av国产免费在线观看| 国产麻豆成人av免费视频| 国产老妇女一区| 狠狠狠狠99中文字幕| 看黄色毛片网站| 亚洲国产欧美在线一区| 亚洲人成网站在线播| 只有这里有精品99| 欧美色视频一区免费| 精品国产露脸久久av麻豆 | 免费看av在线观看网站| 亚洲av成人精品一二三区| videos熟女内射| 国产精品人妻久久久影院| 久久精品熟女亚洲av麻豆精品 | 亚洲高清免费不卡视频| 国内精品美女久久久久久| 午夜爱爱视频在线播放| 久久99蜜桃精品久久| 中文字幕制服av| 久久久成人免费电影| 亚洲人成网站高清观看| 成人特级av手机在线观看| 在线播放无遮挡| 久久精品久久精品一区二区三区| 国产精品一区二区在线观看99 | 99热全是精品| 日韩中字成人| 色尼玛亚洲综合影院| 久久久久久久久久黄片| 少妇的逼水好多| 观看美女的网站| 一个人看的www免费观看视频| 国语对白做爰xxxⅹ性视频网站| 人妻夜夜爽99麻豆av| 精品无人区乱码1区二区| 91av网一区二区| 在线播放国产精品三级| 国产亚洲91精品色在线| 免费av观看视频| 狠狠狠狠99中文字幕| 色网站视频免费| 亚洲四区av| 日韩精品青青久久久久久| 亚洲精品456在线播放app| 少妇的逼水好多| 最近视频中文字幕2019在线8| 亚洲欧美日韩东京热| 一边摸一边抽搐一进一小说| 午夜福利高清视频| 国产精品久久久久久久久免| 国产私拍福利视频在线观看| 国产午夜精品一二区理论片| 两个人视频免费观看高清| 99视频精品全部免费 在线| 在线免费十八禁| 国产精品福利在线免费观看| 直男gayav资源| av又黄又爽大尺度在线免费看 | 又粗又硬又长又爽又黄的视频| 亚洲精华国产精华液的使用体验| 欧美激情在线99| 亚洲中文字幕日韩| 麻豆乱淫一区二区| 日本wwww免费看| 亚洲在线自拍视频| 成人漫画全彩无遮挡| 国产精品.久久久| 男人和女人高潮做爰伦理| 纵有疾风起免费观看全集完整版 | 中文字幕人妻熟人妻熟丝袜美| 日本猛色少妇xxxxx猛交久久| 欧美人与善性xxx| 国模一区二区三区四区视频| 久久久亚洲精品成人影院| 欧美丝袜亚洲另类| 精品国产三级普通话版| 国产淫语在线视频| 国产黄色小视频在线观看| 最近中文字幕2019免费版| 人妻少妇偷人精品九色| 3wmmmm亚洲av在线观看| 国产免费又黄又爽又色| 视频中文字幕在线观看| 99视频精品全部免费 在线| 最近2019中文字幕mv第一页| 午夜a级毛片| 中文字幕久久专区| 嫩草影院新地址| 日本免费在线观看一区| 国产精品一二三区在线看| 亚洲怡红院男人天堂| 秋霞伦理黄片| 女人十人毛片免费观看3o分钟| 看十八女毛片水多多多| 色播亚洲综合网| 亚洲av成人精品一区久久| 永久网站在线| 欧美日韩国产亚洲二区| 视频中文字幕在线观看| 联通29元200g的流量卡| 少妇的逼好多水| 免费一级毛片在线播放高清视频| 久久精品综合一区二区三区| 亚洲国产日韩欧美精品在线观看| 综合色丁香网| 久久鲁丝午夜福利片| 久久综合国产亚洲精品| kizo精华| 国产精品精品国产色婷婷| 国产亚洲精品av在线| 色哟哟·www| 国产精品国产高清国产av| 国产片特级美女逼逼视频| 免费观看a级毛片全部| 成人美女网站在线观看视频| 久久99热这里只频精品6学生 | 久久久久久久午夜电影| 波野结衣二区三区在线| 精品无人区乱码1区二区| 99久久精品热视频| 少妇的逼水好多| 亚洲五月天丁香| 日韩一本色道免费dvd| 国产精品久久电影中文字幕| 男女边吃奶边做爰视频| 人人妻人人澡人人爽人人夜夜 | 亚洲不卡免费看| 欧美一区二区国产精品久久精品| 人人妻人人澡人人爽人人夜夜 | av线在线观看网站| 欧美一区二区国产精品久久精品| 国语自产精品视频在线第100页| 简卡轻食公司| 亚洲av成人精品一二三区| videos熟女内射| 日韩欧美三级三区| 干丝袜人妻中文字幕| 级片在线观看| 七月丁香在线播放| 天堂影院成人在线观看| 美女国产视频在线观看| 免费黄网站久久成人精品| 日韩制服骚丝袜av| 国产亚洲av片在线观看秒播厂 | 最近中文字幕2019免费版| 亚洲最大成人手机在线| 亚洲精品aⅴ在线观看| 国产中年淑女户外野战色| 亚洲欧美精品专区久久| 亚洲精品456在线播放app| 精品国产三级普通话版| 国产综合懂色| 天堂√8在线中文| kizo精华| av在线播放精品| 在线a可以看的网站| 99久国产av精品国产电影| 国产乱人视频| 干丝袜人妻中文字幕| 搡女人真爽免费视频火全软件| 国产 一区 欧美 日韩| 长腿黑丝高跟| 亚洲国产精品合色在线| 国产成人aa在线观看| 99久久精品热视频| 国产成人一区二区在线| 国产淫片久久久久久久久| 久久这里只有精品中国| 日日摸夜夜添夜夜添av毛片| 国产精品熟女久久久久浪| 男人舔女人下体高潮全视频| 久久久欧美国产精品| 亚洲va在线va天堂va国产| 成人三级黄色视频| 亚洲丝袜综合中文字幕| 亚洲高清免费不卡视频| 黄片无遮挡物在线观看| 亚洲丝袜综合中文字幕| 一边摸一边抽搐一进一小说| 亚洲国产精品sss在线观看| 秋霞伦理黄片| 日韩欧美精品免费久久| 日本色播在线视频| 欧美变态另类bdsm刘玥| 插阴视频在线观看视频| 哪个播放器可以免费观看大片| a级一级毛片免费在线观看| 日韩高清综合在线| 久久精品人妻少妇| 蜜桃亚洲精品一区二区三区| 草草在线视频免费看| av专区在线播放| 岛国在线免费视频观看| 日本-黄色视频高清免费观看| 中文字幕精品亚洲无线码一区| 五月伊人婷婷丁香| 国产成人a区在线观看| 最近视频中文字幕2019在线8| av福利片在线观看| 亚洲欧美中文字幕日韩二区| 中文欧美无线码| 亚洲第一区二区三区不卡| 欧美色视频一区免费| 日本与韩国留学比较| 精品久久久久久久久久久久久| 国产一区二区在线av高清观看| 色5月婷婷丁香| 91久久精品国产一区二区成人| 寂寞人妻少妇视频99o|