This paper addresses the clustering problem for mobile ad hoc networks. In the proposed scheme, Doppler shift associated with received signals is used to estimate the relative speed between aelnster head and its membe...This paper addresses the clustering problem for mobile ad hoc networks. In the proposed scheme, Doppler shift associated with received signals is used to estimate the relative speed between aelnster head and its members. With the estimated speed, a node can predict its stay time in every nearby cluster. In the initial clustering stage, a node joins a duster that can provide it with the longest stay time in order to reduce the number of re-affiliations. In the cluster maintaining stage, strategies are designed to help node cope with connection break caused by channel fading and node mobility. Simulation results show that the proposed clustering scheme can reduce the number of re-affiliations and the average disconnection time compared with previous schemes.展开更多
Cooperative spectrum sensing in cog- nitive radio is investigated to improve the det- ection performance of Primary User （PU）. Meanwhile, cluster-based hierarchical coop- eration is introduced for reducing the overh...Cooperative spectrum sensing in cog- nitive radio is investigated to improve the det- ection performance of Primary User （PU）. Meanwhile, cluster-based hierarchical coop- eration is introduced for reducing the overhead as well as maintaining a certain level of sens- ing performance. However, in existing hierar- chically cooperative spectrum sensing algo- rithms, the robustness problem of the system is seldom considered. In this paper, we pro- pose a reputation-based hierarchically coop- erative spectrum sensing scheme in Cognitive Radio Networks （CRNs）. Before spectrum sensing, clusters are grouped based on the location correlation coefficients of Secondary Users （SUs）. In the proposed scheme, there are two levels of cooperation, the first one is performed within a cluster and the second one is carried out among clusters. With the reputa- tion mechanism and modified MAJORITY rule in the second level cooperation, the pro- posed scheme can not only relieve the influ- ence of the shadowing, but also eliminate the impact of the PU emulation attack on a rela- tively large scale. Simulation results show that, in the scenarios with deep-shadowing or mul- tiple attacked SUs, our proposed scheme ach- ieves a better tradeoff between the system robustness and the energy saving compared with those conventionally cooperative sensing schemes.展开更多
Existing IP geolocation algorithms based on delay similarity often rely on the principle that geographically adjacent IPs have similar delays.However,this principle is often invalid in real Internet environment,which ...Existing IP geolocation algorithms based on delay similarity often rely on the principle that geographically adjacent IPs have similar delays.However,this principle is often invalid in real Internet environment,which leads to unreliable geolocation results.To improve the accuracy and reliability of locating IP in real Internet,a street-level IP geolocation algorithm based on landmarks clustering is proposed.Firstly,we use the probes to measure the known landmarks to obtain their delay vectors,and cluster landmarks using them.Secondly,the landmarks are clustered again by their latitude and longitude,and the intersection of these two clustering results is taken to form training sets.Thirdly,we train multiple neural networks to get the mapping relationship between delay and location in each training set.Finally,we determine one of the neural networks for the target by the delay similarity and relative hop counts,and then geolocate the target by this network.As it brings together the delay and geographical coordinates clustering,the proposed algorithm largely improves the inconsistency between them and enhances the mapping relationship between them.We evaluate the algorithm by a series of experiments in Hong Kong,Shanghai,Zhengzhou and New York.The experimental results show that the proposed algorithm achieves street-level IP geolocation,and comparing with existing typical streetlevel geolocation algorithms,the proposed algorithm improves the geolocation reliability significantly.展开更多
近年来,异质信息网络特别是基于位置的社交网络(Location-Based Social Networks,LBSN)中的社区发现已成为新兴的研究热点.然而,目前大多数社区发现研究仅考虑基于同质结构的社交网络,显然都已无法有效融合LBSN这种异质网络所包含的多...近年来,异质信息网络特别是基于位置的社交网络(Location-Based Social Networks,LBSN)中的社区发现已成为新兴的研究热点.然而,目前大多数社区发现研究仅考虑基于同质结构的社交网络,显然都已无法有效融合LBSN这种异质网络所包含的多模实体及其多维关系.为了应对该挑战性问题,本文提出了一种新的双重社区聚类与关联方法(Communities Clustering and Associating Method,CCAM),该方法先在LBSN的社交媒体层上,通过信息熵度量用户发布主题之间的相似性,进而再将相似用户兴趣聚类问题转换成求解基于模糊聚类的目标函数以获得重叠的兴趣主题簇结构.然后在地理位置层中,将用户-位置签到关系网络形成的二分图转换为超图模型,并采用超边聚类方式得到用户关于地理位置的兴趣点特征簇.最后,在兴趣主题簇与地理位置簇之间借助中间用户层的社交关系建立这两层异质簇间的关联性表示模型,并通过随机梯度下降法求解模型的局部最优解.在两个真实数据集Foursquare(NYC)和Yelp上的实验结果表明,本文提出的CCAM方法有效融合了用户-媒体发布关系、用户间社交关系、用户-位置签到关系等多维度关系,能准确获得LBSN中紧密关联的用户兴趣主题簇与地理位置簇,使得这双层社区结构不仅在外部结构特征与兴趣内聚性指标上都优于传统算法,并且还在兴趣主题推荐与位置兴趣点推荐方面的平均准确率提高至少32%.展开更多
基金Supported by the National Science Foundation of China （No. 60830001）, the Program for Changjiang Scholars and Innovative Research Team in University （No. IRT0949） and the State key Laboratory of Rail traffic Con~ol and Safety （No. RCS2010ZT012）
文摘This paper addresses the clustering problem for mobile ad hoc networks. In the proposed scheme, Doppler shift associated with received signals is used to estimate the relative speed between aelnster head and its members. With the estimated speed, a node can predict its stay time in every nearby cluster. In the initial clustering stage, a node joins a duster that can provide it with the longest stay time in order to reduce the number of re-affiliations. In the cluster maintaining stage, strategies are designed to help node cope with connection break caused by channel fading and node mobility. Simulation results show that the proposed clustering scheme can reduce the number of re-affiliations and the average disconnection time compared with previous schemes.
基金ACKNOWLEDGEMENT This work was partially supported by the Na- tional Natural Science Foundation of China under Grant No. 61071127 and the Science and Technology Department of Zhejiang Pro- vince under Grants No. 2012C01036-1, No. 2011R10035.
文摘Cooperative spectrum sensing in cog- nitive radio is investigated to improve the det- ection performance of Primary User （PU）. Meanwhile, cluster-based hierarchical coop- eration is introduced for reducing the overhead as well as maintaining a certain level of sens- ing performance. However, in existing hierar- chically cooperative spectrum sensing algo- rithms, the robustness problem of the system is seldom considered. In this paper, we pro- pose a reputation-based hierarchically coop- erative spectrum sensing scheme in Cognitive Radio Networks （CRNs）. Before spectrum sensing, clusters are grouped based on the location correlation coefficients of Secondary Users （SUs）. In the proposed scheme, there are two levels of cooperation, the first one is performed within a cluster and the second one is carried out among clusters. With the reputa- tion mechanism and modified MAJORITY rule in the second level cooperation, the pro- posed scheme can not only relieve the influ- ence of the shadowing, but also eliminate the impact of the PU emulation attack on a rela- tively large scale. Simulation results show that, in the scenarios with deep-shadowing or mul- tiple attacked SUs, our proposed scheme ach- ieves a better tradeoff between the system robustness and the energy saving compared with those conventionally cooperative sensing schemes.
基金the National Key R&D Program of China 2016YFB0801303(F.L.received the grant,the sponsors’website is https://service.most.gov.cn/)by the National Key R&D Program of China 2016QY01W0105(X.L.received the grant,the sponsors’website is https://service.most.gov.cn/)+5 种基金by the National Natural Science Foundation of China U1636219(X.L.received the grant,the sponsors’website is http://www.nsfc.gov.cn/)by the National Natural Science Foundation of China 61602508(J.L.received the grant,the sponsors’website is http://www.nsfc.gov.cn/)by the National Natural Science Foundation of China 61772549(F.L.received the grant,the sponsors’website is http://www.nsfc.gov.cn/)by the National Natural Science Foundation of China U1736214(F.L.received the grant,the sponsors’website is http://www.nsfc.gov.cn/)by the National Natural Science Foundation of China U1804263(X.L.received the grant,the sponsors’website is http://www.nsfc.gov.cn/)by the Science and Technology Innovation Talent Project of Henan Province 184200510018(X.L.received the grant,the sponsors’website is http://www.hnkjt.gov.cn/).
文摘Existing IP geolocation algorithms based on delay similarity often rely on the principle that geographically adjacent IPs have similar delays.However,this principle is often invalid in real Internet environment,which leads to unreliable geolocation results.To improve the accuracy and reliability of locating IP in real Internet,a street-level IP geolocation algorithm based on landmarks clustering is proposed.Firstly,we use the probes to measure the known landmarks to obtain their delay vectors,and cluster landmarks using them.Secondly,the landmarks are clustered again by their latitude and longitude,and the intersection of these two clustering results is taken to form training sets.Thirdly,we train multiple neural networks to get the mapping relationship between delay and location in each training set.Finally,we determine one of the neural networks for the target by the delay similarity and relative hop counts,and then geolocate the target by this network.As it brings together the delay and geographical coordinates clustering,the proposed algorithm largely improves the inconsistency between them and enhances the mapping relationship between them.We evaluate the algorithm by a series of experiments in Hong Kong,Shanghai,Zhengzhou and New York.The experimental results show that the proposed algorithm achieves street-level IP geolocation,and comparing with existing typical streetlevel geolocation algorithms,the proposed algorithm improves the geolocation reliability significantly.
文摘近年来,异质信息网络特别是基于位置的社交网络(Location-Based Social Networks,LBSN)中的社区发现已成为新兴的研究热点.然而,目前大多数社区发现研究仅考虑基于同质结构的社交网络,显然都已无法有效融合LBSN这种异质网络所包含的多模实体及其多维关系.为了应对该挑战性问题,本文提出了一种新的双重社区聚类与关联方法(Communities Clustering and Associating Method,CCAM),该方法先在LBSN的社交媒体层上,通过信息熵度量用户发布主题之间的相似性,进而再将相似用户兴趣聚类问题转换成求解基于模糊聚类的目标函数以获得重叠的兴趣主题簇结构.然后在地理位置层中,将用户-位置签到关系网络形成的二分图转换为超图模型,并采用超边聚类方式得到用户关于地理位置的兴趣点特征簇.最后,在兴趣主题簇与地理位置簇之间借助中间用户层的社交关系建立这两层异质簇间的关联性表示模型,并通过随机梯度下降法求解模型的局部最优解.在两个真实数据集Foursquare(NYC)和Yelp上的实验结果表明,本文提出的CCAM方法有效融合了用户-媒体发布关系、用户间社交关系、用户-位置签到关系等多维度关系,能准确获得LBSN中紧密关联的用户兴趣主题簇与地理位置簇,使得这双层社区结构不仅在外部结构特征与兴趣内聚性指标上都优于传统算法,并且还在兴趣主题推荐与位置兴趣点推荐方面的平均准确率提高至少32%.
