In wireless sensor networks（WSNs）,appropriate topology control（TC）could efficiently balance the load among sensor nodes and extend network lifespan.Clustering is an effective topology control technique that could ...In wireless sensor networks（WSNs）,appropriate topology control（TC）could efficiently balance the load among sensor nodes and extend network lifespan.Clustering is an effective topology control technique that could reduce energy consumption and provide scalability to WSNs.However,some clustering algorithms,including the traditional low energy adaptive clustering hierarchy（LEACH）,don＇t consider the residual energy and the communication distance.The energy consumption could dramatically increase in the case of long communication distance and high rate of control message exchange.In this paper we propose an energy-balanced clustering algorithm which considers the communication distance and the residual energy.Moreover the cluster head（CH）reselection is relevant to the current CH residual energy in order to reduce overheads.The simulation results demonstrate that the proposed algorithm prolongs the lifetime of the WSN in comparison to the LEACH and a hybrid clustering approach（HCA）.展开更多
The classification of tropical cyclones(TCs) is significant to obtaining their temporal and spatial variation characteristics in the context of dramatic-changing global climate. A new TCs clustering method by using K-...The classification of tropical cyclones(TCs) is significant to obtaining their temporal and spatial variation characteristics in the context of dramatic-changing global climate. A new TCs clustering method by using K-means clustering algorithm with nine physical indexes is proposed in the paper. Each TC is quantified into an 11-dimensional vector concerning trajectory attributes, time attributes and power attributes. Two recurving clusters(cluster A and E)and three straight-moving clusters(cluster B, C and D) are categorized from the TC best-track dataset of the western North Pacific(WNP) over the period of 1949-2013, and TCs' properties have been analyzed and compared in different aspects. The calculation results of coefficient variation(CV) and Nash-Sutcliffe efficiency(NSE) reveal a high level of intra-cluster cohesiveness and inter-cluster divergence, which means that the physical index system could serve as a feasible method of TCs classification. The clusters are then analyzed in terms of trajectory, lifespan, seasonality, trend,intensity and Power Dissipation Index(PDI). The five classified clusters show distinct features in TCs' temporal and spatial development discipline. Moreover, each cluster has its individual motion pattern, variation trend, influence region and impact degree.展开更多
The Virtual Machine(VM) placement is a serious problem to limit the improvement of resource utilization of data center. The VM traffic bandwidth demand is a Non zero-sum resource that the global traffic sum is relativ...The Virtual Machine(VM) placement is a serious problem to limit the improvement of resource utilization of data center. The VM traffic bandwidth demand is a Non zero-sum resource that the global traffic sum is relative with each VM placement position. In this paper, we introduce a new improved traffic constant algorithm in the data center, called Degree and Weighted Maximum Traffic Ratio(DWMTR). The proposal DWMTR algorithm defines a new weighted ratio parameter in this paper. The main body of the parameter is constructed with the ratio, current overall intra-cluster traffic divided by current overall inter-cluster traffic, when a new VM places in the data center. The DWMTR algorithm has the ability to constraint the inter-cluster traffic incensement more strictly than the current VM placement algorithms based on traffic bandwidth allocation. For this algorithm based on the theoretical analysis and simulation, it confirms the proposed DWMTR possesses smaller global interactive traffic cost than the control group algorithms in the appointed VM placement in the three-layer data center model.展开更多
Wireless sensor networks are energy constraint networks. Energy efficiency, to prolong the network for a longer time is critical issue for wireless sensor network protocols. Clustering protocols are energy efficient a...Wireless sensor networks are energy constraint networks. Energy efficiency, to prolong the network for a longer time is critical issue for wireless sensor network protocols. Clustering protocols are energy efficient approaches to extend the lifetime of network. Intra-cluster communication is the main driving factor for energy efficiency of clustering protocols. Intra-cluster energy consumption depends upon the position of cluster head in the cluster. Wrongly positioned clusters head make cluster more energy consuming. In this paper, a simple and efficient cluster head selection scheme is proposed, named Smart Cluster Head Selection (SCHS). It can be implemented with any distributed clustering approach. In SCHS, the area is divided into two parts: border area and inner area. Only inner area nodes are eligible for cluster head role. SCHS reduces the intra-cluster communication distance hence improves the energy efficiency of cluster. The simulation results show that SCHS has significant improvement over LEACH in terms of lifetime of network and data units gathered at base station.展开更多
For many clustering algorithms,it is very important to determine an appropriate number of clusters,which is called cluster validity problem.In this paper,a new clustering validity assessment index is proposed based on...For many clustering algorithms,it is very important to determine an appropriate number of clusters,which is called cluster validity problem.In this paper,a new clustering validity assessment index is proposed based on a novel method to select the margin point between two clusters for in-ter-cluster similarity more accurately,and provides an improved scatter function for intra-cluster similarity.Simulation results show the effectiveness of the proposed index on the data sets under consideration regardless of the choice of a clustering algorithm.展开更多
Background: Factors associated with hospital mortality are usually identified and their effects are quantified through statistical modeling. To guide the choice of the best statistical model, we first quantify the pre...Background: Factors associated with hospital mortality are usually identified and their effects are quantified through statistical modeling. To guide the choice of the best statistical model, we first quantify the predictive ability of each model and then use the CIHI index to see if the hospital policy needs any change. Objectives: The main purpose of this study compared three statistical models in the evaluation of the association between hospital mortality and two risk factors, namely subject’s age at admission and the length of stay, adjusting for the effect of Diagnostic Related Groups (DRG). Methods: We use several SAS procedures to quantify the effect of DRG on the variability in hospital mortality. These procedures are the Logistic Regression model (ignoring the DRG effect), the Generalized Estimating Equation (GEE) that takes into account the within DRG clustering effect (but the within cluster correlation is treated as nuisance parameter), and the Generalized Linear Mixed Model (GLIMMIX). We showed that the GLIMMIX is superior to other models as it properly accounts for the clustering effect of “Diagnostic Related Groups” denoted by DRG. Results: The GLM procedure showed that the proportional contribution of DRG is 16%. All three models showed significant and increasing trend in mortality (P < 0.0001) with respect to the two risk factors (age at admission, and hospital length of stay). It was also clear that the CIHI index was not different under the three models. We re-estimated the models parameters after dichotomizing the risk factors at the optimal cut-off points, using the ROC curve. The parameters estimates and their significance did not change.展开更多
提出了一种基于簇结构的无线传感器网络数据收集协议EADEEG(an energy-aware data gathering protocol for wireless sensor networks).EADEEG通过最小化网络通信开销以及良好的能量负载平衡方法,可以有效地延长网络寿命.与以前的相关...提出了一种基于簇结构的无线传感器网络数据收集协议EADEEG(an energy-aware data gathering protocol for wireless sensor networks).EADEEG通过最小化网络通信开销以及良好的能量负载平衡方法,可以有效地延长网络寿命.与以前的相关研究相比,EADEEG采用了一种全新的簇头竞争参数,能够更好地解决节点能量异构问题.此外,EADEEG也采用了一种简单而有效的簇内节点调度算法,通过控制活动节点的密度,可以在不增加额外控制开销的条件下关闭冗余节点并保证覆盖要求,因此可以进一步延长网络寿命.模拟实验证明,在节点初始能量同构和异构两种情况下,EADEEG协议都能够满足用户对覆盖率的要求,并在网络寿命上大幅度优于LEACH(low energy adaptive clustering hierarchy),PEGASIS(power-efficient gathering in sensor information systems)和DEEG(distributed energy-efficient data gathering and aggregation protocol)协议.展开更多
为改善超密集网络中的内容分发效率,提出一种基于协作缓存的内容分发机制。在对小小区基站(Small-cell Base Station,SBS)进行分簇的基础上,由每个簇自主决定内容如何在簇内的SBS进行缓存;其次,在处理用户内容请求时,不仅考虑了簇内协作...为改善超密集网络中的内容分发效率,提出一种基于协作缓存的内容分发机制。在对小小区基站(Small-cell Base Station,SBS)进行分簇的基础上,由每个簇自主决定内容如何在簇内的SBS进行缓存;其次,在处理用户内容请求时,不仅考虑了簇内协作,还考虑了簇间协作,以进一步降低宏基站和移动核心网的负载。仿真结果表明,与无网内缓存以及只有簇内协作的场景相比,该方法可以有效降低内容传输时延,提高内容分发性能。展开更多
基金Supported by the National Natural Science Foundation of China(6104086)Scientific Research,Postgraduate Training Joint-Build Project(20120639002)
文摘In wireless sensor networks（WSNs）,appropriate topology control（TC）could efficiently balance the load among sensor nodes and extend network lifespan.Clustering is an effective topology control technique that could reduce energy consumption and provide scalability to WSNs.However,some clustering algorithms,including the traditional low energy adaptive clustering hierarchy（LEACH）,don＇t consider the residual energy and the communication distance.The energy consumption could dramatically increase in the case of long communication distance and high rate of control message exchange.In this paper we propose an energy-balanced clustering algorithm which considers the communication distance and the residual energy.Moreover the cluster head（CH）reselection is relevant to the current CH residual energy in order to reduce overheads.The simulation results demonstrate that the proposed algorithm prolongs the lifetime of the WSN in comparison to the LEACH and a hybrid clustering approach（HCA）.
基金National Key Research and Development Program of China(2016YFC0401903)National Natural Science Foundation of China(51722906,51679159,51509179)Tianjin Research Program of Application Foundation and Advanced Technology(15JCYBTC21800)
文摘The classification of tropical cyclones(TCs) is significant to obtaining their temporal and spatial variation characteristics in the context of dramatic-changing global climate. A new TCs clustering method by using K-means clustering algorithm with nine physical indexes is proposed in the paper. Each TC is quantified into an 11-dimensional vector concerning trajectory attributes, time attributes and power attributes. Two recurving clusters(cluster A and E)and three straight-moving clusters(cluster B, C and D) are categorized from the TC best-track dataset of the western North Pacific(WNP) over the period of 1949-2013, and TCs' properties have been analyzed and compared in different aspects. The calculation results of coefficient variation(CV) and Nash-Sutcliffe efficiency(NSE) reveal a high level of intra-cluster cohesiveness and inter-cluster divergence, which means that the physical index system could serve as a feasible method of TCs classification. The clusters are then analyzed in terms of trajectory, lifespan, seasonality, trend,intensity and Power Dissipation Index(PDI). The five classified clusters show distinct features in TCs' temporal and spatial development discipline. Moreover, each cluster has its individual motion pattern, variation trend, influence region and impact degree.
基金supported by National Major Projects (No. 2015ZX03001013-002)National Natural Science Foundation of China (No. 61173149)+1 种基金Beijing Higher Education Young Elite Teacher ProjectFundamental Research Funds for the Central Universities
文摘The Virtual Machine(VM) placement is a serious problem to limit the improvement of resource utilization of data center. The VM traffic bandwidth demand is a Non zero-sum resource that the global traffic sum is relative with each VM placement position. In this paper, we introduce a new improved traffic constant algorithm in the data center, called Degree and Weighted Maximum Traffic Ratio(DWMTR). The proposal DWMTR algorithm defines a new weighted ratio parameter in this paper. The main body of the parameter is constructed with the ratio, current overall intra-cluster traffic divided by current overall inter-cluster traffic, when a new VM places in the data center. The DWMTR algorithm has the ability to constraint the inter-cluster traffic incensement more strictly than the current VM placement algorithms based on traffic bandwidth allocation. For this algorithm based on the theoretical analysis and simulation, it confirms the proposed DWMTR possesses smaller global interactive traffic cost than the control group algorithms in the appointed VM placement in the three-layer data center model.
文摘Wireless sensor networks are energy constraint networks. Energy efficiency, to prolong the network for a longer time is critical issue for wireless sensor network protocols. Clustering protocols are energy efficient approaches to extend the lifetime of network. Intra-cluster communication is the main driving factor for energy efficiency of clustering protocols. Intra-cluster energy consumption depends upon the position of cluster head in the cluster. Wrongly positioned clusters head make cluster more energy consuming. In this paper, a simple and efficient cluster head selection scheme is proposed, named Smart Cluster Head Selection (SCHS). It can be implemented with any distributed clustering approach. In SCHS, the area is divided into two parts: border area and inner area. Only inner area nodes are eligible for cluster head role. SCHS reduces the intra-cluster communication distance hence improves the energy efficiency of cluster. The simulation results show that SCHS has significant improvement over LEACH in terms of lifetime of network and data units gathered at base station.
文摘For many clustering algorithms,it is very important to determine an appropriate number of clusters,which is called cluster validity problem.In this paper,a new clustering validity assessment index is proposed based on a novel method to select the margin point between two clusters for in-ter-cluster similarity more accurately,and provides an improved scatter function for intra-cluster similarity.Simulation results show the effectiveness of the proposed index on the data sets under consideration regardless of the choice of a clustering algorithm.
文摘Background: Factors associated with hospital mortality are usually identified and their effects are quantified through statistical modeling. To guide the choice of the best statistical model, we first quantify the predictive ability of each model and then use the CIHI index to see if the hospital policy needs any change. Objectives: The main purpose of this study compared three statistical models in the evaluation of the association between hospital mortality and two risk factors, namely subject’s age at admission and the length of stay, adjusting for the effect of Diagnostic Related Groups (DRG). Methods: We use several SAS procedures to quantify the effect of DRG on the variability in hospital mortality. These procedures are the Logistic Regression model (ignoring the DRG effect), the Generalized Estimating Equation (GEE) that takes into account the within DRG clustering effect (but the within cluster correlation is treated as nuisance parameter), and the Generalized Linear Mixed Model (GLIMMIX). We showed that the GLIMMIX is superior to other models as it properly accounts for the clustering effect of “Diagnostic Related Groups” denoted by DRG. Results: The GLM procedure showed that the proportional contribution of DRG is 16%. All three models showed significant and increasing trend in mortality (P < 0.0001) with respect to the two risk factors (age at admission, and hospital length of stay). It was also clear that the CIHI index was not different under the three models. We re-estimated the models parameters after dichotomizing the risk factors at the optimal cut-off points, using the ROC curve. The parameters estimates and their significance did not change.
基金Supported by the National Natural Science Foundation of China under Grant No.60573132(国家自然科学基金)the National Grand Fundamental Research973Program of China under Grant No.2006CB303000(国家重点基础研究发展规划(973))the Hong Kong Polytechnic University under Grant No.A-PF77(香港理工大学)
文摘提出了一种基于簇结构的无线传感器网络数据收集协议EADEEG(an energy-aware data gathering protocol for wireless sensor networks).EADEEG通过最小化网络通信开销以及良好的能量负载平衡方法,可以有效地延长网络寿命.与以前的相关研究相比,EADEEG采用了一种全新的簇头竞争参数,能够更好地解决节点能量异构问题.此外,EADEEG也采用了一种简单而有效的簇内节点调度算法,通过控制活动节点的密度,可以在不增加额外控制开销的条件下关闭冗余节点并保证覆盖要求,因此可以进一步延长网络寿命.模拟实验证明,在节点初始能量同构和异构两种情况下,EADEEG协议都能够满足用户对覆盖率的要求,并在网络寿命上大幅度优于LEACH(low energy adaptive clustering hierarchy),PEGASIS(power-efficient gathering in sensor information systems)和DEEG(distributed energy-efficient data gathering and aggregation protocol)协议.
文摘为改善超密集网络中的内容分发效率,提出一种基于协作缓存的内容分发机制。在对小小区基站(Small-cell Base Station,SBS)进行分簇的基础上,由每个簇自主决定内容如何在簇内的SBS进行缓存;其次,在处理用户内容请求时,不仅考虑了簇内协作,还考虑了簇间协作,以进一步降低宏基站和移动核心网的负载。仿真结果表明,与无网内缓存以及只有簇内协作的场景相比,该方法可以有效降低内容传输时延,提高内容分发性能。
