In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,etc.At the same time,proper localization...In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,etc.At the same time,proper localization of nodes in real time wireless networks helps to improve the overall functioning of networks.This study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization(IM-EECNL)approach for real-time wireless networks.The proposed IM-EECNL technique involves two major processes namely node localization and clustering.Firstly,Chaotic Water Strider Algorithm based Node Localization(CWSANL)technique to determine the unknown position of the nodes.Secondly,an Oppositional Archimedes Optimization Algorithm based Clustering(OAOAC)technique is applied to accomplish energy efficiency in the network.Besides,the OAOAC technique derives afitness function comprising residual energy,distance to cluster heads(CHs),distance to base station(BS),and load.The performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency.A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio(PDR)of 0.985.展开更多
Wireless Sensor Networks(WSNs)are a major element of Internet of Things(IoT)networks which offer seamless sensing and wireless connectivity.Disaster management in smart cities can be considered as a safety critical ap...Wireless Sensor Networks(WSNs)are a major element of Internet of Things(IoT)networks which offer seamless sensing and wireless connectivity.Disaster management in smart cities can be considered as a safety critical application.Therefore,it becomes essential in ensuring network accessibility by improving the lifetime of IoT assisted WSN.Clustering and multihop routing are considered beneficial solutions to accomplish energy efficiency in IoT networks.This article designs an IoT enabled energy aware metaheuristic clustering with routing protocol for real time disaster management(EAMCR-RTDM).The proposed EAMCR-RTDM technique mainly intends to manage the energy utilization of nodes with the consideration of the features of the disaster region.To achieve this,EAMCR-RTDM technique primarily designs a yellow saddle goatfish based clustering(YSGF-C)technique to elect cluster heads(CHs)and organize clusters.In addition,enhanced cockroach swarm optimization(ECSO)based multihop routing(ECSO-MHR)approach was derived for optimal route selection.The YSGF-C and ECSO-MHR techniques compute fitness functions using different input variables for achieving improved energy efficiency and network lifetime.The design of YSGF-C and ECSO-MHR techniques for disaster management in IoT networks shows the novelty of the work.For examining the improved outcomes of the EAMCR-RTDM system,a wide range of simulations were performed and the extensive results are assessed in terms of different measures.The comparative outcomes highlighted the enhanced outcomes of the EAMCRRTDM algorithm over the existing approaches.展开更多
Oracle Maximum Availability Architecture(MAA)是基于Oracle的整套最高可用性(HA)容灾方案。MAA将提供最优方法的建议让用户能够通过多种oracle数据库工具的配合实现系统可用性的最大化。其可扩展性和健壮性都达到了很高的标准。一套...Oracle Maximum Availability Architecture(MAA)是基于Oracle的整套最高可用性(HA)容灾方案。MAA将提供最优方法的建议让用户能够通过多种oracle数据库工具的配合实现系统可用性的最大化。其可扩展性和健壮性都达到了很高的标准。一套完善的MAA可以实现服务器级别的本地容灾、异地容灾、在线容灾和离线容灾,使数据库服务器在遭到不可预知的自然或是人为破坏时仍然可以不间断地对外提供服务,从而保证了系统级别的高可用性。展开更多
在节能减排和激烈同行竞争的环境下,应用服务器集群的能耗与性能优化十分迫切.针对已有研究在性能指标和实时性方面的不足,提出一种集群能耗与性能实时优化方案.该方案结合采用线性加权法和主目标法优化集群功率与请求丢弃率这两个目标...在节能减排和激烈同行竞争的环境下,应用服务器集群的能耗与性能优化十分迫切.针对已有研究在性能指标和实时性方面的不足,提出一种集群能耗与性能实时优化方案.该方案结合采用线性加权法和主目标法优化集群功率与请求丢弃率这两个目标,将双目标优化转换成一个单目标约束优化.首先基于CPU频率等效连续调整模式下的服务器负载-功率模型,定义很少的变量将集群优化描述成混合整数二次规划问题,然后采用变量拆分和变量转换将其转化成混合整数线性规划(mixed integer linear programming,MILP)问题并引入特殊顺序集约束,最后采用Gurobi优化器求解该MILP.通过对CPU频率调整的进一步优化,大幅度减少了CPU频率的切换.多种场景下的测试表明,该方案的求解时间约在10 ms左右,特殊顺序集约束的引入使求解时间更为稳定,从而能够保证优化的实时进行.展开更多
具有噪声的基于密度的空间聚类(Density‑based spatial clustering of applications with noise,DBSCAN)能够发现不同密度和大小的类簇,对噪声也有很好的鲁棒性,被广泛地应用到数据挖掘的任务中。DBSCAN通常需要调整参数MinPts和Eps以...具有噪声的基于密度的空间聚类(Density‑based spatial clustering of applications with noise,DBSCAN)能够发现不同密度和大小的类簇,对噪声也有很好的鲁棒性,被广泛地应用到数据挖掘的任务中。DBSCAN通常需要调整参数MinPts和Eps以达到更优的聚类效果,但往往在搜索最优参数的过程中会影响DBSCAN的性能。本文从两个方面优化DBSCAN,一方面,提出一种无参的方法优化DBSCAN全局参数选择。无参方法利用自然最近邻获得数据集的自然特征值,并将自然特征值作为参数MinPts值。然后,根据自然特征值计算自然特征集合,利用自然特征集合中的数据分布特性,分别采取统计最小值、平均值和最大值3种方式得到Eps值。另一方面,采用集成数据科学实时加速平台(Real‑time acceleration platform for integrated data science,RAPIDS)的图形处理器(Graphics processing unit,GPU)计算加快DBSCAN算法的收敛速度。实验结果表明,本文提出的方法在优化DBSCAN参数选择的同时,取得了与密度峰值聚类(Density peaks clustering,DPC)相当的聚类结果。展开更多
基金supported by Ulsan Metropolitan City-ETRI joint cooperation project[21AS1600,Development of intelligent technology for key industriesautonomous human-mobile-space autonomous collaboration intelligence technology].
文摘In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,etc.At the same time,proper localization of nodes in real time wireless networks helps to improve the overall functioning of networks.This study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization(IM-EECNL)approach for real-time wireless networks.The proposed IM-EECNL technique involves two major processes namely node localization and clustering.Firstly,Chaotic Water Strider Algorithm based Node Localization(CWSANL)technique to determine the unknown position of the nodes.Secondly,an Oppositional Archimedes Optimization Algorithm based Clustering(OAOAC)technique is applied to accomplish energy efficiency in the network.Besides,the OAOAC technique derives afitness function comprising residual energy,distance to cluster heads(CHs),distance to base station(BS),and load.The performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency.A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio(PDR)of 0.985.
基金This research has been funded by Dirección General de Investigaciones of Universidad Santiago de Cali under call No.01–2021.
文摘Wireless Sensor Networks(WSNs)are a major element of Internet of Things(IoT)networks which offer seamless sensing and wireless connectivity.Disaster management in smart cities can be considered as a safety critical application.Therefore,it becomes essential in ensuring network accessibility by improving the lifetime of IoT assisted WSN.Clustering and multihop routing are considered beneficial solutions to accomplish energy efficiency in IoT networks.This article designs an IoT enabled energy aware metaheuristic clustering with routing protocol for real time disaster management(EAMCR-RTDM).The proposed EAMCR-RTDM technique mainly intends to manage the energy utilization of nodes with the consideration of the features of the disaster region.To achieve this,EAMCR-RTDM technique primarily designs a yellow saddle goatfish based clustering(YSGF-C)technique to elect cluster heads(CHs)and organize clusters.In addition,enhanced cockroach swarm optimization(ECSO)based multihop routing(ECSO-MHR)approach was derived for optimal route selection.The YSGF-C and ECSO-MHR techniques compute fitness functions using different input variables for achieving improved energy efficiency and network lifetime.The design of YSGF-C and ECSO-MHR techniques for disaster management in IoT networks shows the novelty of the work.For examining the improved outcomes of the EAMCR-RTDM system,a wide range of simulations were performed and the extensive results are assessed in terms of different measures.The comparative outcomes highlighted the enhanced outcomes of the EAMCRRTDM algorithm over the existing approaches.
文摘Oracle Maximum Availability Architecture(MAA)是基于Oracle的整套最高可用性(HA)容灾方案。MAA将提供最优方法的建议让用户能够通过多种oracle数据库工具的配合实现系统可用性的最大化。其可扩展性和健壮性都达到了很高的标准。一套完善的MAA可以实现服务器级别的本地容灾、异地容灾、在线容灾和离线容灾,使数据库服务器在遭到不可预知的自然或是人为破坏时仍然可以不间断地对外提供服务,从而保证了系统级别的高可用性。
文摘在节能减排和激烈同行竞争的环境下,应用服务器集群的能耗与性能优化十分迫切.针对已有研究在性能指标和实时性方面的不足,提出一种集群能耗与性能实时优化方案.该方案结合采用线性加权法和主目标法优化集群功率与请求丢弃率这两个目标,将双目标优化转换成一个单目标约束优化.首先基于CPU频率等效连续调整模式下的服务器负载-功率模型,定义很少的变量将集群优化描述成混合整数二次规划问题,然后采用变量拆分和变量转换将其转化成混合整数线性规划(mixed integer linear programming,MILP)问题并引入特殊顺序集约束,最后采用Gurobi优化器求解该MILP.通过对CPU频率调整的进一步优化,大幅度减少了CPU频率的切换.多种场景下的测试表明,该方案的求解时间约在10 ms左右,特殊顺序集约束的引入使求解时间更为稳定,从而能够保证优化的实时进行.
文摘具有噪声的基于密度的空间聚类(Density‑based spatial clustering of applications with noise,DBSCAN)能够发现不同密度和大小的类簇,对噪声也有很好的鲁棒性,被广泛地应用到数据挖掘的任务中。DBSCAN通常需要调整参数MinPts和Eps以达到更优的聚类效果,但往往在搜索最优参数的过程中会影响DBSCAN的性能。本文从两个方面优化DBSCAN,一方面,提出一种无参的方法优化DBSCAN全局参数选择。无参方法利用自然最近邻获得数据集的自然特征值,并将自然特征值作为参数MinPts值。然后,根据自然特征值计算自然特征集合,利用自然特征集合中的数据分布特性,分别采取统计最小值、平均值和最大值3种方式得到Eps值。另一方面,采用集成数据科学实时加速平台(Real‑time acceleration platform for integrated data science,RAPIDS)的图形处理器(Graphics processing unit,GPU)计算加快DBSCAN算法的收敛速度。实验结果表明,本文提出的方法在优化DBSCAN参数选择的同时,取得了与密度峰值聚类(Density peaks clustering,DPC)相当的聚类结果。
