Improved Load-Balanced Clustering for Energy-Aware Routing(ILBC-EAR)in WSNs

Sensors are considered as important elements of electronic devices.In many applications and service,Wireless Sensor Networks(WSNs)are involved in significant data sharing that are delivered to the sink node in energy efficient man-ner using multi-hop communications.But,the major challenge in WSN is the nodes are having limited battery resources,it is important to monitor the consumption rate of energy is very much needed.However,reducing energy con-sumption can increase the network lifetime in effective manner.For that,clustering methods are widely used for optimizing the rate of energy consumption among the sensor nodes.In that concern,this paper involves in deriving a novel model called Improved Load-Balanced Clustering for Energy-Aware Routing(ILBC-EAR),which mainly concentrates on optimal energy utilization with load-balanced process among cluster heads and member nodes.For providing equal rate of energy consumption among nodes,the dimensions of framed clusters are measured.Moreover,the model develops a Finest Routing Scheme based on Load-Balanced Clustering to transmit the sensed information to the sink or base station.The evaluation results depict that the derived energy aware model attains higher rate of life time than other works and also achieves balanced energy rate among head node.Additionally,the model also provides higher throughput and minimal delay in delivering data packets.

Electric Load Clustering in Smart Grid:Methodologies,Applications,and Future Trends

With the increasingly widespread of advanced metering infrastructure,electric load clustering is becoming more essential for its great potential in analytics of consumers’energy consumption patterns and preference through data mining.Moreover,a variety of electric load clustering techniques have been put into practice to obtain the distribution of load data,observe the characteristics of load clusters,and classify the components of the total load.This can give rise to the development of related techniques and research in the smart grid,such as demand-side response.This paper summarizes the basic concepts and the general process in electric load clustering.Several similarity measurements and five major categories in electric load clustering are then comprehensively summarized along with their advantages and disadvantages.Afterwards,eight indices widely used to evaluate the validity of electric load clustering are described.Finally,vital applications are discussed thoroughly along with future trends including the tariff design,anomaly detection,load forecasting,data security and big data,etc.

Finite-difference model of land subsidence caused by cluster loads in Zhengzhou,China

Groundwater exploitation has been regarded as the main reason for land subsidence in China and thus receives considerable attention from the government and the academic community.Recently,building loads have been identified as another important factor of land subsidence,but researches in this sector have lagged.The effect of a single building load on land subsidence was neglected in many cases owing to the narrow scope and the limited depth of the additional stress in stratum.However,due to the superposition of stresses between buildings,the additional stress of cluster loads is greater than that of a single building load under the same condition,so that the land subsidence caused by cluster loads cannot be neglected.Taking Shamen village in the north of Zhengzhou,China,as an example,a finite-difference model based on the Biot consolidation theory to calculate the land subsidence caused by cluster loads was established in this paper.Cluster loads present the characteristics of large-area loads,and the land subsidence caused by cluster loads can have multiple primary consolidation processes due to the stress superposition of different buildings was shown by the simulation results.Pore water migration distances are longer when the cluster loads with high plot ratio are imposed,so that consolidation takes longer time.The higher the plot ratio is,the deeper the effective deformation is,and thus the greater the land subsidence is.A higher plot ratio also increases the contribution that the deeper stratigraphic layers make to land subsidence.Contrary to the calculated results of land subsidence caused by cluster loads and groundwater recession,the percentage of settlement caused by cluster loads in the total settlement was 49.43%and 55.06%at two simulated monitoring points,respectively.These data suggest that the cluster loads can be one of the main causes of land subsidence.

A New Load Balancing Scheme on Heterogeneous Database Cluster

In heterogeneous database cluster, the performance of load balancing is closely related to the computing capabilities of heterogeneous nodes and the different types of workloads. Thus, a method is introduced to evaluate the load status of nodes by the weighted load values with consideration of both the utilization of different resources and the workload types in a load balancer and an efficient and dynamic load balancing scheme is proposed for OLTP(online transaction processing) workloads to maximize the utilization of distributed resources and achieve better performance, which need not collect the feedback of load information from the lower nodes and effectively keeps from the data skew. The simulation results for OLTP services gained by TPC-C tool show that the dynamic weighted balancing policy leads to sub-linear throughput speedup and keeps the heterogeneous cluster well balanced.

A Load Balance Clustering Routing Algorism Based on SOM

In order to solve the uneven node load in the tradition clustering routing protocols, a new clustering algorism based on SOM is proposed. Firstly, the network radio model and the energy consumption model are defined. A new algorism using SOM to form the cluster and select the cluster head is defined. In the clustering node remain energy and the Euclidean distance from cluster head to the cluster member are considered. The experiment shows our method has the longer life cycle and less total energy consumption. It is an effective clustering protocol.

Substation clustering based on improved KFCM algorithm with adaptive optimal clustering number selection

The premise and basis of load modeling are substation load composition inquiries and cluster analyses.However,the traditional kernel fuzzy C-means(KFCM)algorithm is limited by artificial clustering number selection and its convergence to local optimal solutions.To overcome these limitations,an improved KFCM algorithm with adaptive optimal clustering number selection is proposed in this paper.This algorithm optimizes the KFCM algorithm by combining the powerful global search ability of genetic algorithm and the robust local search ability of simulated annealing algorithm.The improved KFCM algorithm adaptively determines the ideal number of clusters using the clustering evaluation index ratio.Compared with the traditional KFCM algorithm,the enhanced KFCM algorithm has robust clustering and comprehensive abilities,enabling the efficient convergence to the global optimal solution.

A Coverage-Aware Unequal Clustering Protocol with Load Separation for Ambient Assisted Living Based on Wireless Sensor Networks

Ambient Assisted Living(AAL) is becoming an important research field. Many technologies have emerged related with pervasive computing vision, which can give support for AAL. One of the most reliable approaches is based on wireless sensor networks(WSNs). In this paper, we propose a coverage-aware unequal clustering protocol with load separation(CUCPLS) for data gathering of AAL applications based on WSNs. Firstly, the coverage overlap factor for nodes is introduced that accounts for the degree of target nodes covered. In addition, to balance the intra-cluster and inter-cluster energy consumptions, different competition radiuses of CHs are computed theoretically in different rings, and smaller clusters are formed near the sink. Moreover, two CHs are selected in each cluster for load separation to alleviate the substantial energy consumption difference between a single CH and its member nodes. Furthermore, a backoff waiting time is adopted during the selection of the two CHs to reduce the number of control messages employed. Simulation results demonstrate that the CUCPLS not only can achieve better coverage performance, but also balance the energy consumption of a network and prolong network lifetime.

A LIMITED RESOURCE VECTOR LOAD-BALANCING ALGORITHM FOR SOFTSWITCH-BASED HETEROGENEOUS CLUSTERED MEDIA SERVER

Based on the system feature of softswitch-based heterogeneous clustered media server, this paper proposed a limited resource vector load-balancing algorithm. The purpose of the algorithm was to balance the load of clusters by utilizing all system resources effectively and to avoid violent shaking of the system per- formance. A lot of simulations on the Petri net model of load balance system are conducted and the algorithm is compared with some traditional algorithms on balancing ability for heterogeneity, system throughput, re- quest response time and performance stability. The results of simulations show that the algorithm achieves system higher performance and it has excellent ability to deal with the heterogeneity of clustered media server.

Research on Dynamic Clustering Routing Considering Node Load for Wireless Sensor Networks

Aiming at the problem that node load is rarely considered in existing clustering routing algorithm for Wireless Sensor Networks (WSNs), a dynamic clustering routing algorithm for WSN is presented in this paper called DCRCL (Dynamic Clustering Routing Considering Load). This algorithm is comprised of three phases including cluster head (CH) selection, cluster setup and inter-cluster routing. First, the CHs are selected based on residual energy and node load. Then the non-CH nodes choose a cluster by comparing the cost function of its neighbor CHs. At last, each CH communicates with base station by using multi-hop communication. The simulation results show that comparing with the existing one, the techniques life cycle and date volume of the network are increased by 30.7 percent and 29.8 percent respectively by using the proposed algorithm DCRCL.

Load-Balance Policy in Two Level-Cluster File System

In this paper, we explored a load-balancing algorithm in a cluster file system contains two levels of metadata-server, primary-level server quickly distributestasks to second-level servers depending on the closest load-balancing information. At the same time, we explored a method which accurately reflect I/O traffic and storage of storage-node： computing the heat-value of file, according to which we realized a more logical storage allocation. According to the experiment result, we conclude that this new algorithm shortens the executing time of tasks and improves the system performance compared with other load algorithm.

Energy-balanced clustering protocol for data gathering in wireless sensor networks with unbalanced traffic load

Energy-efficient data gathering in multi-hop wireless sensor networks was studied,considering that different node produces different amounts of data in realistic environments.A novel dominating set based clustering protocol (DSCP) was proposed to solve the data gathering problem in this scenario.In DSCP,a node evaluates the potential lifetime of the network (from its local point of view) assuming that it acts as the cluster head,and claims to be a tentative cluster head if it maximizes the potential lifetime.When evaluating the potential lifetime of the network,a node considers not only its remaining energy,but also other factors including its traffic load,the number of its neighbors,and the traffic loads of its neighbors.A tentative cluster head becomes a final cluster head with a probability inversely proportional to the number of tentative cluster heads that cover its neighbors.The protocol can terminate in O(n/lg n) steps,and its total message complexity is O(n2/lg n).Simulation results show that DSCP can effectively prolong the lifetime of the network in multi-hop networks with unbalanced traffic load.Compared with EECT,the network lifetime is prolonged by 56.6% in average.

计及P2G的综合能源系统CCUS容量规划

考虑到碳捕集利用与封存(CCUS)是对电转气(P2G)过程中二氧化碳(CO_(2))来源的细化,文中提出对综合能源系统(IES)进行电-氢-氧-甲烷-二氧化碳和虚拟二氧化碳等P2G过程的定性定量建模,以及对风-光-电动汽车-负荷等随机源荷进行时空聚类以深度挖掘IES氢源碳源潜力的CCUS设备容量规划模型。以阶梯正负碳交易、弃风弃光惩罚和年设备投资成本等的年综合成本最低为目标,以碳捕集、碳存储和甲烷化等CCUS设备功率容量限值为全局变量对电气平衡和能量耦合进行二次约束,经过混合整数线性化处理后利用Cplex求解。算例分析表明,源荷时空聚类可在典型日场景缩减时保留IES廉价碳源氢源特征,合理规划后的CCUS设备可兼具弃风消纳和碳减排功能,使P2G制取的天然气更具市场竞争力。

Implementation Study of Dynamic Load Balancing Algorithm of Parallel Tree Computation on Clusters of Heterogeneous Workstation

The rapid growth of interconnected high performance workstations has produced a new computing paradigm called clustered of workstations computing. In these systems load balance problem is a serious impediment to achieve good performance. The main concern of this paper is the implementation of dynamic load balancing algorithm, asynchronous Round Robin (ARR), for balancing workload of parallel tree computation depth-first-search algorithm on Cluster of Heterogeneous Workstations (COW) Many algorithms in artificial intelligence and other areas of computer science are based on depth first search in implicitty defined trees. For these algorithms a load-balancing scheme is required, which is able to evenly distribute parts of an irregularly shaped tree over the workstations with minimal interprocessor communication and without prior knowledge of the tree’s shape. For the (ARR) algorithm only minimal interprocessor communication is needed when necessary and it runs under the MPI (Message passing interface) that allows parallel execution on heterogeneous SUN cluster of workstation platform. The program code is written in C language and executed under UNIX operating system (Solaris version).

基于VMD和时空网络变分自编码器的负荷聚类

为了解决用户用电负荷曲线数据维度高、特征提取困难以及序列存在信号模态混叠的问题,提出了使用变分模态分解(variational modal decomposition,VMD)和改进基于时空网络的变分自编码器(variational auto-encoder,VAE)对电力负荷曲线进行特征提取。通过模态分解得到信号的固有模态,对模态重构得到时序特征较明显的序列信号。再通过长短期记忆网络(long short-term memory network,LSTM)和卷积网络(convolutional neural network,CNN)组成的时空变分自编码器进行潜在特征提取,并构建网络分类器来联合损失优化自编码器模型。最后使用Minibatchkmeans算法聚类并计算聚类中心。使用UCI数据集中葡萄牙居民用电量作为实验数据,通过实验结果表明经模态分解后通过降维再聚类的算法在戴维斯丁堡指数(Davies-Bouldin index,DBI)和轮廓系数(silhouette coefficient,SC)上表现出较好效果。

LOW-COST HIGH PERFORMANCE CLUSTER OF WORK-STATIONS BASED ON DYNAMIC LOAD BALANCING FOR PARALLEL DEPTH-FIRST SEARCH(DFS)

This paper presented an idea to replace the traditionally expensive parallel machines by heterogeneous cluster of workstations. To emphasise the usability of cluster of workstations platform for parallel and distributed computing, also the paper presented the status report on the effort and experiences for the implementation of a dynamic load balancing for parallel tree computation depth first search(DFS) on the cluster of a workstations project. It compared the speedup performance obtained from our platform with that obtained from the traditional one. The speedup results show that cluster of workstations can be a serious alternative to the expensive parallel machines.

基于画像的工业园区需求响应潜力评估

需求响应潜力的准确评估是电网调度部门制定科学合理调度计划的数据支撑,对于需求响应实施过程中涉及的各个利益主体均有重要意义。由于工业负荷用电量大且用电规律,其可挖掘的调节潜力巨大,是最理想的需求响应资源之一。首先,针对典型可调节用户,聚类分析其主要用电计划类型,并根据最大相关最小冗余准则对各用电计划进行画像;然后,以典型可调节用户不同用电计划的数据集作为训练集进行负荷超短期预测,得到典型用户在调度时段的可调节容量;最后,基于所提出的园区响应潜力评估框架,根据用电行为聚类确定各工厂所属典型可调节负荷类别,并根据各类型可调节负荷占比得到园区内对应类型负荷的可调节容量,进而得到园区总体的调节潜力。仿真结果表明,所提评估方法可分析出园区内各类可调节负荷的调节潜力,并结合园区总调节潜力的实际数据验证了评估方法的有效性。

空调温控负荷集群参与光伏消纳的潜力评估与互动框架

空调温控负荷集群作为当下最具调节潜力的需求侧响应资源之一,在削峰、填谷、分布式光伏消纳和电网调控中将发挥重要作用。因此,提出一种电力市场环境下,基于数据驱动和深度置信网络的空调温控负荷集群参与分布式光伏消纳的可调节潜力评估与互动框架。首先,利用数据驱动构建了基于深度置信网络的可调节潜力评估模型,实时输出温控负荷集群的可调节潜力;其次,考虑功率调整量在一定范围内变化的前提下,构建基于深度置信网络的需求互动模型,对温控负荷集群进行实时温度调控。最后,以冀北地区某10kV馈线作为实际算例进行分析,结果表明:所提框架能够充分利用空调温控负荷集群的可调节潜力,参与分布式光伏的消纳。

工业用户连续参与需求响应的用户基线负荷精准计算方法

提出了一种将K-means聚类分析与长短期记忆神经网络算法结合,通过工业同源组信息进行迁移学习优化的计算方法。该方法实现了对长时间连续参与需求响应的工业用户基线负荷的精准计算,提高了工业用户需求响应效果评价的准确性。通过城市级虚拟电厂平台采集的参与需求响应实践的工业用户电力负荷数据,验证了该方法的有效性。

基于改进哈里斯鹰优化算法的配电网动态重构

针对风光荷不确定性的配电网重构问题,建立分布式电源和负荷出力模型,以系统运行成本和电压偏移构建多目标函数。提出一种改进粒子群算法融合K-means(improved particle swarm optimization and K-means,IPSO-Kmeans)聚类算法来划分典型日负荷曲线,将改进哈里斯鹰优化(improved Harris hawk optimization,IHHO)算法应用于配电网重构,进行寻优计算。为了改善哈里斯鹰优化(Harris hawk optimization,HHO)算法种群分布不均、无法完整搜索到最优解空间范围、易于陷入局部收敛等问题,引入佳点集生成种群初始化,提高种群搜索空间的均匀性。将麻雀搜索算法中的探索者位置更新公式与哈里斯鹰优化算法探索阶段的位置更新公式结合,以提高算法的全局搜索能力。利用柯西-高斯变异扰动策略跳出局部最优解。最后在IEEE33节点配网系统仿真,结果表明所提方法的有效性。

Kubernetes资源调度策略

在Kubernetes中可以通过nodeSelector在不同节点上部署不同的应用,但是当指定节点资源不足时,应用将会处于无法被调度的状态。针对这个问题,提出一种Kubernetes资源调度策略,设计实现一个自定义调度器,在指定节点部署应用资源不足时,将等待调度的应用调度到Kubernetes集群中的其它节点上。实验结果表明,该自定义调度器的调度策略能够在指定部署节点资源不足时将等待调度的应用调度到其它节点上,提高了集群资源利用率和集群负载均衡,降低了用户应用等待调度时间。