Directional data traffic and the total power consumption of a cluster are usually overlooked in traditional energy-efficient clustering algorithms for homogeneous sensor networks. This paper proposes to balance power ...
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ISBN:
(纸本)9781424413119
Directional data traffic and the total power consumption of a cluster are usually overlooked in traditional energy-efficient clustering algorithms for homogeneous sensor networks. This paper proposes to balance power consumption throughout the network and to reduce the total power consumption of the cluster. The clusters are organized in such a way that their lifetimes are equalized by making the total power consumption proportional to the energy stored. Furthermore, the clusterheads are maintained at the centre of the cluster without re-organizing the clusters to reduce the total power consumption of the cluster. Performance evaluation shows that the proposed clustering algorithm indeed improves energy efficiency for sensor networks.
Considering the problem of autonomous course-tracking of Unmanned Surface Vessel (USV) with uncertain systems, this paper put forwards a control method based on the clustering algorithm, The designed controller is com...
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ISBN:
(纸本)9789881563958
Considering the problem of autonomous course-tracking of Unmanned Surface Vessel (USV) with uncertain systems, this paper put forwards a control method based on the clustering algorithm, The designed controller is composed of a clustering algorithm controller and a PID controller. Because of the uncertainty of the system, the clustering algorithm analysis controller online learned and classified the expected course to control the rudder, the PID controller adjusts the error. This method solves the problems of low control accuracy and poor control performance of robustness brought by the single system controller, By applying the proposed control method to the control design of USV autonomous course-tracking, the results of the simulation experiment show the effectiveness of the control method.
As the division of labor in the industry becomes more refined, an increasing number of companies are abandoning infrastructure construction and instead moving their operations to cloud data centers (CBDCs). Cloud serv...
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As the division of labor in the industry becomes more refined, an increasing number of companies are abandoning infrastructure construction and instead moving their operations to cloud data centers (CBDCs). Cloud service providers are responding to the surge in demand by deploying their own CBDCs worldwide. However, the energy consumption and operation costs of these CBDCs vary depending on the region's environment and policies. To mitigate these costs, cloud service providers often employ resource management algorithms. This article conducts a comprehensive analysis of the cross-regional CBDC model, including establishing virtual machine classification rules based on clustering results. Ultimately, this article proposes a low-energy resource classification algorithm for cross-regional CBDCs based on the K-means clustering algorithm (LCKC). The effectiveness of the LCKC algorithm is compared to that of other algorithms, and the results indicate that it reduces energy consumption in cross-regional CBDCs.
The goals of wireless sensor networks (WSNs) are to sense and collect data and to transmit the information to a sink. Because the sensor nodes are typically battery powered, the main challenges in WSNs are to optimise...
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The goals of wireless sensor networks (WSNs) are to sense and collect data and to transmit the information to a sink. Because the sensor nodes are typically battery powered, the main challenges in WSNs are to optimise the energy consumption and to prolong the network lifetime. This paper proposes a centralised clustering algorithm termed the minimum distance clustering algorithm that is based on an improved differential evolution (MD-IDE). The new algorithm combines the advantages of simulated annealing and differential evolution to determine the cluster heads (CHs) for minimising the communication distance of the WSN. Many simulation results demonstrate that the performance of MD-IDE outperforms other well-known protocols, including the low-energy adaptive clustering hierarchy (LEACH) and LEACH-C algorithms, in the aspects of reducing the communication distance of the WSN for reducing energy consumption.
This paper demonstrates the convergence of model-based statistics from multiple simulated realizations. Theoretically, the convergence of realization statistics is guaranteed over the number of realizations that are i...
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This paper demonstrates the convergence of model-based statistics from multiple simulated realizations. Theoretically, the convergence of realization statistics is guaranteed over the number of realizations that are independent among themselves. The rate at which realization-based statistics converges with model-based statistics is important and must be assessed. However, due to poor selection of the random number generator, the generated realization might be far from mutual independence. We use the k-means clustering algorithm to select nearly independent realizations from a set of realization models. We apply the proposed algorithm to a coastal erosion problem in Alaska to estimate the amount of gravel.
作者：
Modak, SoumitaUniv Calcutta
Fac Stat Dept Stat Basanti Devi Coll 147B Rash Behari Ave Kolkata 700029 India
A novel nonparametric clustering algorithm is proposed using the interpoint distances between the members of the data to reveal the inherent clustering structure existing in the given set of data, where we apply the c...
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A novel nonparametric clustering algorithm is proposed using the interpoint distances between the members of the data to reveal the inherent clustering structure existing in the given set of data, where we apply the classical nonparametric univariate kernel density estimation method to the interpoint distances to estimate the density around a data member. Our clustering algorithm is simple in its formation and easy to apply resulting in well-defined clusters. The algorithm starts with objective selection of the initial cluster representative and always converges independently of this choice. The method finds the number of clusters itself and can be used irrespective of the nature of underlying data by using an appropriate interpoint distance measure. The cluster analysis can be carried out in any dimensional space with viability to high-dimensional use. The distributions of the data or their interpoint distances are not required to be known due to the design of our procedure, except the assumption that the interpoint distances possess a density function. Data study shows its effectiveness and superiority over the widely used clustering algorithms.
As data mining having attracted a significant amount of research attention, many clustering algorithms have been proposed in the past decades. However, most of existing clustering methods have high computational time ...
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As data mining having attracted a significant amount of research attention, many clustering algorithms have been proposed in the past decades. However, most of existing clustering methods have high computational time or are not suitable for discovering clusters with non-convex shape. In this paper, an efficient clustering algorithm CHSMST is proposed, which is based on clustering based on hyper surface (CHS) and minimum spanning tree. In the first step, CHSMST applies CHS to obtain initial clusters immediately. Thereafter, minimum spanning tree is introduced to handle locally dense data which is hard for CHS to deal with. The experiments show that CHSMST can discover clusters with arbitrary shape. Moreover, CHSMST is insensitive to the order of input samples and the run time of the algorithm increases moderately as the scale of dataset becomes large.
Fuzzy clustering is superior to crisp clustering when the boundaries among the clusters are vague and ambiguous. However, the main limitation of both fuzzy and crisp clustering algorithms is their sensitivity to the n...
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Fuzzy clustering is superior to crisp clustering when the boundaries among the clusters are vague and ambiguous. However, the main limitation of both fuzzy and crisp clustering algorithms is their sensitivity to the number of potential clusters and/or their initial positions. Moreover, the comprehensibility of obtained clusters is not expertized, whereupon in data-mining applications, the discovered knowledge is not understandable for human users. To overcome these restrictions, a novel fuzzy rule-based clustering algorithm (FRBC) is proposed in this paper. Like fuzzy rule-based classifiers, the FRBC employs a supervised classification approach to do the unsupervised cluster analysis. It tries to automatically explore the potential clusters in the data patterns and identify them with some interpretable fuzzy rules. Simultaneous classification of data patterns with these fuzzy rules can reveal the actual boundaries of the clusters. To illustrate the capability of FRBC to explore the clusters in data, the experimental results on some benchmark datasets are obtained and compared with other fuzzy clustering algorithms. The clusters specified by fuzzy rules are human understandable with acceptable accuracy.
LEACH class clustering algorithms have been widely used in the topology control of mobile sensor networks because of their simplicity, practicality and good energy efficiency. Aiming at the common problems of LEACH cl...
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LEACH class clustering algorithms have been widely used in the topology control of mobile sensor networks because of their simplicity, practicality and good energy efficiency. Aiming at the common problems of LEACH clustering algorithm, such as large energy consumption of nodes, short network life cycle, easy loss of data transmission, etc., according to the set mobile sensor network model, based on LEACH-Mobile algorithm, a low-energy clustering algorithm LEACH-MII is designed. LEACH-MII clustering algorithm assigns tasks according to the working conditions of sensor nodes, combines the activity, location and energy consumption of nodes to select and rotate cluster heads;and uses dynamic time slot allocation mechanism to avoid time slot reallocation caused by frequent join-cluster and leave-cluster of sensor nodes, so as to ensure the stability and connectivity of the network structure, reduce the energy consumption of nodes, and extend the life cycle of the network. In order to verify the feasibility and effectiveness of LEACH-MII clustering algorithm, the simulation experiment of LEACH-MII clustering algorithm is carried out. The simulation results show that The data transmission efficiency, average node energy consumption and network life cycle of LEACH-MII network are 30%, 20% and 15% better than LEACH-Mobile network, respectively. In addition, the performance of LEACH-MII network is better than that of LEACH-Mobile network, such as the load balance degree, the scalability of network monitoring range, and the speed effect of network life cycle.
As one of the most important techniques in data mining, cluster analysis has attracted more and more attentions in this big data era. Most clustering algorithms have encountered with challenges including cluster cente...
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As one of the most important techniques in data mining, cluster analysis has attracted more and more attentions in this big data era. Most clustering algorithms have encountered with challenges including cluster centers determination difficulty, low clustering accuracy, uneven clustering efficiency of different data sets and sensible parameter dependence. Aiming at clustering center determination difficulty and parameter dependence, a novel cluster center fast determination clustering algorithm was proposed in this paper. It is supposed that clustering centers are those data points with higher density and larger distance from other data points of higher density. Normal distribution curves are designed to fit the density distribution curve of density distance product. And the singular points outside the confidence interval by setting the confidence interval are proved to be clustering centers by theory analysis and simulations. Finally, according to these clustering centers, a time scan clustering is designed for the rest of the points by density to complete the clustering. Density radius is a sensible parameter in calculating density for each data point, mountain climbing algorithm is thus used to realize self-adaptive density radius. Abundant typical benchmark data sets are testified to evaluate the performance of the brought up algorithms compared with other clustering algorithms in both aspects of clustering quality and time complexity. (C) 2017 Published by Elsevier B.V.
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