This paper proposes a robust Criticality-based clustering algorithm (CCA) for Vehicular Ad Hoc NETworks (VANETs) based on the concept of network criticality. Network criticality is a global metric on an undirected gra...
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ISBN:
(纸本)9781457720536
This paper proposes a robust Criticality-based clustering algorithm (CCA) for Vehicular Ad Hoc NETworks (VANETs) based on the concept of network criticality. Network criticality is a global metric on an undirected graph, that quantifies the robustness of the graph against environmental changes such as topology. In this paper, we localize the notion of network criticality and apply it to control cluster formation in the vehicular wireless network. We use the localized notion of node criticality together with a universal link measure, Link Expiration Time (LET), to derive a distributed multi-hop clustering algorithm for VANETs. Simulation results show that the proposed CCA forms robust cluster structures.
clustering gene sequences into families is important for understanding and predicting gene function. Many clustering algorithms and alignment-free similarity measures have been used to analyze gene family. The cluster...
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ISBN:
(纸本)9783642275517
clustering gene sequences into families is important for understanding and predicting gene function. Many clustering algorithms and alignment-free similarity measures have been used to analyze gene family. The clustering results can be influenced by the similarity measure and clustering algorithm used. We compare the results from running four commonly used clustering methods, including K-means, single-linkage clustering, complete-linkage clustering and average-linkage clustering, on three alignment-free similarity measures. We try to find out which method should provide the best clustering result based on real-world gene family datasets. Experiment results show that average-linkage clustering with our similarity measure, DMk, performed best.
This paper obtains the research and analysis on clustering algorithms for stream data mining. And swarm intelligence optimization algorithm is applied to stream data clustering analysis. algorithms for the representat...
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ISBN:
(纸本)9781457715846
This paper obtains the research and analysis on clustering algorithms for stream data mining. And swarm intelligence optimization algorithm is applied to stream data clustering analysis. algorithms for the representative are particle swarm optimization and ant colony optimization has been studied. The generation of the algorithm to achieve and in the application of cluster analysis in a comparative analysis, and proposed the next step of a cluster analysis based on swarm intelligence in research work.
With the increased adoption of technologies like wireless sensor networks by real-world applications, dynamic network topologies are becoming the rule rather than the exception. Node mobility, however, introduces a ra...
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With the increased adoption of technologies like wireless sensor networks by real-world applications, dynamic network topologies are becoming the rule rather than the exception. Node mobility, however, introduces a range of problems (communication interference, path uncertainty, low quality of service and information loss, etc.) that are not handled well by periodically refreshing state information, as algorithms designed for static networks typically do. To address specifically this problem, the main contribution of this paper is the introduction of a novel mechanism (called ASH) for the creation of a quasi-static overlay on top of a mobile topology. It is powered by simple, local interactions between nodes and exhibits self-healing and self-organization capabilities with respect to failures and node mobility. We show that the overlay mechanism works without assumptions about position, orientation, speed, motion correlation, and trajectory prediction of the nodes. A preliminary evaluation by means of simulation shows that ASH succeeds in tackling node mobility, while consuming only minimal resources.
Compared with flat routing protocols, clustering is a fundamental performance improvement technique in wireless sensor networks, which can increase network scalability and lifetime. In this paper, we integrate the mul...
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Compared with flat routing protocols, clustering is a fundamental performance improvement technique in wireless sensor networks, which can increase network scalability and lifetime. In this paper, we integrate the multi-hop technique with a backoff-based clustering algorithm to organize sensors. By using an adaptive backoff strategy, the algorithm not only realizes load balance among sensor node, but also achieves fairly uniform cluster head distribution across the network. Simulation results also demonstrate our algorithm is more energy-efficient than classical ones. Our algorithm is also easily extended to generate a hierarchy of cluster heads to obtain better network management and energy-efficiency.
clustering techniques have been widely used for solving various engineering problems such as system architecture, modular product/system design, group technology, machine layout, and so on. Most of these problems use ...
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clustering techniques have been widely used for solving various engineering problems such as system architecture, modular product/system design, group technology, machine layout, and so on. Most of these problems use matrix formulation to model the problem. Once the matrix formulation for the problem is obtained, cluster analysis is used to group objects represented in the matrix into homogenous clusters based on object features. In this correspondence, a new efficient algorithm for clustering large n x n binary and nonbinary (weighted) matrices is presented. For an n x n incidence matrix, the algorithm first creates n clusters. Once the initial clusters are obtained, the algorithm uses improvement steps to continuously improve the quality of the solution obtained in the previous step. Modifications to the algorithm are presented for clustering n x m matrices. A detailed discussion on the effectiveness of the clustering algorithm when it is applied to matrices of various sizes and sparsity is also presented. The application of the n x n clustering algorithm developed in this correspondence is presented with the development of modular electrical/electronic vehicle door architectures.
Various data-centric web applications are becoming the developing trend of information society. Cloud computing currently adopt column-oriented storage wide table to represent the heterogeneous structured data of thes...
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ISBN:
(纸本)9783037853191
Various data-centric web applications are becoming the developing trend of information society. Cloud computing currently adopt column-oriented storage wide table to represent the heterogeneous structured data of these applications. The wide table reduces the waste of storage space, but slows down query efficiency. The paper implements the hybrid partition on access frequent (HPAF) to horizontally and vertically partition a wide table. It uses a variant of consistent hashing to dynamically horizontally partition a wide table across multiple storage nodes on each node's performance;It use entropy to represent the number of reducing access data block from the table with N columns than from N column-oriented storage tables. According to the second law of thermodynamics, the paper designs an entropy increasing clustering algorithm to classify the columns of a wide table. The algorithm finds a cluster with multiple classes which save maximum access time. The paper implements an algorithm for structured query across multiple materialized views too. Lastly the paper demonstrates the query performance and storage efficiency of our strategy compared to single column storage.
clustering based approaches in Wireless Sensor Networks helps in identifying the summarized data by exploiting the feature of data redundancy in sensor networks. Due to the inexpensive hardware used and unattended ope...
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clustering based approaches in Wireless Sensor Networks helps in identifying the summarized data by exploiting the feature of data redundancy in sensor networks. Due to the inexpensive hardware used and unattended operation nature, nodes in the sensor networks are often prone to many failures malicious attacks and resource constraints and data collected in sensor networks are found to be unreliable. Moreover, the wide usages of sensor network in diverse application have put a constraint on sensor protocol to handle data of mixed types. To address the issues of energy minimization and data reliability, we propose a distributed agglomerative cluster based anomaly detection algorithm termed DACAD to detect the faulty readings based on kNN approach. Additionally, to support applications with mixed data attributes, we design a heterogeneous distance function, HOEM to handle both continuous and nominal attributes. In this paper we have evaluated the performance of proposed algorithm in terms of false alarm rate, false positive rate and detection rate. Our results demonstrate that the proposed distance achieves a comparable detection rate with low false alarm rate with a significant reduction in computation and communication over head and operates with both continuous and nominal data. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of ICCTSD 2011
Locating distribution centers optimally is a crucial and systematic task for *** located distribution centers can significantly improve the logistics system's efficiency and reduce its operational ***,it is not an...
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Locating distribution centers optimally is a crucial and systematic task for *** located distribution centers can significantly improve the logistics system's efficiency and reduce its operational ***,it is not an easy task to optimize distribution center locations and previous studies focused primarily on location optimization of a single distribution *** growing logistics demands,multiple distribution centers become necessary to meet customers' requirements,but few studies have tackled the multiple distribution center locations(MDCLs) *** paper presents a comprehensive algorithm to address the MDCLs *** integration and clustering approach using the improved axiomatic fuzzy set(AFS) theory is developed for location clustering based on multiple hierarchical evaluation ***,technique for order preference by similarity to ideal solution(TOPSIS) is applied for evaluating and selecting the best candidate for each *** analysis is also conducted to assess the influence of each criterion in the location planning decision *** from a case study in Guiyang,China,reveals that the proposed approach developed in this study outperforms other similar algorithms for MDCLs *** new method may easily be extended to address location planning of other types of facilities,including hospitals,fire stations and schools.
Vehicular communications are expected to enable the development of Intelligent Cooperative Systems for solving crucial problems related to mobility: road safety, traffic management etc. Information and Communication T...
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Vehicular communications are expected to enable the development of Intelligent Cooperative Systems for solving crucial problems related to mobility: road safety, traffic management etc. Information and Communication Technologies could also play an important role in order to optimise the energy management of conventional, hybrid and electrical vehicles and, thus, to reduce their environment impact. In particular, vehicular communications could be used to predict driving conditions with the objective to determine future load power demand. An adaptive energy management strategy for series Hybrid Electric Vehicles (HEVs) based on genetic algorithm optimised maps and the Simulation of Urban Mobility (SUMO) predictor is presented here.
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