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  • Open Access

    ARTICLE

    An Asynchronous Clustering and Mobile Data Gathering Schema Based on Timer Mechanism in Wireless Sensor Networks

    Jin Wang1,2,3, Yu Gao3, Wei Liu3, Wenbing Wu1, Se-Jung Lim4,*

    CMC-Computers, Materials & Continua, Vol.58, No.3, pp. 711-725, 2019, DOI:10.32604/cmc.2019.05450

    Abstract Recently, Wireless sensor networks (WSNs) have become very popular research topics which are applied to many applications. They provide pervasive computing services and techniques in various potential applications for the Internet of Things (IoT). An Asynchronous Clustering and Mobile Data Gathering based on Timer Mechanism (ACMDGTM) algorithm is proposed which would mitigate the problem of “hot spots” among sensors to enhance the lifetime of networks. The clustering process takes sensors’ location and residual energy into consideration to elect suitable cluster heads. Furthermore, one mobile sink node is employed to access cluster heads in accordance with More >

  • Open Access

    ARTICLE

    An Energy-Efficient Protocol Using an Objective Function & Random Search with Jumps for WSN

    Mohammed Kaddi1,3,*, Khelifa Benahmed2, Mohammed Omari3

    CMC-Computers, Materials & Continua, Vol.58, No.3, pp. 603-624, 2019, DOI:10.32604/cmc.2019.05341

    Abstract Wireless Sensor Networks (WSNs) have hardware and software limitations and are deployed in hostile environments. The problem of energy consumption in WSNs has become a very important axis of research. To obtain good performance in terms of the network lifetime, several routing protocols have been proposed in the literature. Hierarchical routing is considered to be the most favorable approach in terms of energy efficiency. It is based on the concept parent-child hierarchy where the child nodes forward their messages to their parent, and then the parent node forwards them, directly or via other parent nodes,… More >

  • Open Access

    ARTICLE

    An Improved Unsupervised Image Segmentation Method Based on Multi-Objective Particle Swarm Optimization Clustering Algorithm

    Zhe Liu1,2,*, Bao Xiang1,3, Yuqing Song1, Hu Lu1, Qingfeng Liu1

    CMC-Computers, Materials & Continua, Vol.58, No.2, pp. 451-461, 2019, DOI:10.32604/cmc.2019.04069

    Abstract Most image segmentation methods based on clustering algorithms use single-objective function to implement image segmentation. To avoid the defect, this paper proposes a new image segmentation method based on a multi-objective particle swarm optimization (PSO) clustering algorithm. This unsupervised algorithm not only offers a new similarity computing approach based on electromagnetic forces, but also obtains the proper number of clusters which is determined by scale-space theory. It is experimentally demonstrated that the applicability and effectiveness of the proposed multi-objective PSO clustering algorithm. More >

  • Open Access

    ARTICLE

    A Method of Identifying Thunderstorm Clouds in Satellite Cloud Image Based on Clustering

    Lili He1,2, Dantong Ouyang1,2, Meng Wang1,2, Hongtao Bai1,2, Qianlong Yang1,2, Yaqing Liu3,4, Yu Jiang1,2,*

    CMC-Computers, Materials & Continua, Vol.57, No.3, pp. 549-570, 2018, DOI:10.32604/cmc.2018.03840

    Abstract In this paper, the clustering analysis is applied to the satellite image segmentation, and a cloud-based thunderstorm cloud recognition method is proposed in combination with the strong cloud computing power. The method firstly adopts the fuzzy C-means clustering (FCM) to obtain the satellite cloud image segmentation. Secondly, in the cloud image, we dispose the ‘high-density connected’ pixels in the same cloud clusters and the ‘low-density connected’ pixels in different cloud clusters. Therefore, we apply the DBSCAN algorithm to the cloud image obtained in the first step to realize cloud cluster knowledge. Finally, using the method More >

  • Open Access

    ARTICLE

    An Evidence Combination Method based on DBSCAN Clustering

    Kehua Yang1,2,*, Tian Tan1, Wei Zhang1

    CMC-Computers, Materials & Continua, Vol.57, No.2, pp. 269-281, 2018, DOI:10.32604/cmc.2018.03696

    Abstract Dempster-Shafer (D-S) evidence theory is a key technology for integrating uncertain information from multiple sources. However, the combination rules can be paradoxical when the evidence seriously conflict with each other. In the paper, we propose a novel combination algorithm based on unsupervised Density-Based Spatial Clustering of Applications with Noise (DBSCAN) density clustering. In the proposed mechanism, firstly, the original evidence sets are preprocessed by DBSCAN density clustering, and a successfully focal element similarity criteria is used to mine the potential information between the evidence, and make a correct measure of the conflict evidence. Then, two More >

  • Open Access

    ARTICLE

    Method of Time Series Similarity Measurement Based on Dynamic Time Warping

    Lianggui Liu1,*, Wei Li1, Huiling Jia1

    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 97-106, 2018, DOI:10.32604/cmc.2018.03511

    Abstract With the rapid development of mobile communication all over the world, the similarity of mobile phone communication data has received widely attention due to its advantage for the construction of smart cities. Mobile phone communication data can be regarded as a type of time series and dynamic time warping (DTW) and derivative dynamic time warping (DDTW) are usually used to analyze the similarity of these data. However, many traditional methods only calculate the distance between time series while neglecting the shape characteristics of time series. In this paper, a novel hybrid method based on the More >

  • Open Access

    ARTICLE

    A Spark Scheduling Strategy for Heterogeneous Cluster

    Xuewen Zhang1, Zhonghao Li1, Gongshen Liu1,*, Jiajun Xu1, Tiankai Xie2, Jan Pan Nees1

    CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 405-417, 2018, DOI:10.3970/cmc.2018.02527

    Abstract As a main distributed computing system, Spark has been used to solve problems with more and more complex tasks. However, the native scheduling strategy of Spark assumes it works on a homogenized cluster, which is not so effective when it comes to heterogeneous cluster. The aim of this study is looking for a more effective strategy to schedule tasks and adding it to the source code of Spark. After investigating Spark scheduling principles and mechanisms, we developed a stratifying algorithm and a node scheduling algorithm is proposed in this paper to optimize the native scheduling More >

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