Energy consumption affects Wireless Sensor Networks (WSNs) lifetime and may cause network degradation. Potential work has been focused on consumed energy reduction techniques. The consumed energy during communication ...
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ISBN:
(纸本)9781479985470
Energy consumption affects Wireless Sensor Networks (WSNs) lifetime and may cause network degradation. Potential work has been focused on consumed energy reduction techniques. The consumed energy during communication is affected exponentially by the distance between the communicating nodes;the more communication distance between two nodes the more energy consumed. clustering was used to help in reducing the energy consumed in the wireless data transmission. clustering gathers the nodes into groups called clusters. One node from each cluster is elected to be the cluster head (CH). Deciding the optimal number of clusters and which sensors should be CHs is a challenge problem. We presented two hybrid clustering algorithms called K-Means Particle Swarm Optimization (KPSO) and K-Means Genetic algorithms (KGAs) in [1], [2] with significant improvement over traditional Low Energy Adaptive clustering Hierarchy protocol (LEACH). Considering the various antenna patterns for WSN we were able to improve the clustering algorithm performance in energy saving. In this article, we shall review our presented algorithms and present in details the new antenna pattern design based PSO and GAs.
Radio channel propagation models for the millimeter wave (mmWave) spectrum are extremely important for planning future 5G wireless communication systems. Transmitted radio signals are received as clusters of multipath...
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Radio channel propagation models for the millimeter wave (mmWave) spectrum are extremely important for planning future 5G wireless communication systems. Transmitted radio signals are received as clusters of multipath rays. Identifying these clusters provides better spatial and temporal characteristics of the mmWave channel. This paper deals with the clustering process and its validation across a wide range of frequencies in the mmWave spectrum below 100 GHz. By way of simulations, we show that in outdoor communication scenarios clustering of received rays is influenced by the frequency of the transmitted signal. This demonstrates the sparse characteristic of the mmWave spectrum (i.e., we obtain a lower number of rays at the receiver for the same urban scenario). We use the well-known k-means clustering algorithm to group arriving rays at the receiver. The accuracy of this partitioning is studied with both cluster validity indices (CVIs) and score fusion techniques. Finally, we analyze how the clustering solution changes with narrower-beam antennas, and we provide a comparison of the cluster characteristics for different types of antennas.
Despite an increasing consensus regarding the significance of properly identifying the most suitable clustering method for a given problem, a surprising amount of educational research, including both educational data ...
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Despite an increasing consensus regarding the significance of properly identifying the most suitable clustering method for a given problem, a surprising amount of educational research, including both educational data mining (EDM) and learning analytics (LA), neglects this critical task. This shortcoming could in many cases have a negative impact on the prediction power of both the EDM and LA based approaches. To address such issues, this work proposes an evaluation approach that automatically compares several clustering methods using multiple internal and external performance measures on 9 real-world educational datasets of different sizes, created from the University of Tartu's Moodle system, to produce two-way clustering. Moreover, to investigate the possible effect of normalization on the performance of the clustering algorithms, this work performs the same experiment on a normalized version of the datasets. Since such an exhaustive evaluation includes multiple criteria, the proposed approach employs a multiple criteria decision-making method (i.e., TOPSIS) to rank the most suitable methods for each dataset. Our results reveal that the proposed approach can automatically compare the performance of the clustering methods and accordingly recommend the most suitable method for each dataset. Furthermore, our results show that in both normalized and nonnormalized datasets of different sizes with 10 features, DBSCAN and k-medoids are the best clustering methods, whereas agglomerative and spectral methods appear to be among the most stable and highly performing clustering methods for such datasets with 15 features. Regarding datasets with more than 15 features, OPTICS is among the top-ranked algorithms among the nonnormalized datasets, and k-medoids is the best among the normalized datasets. Interestingly, our findings reveal that normalization may have a negative effect on the performance of certain methods, e.g., spectral clustering and OPTICS;however, it appears to m
Wireless sensors are regarded as critical components in allowing effective IoT networking that has spread into a variety of real-time applications. One of the primary goals in constructing a wireless sensor network (W...
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In this paper we introduce new models for semisupervised clustering problem;in particular we address this problem from the representation space point of view. Given a dataset enhanced with constraints (typically must-...
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With the development of wireless communication, positioning and sensor technology, the acquisition of spatiotemporal trajectory data becomes more and more easy. Spatiotemporal trajectory data is composed of a series o...
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作者：
Amel, HebboulFella, Hachouf
Université Constantine 2 - Abdelhamid Mehri-Ali Mendjeli Constantine25000 Algeria Laboratore d'Automatique and Robotique
Département d'Electronique Faculté des sciences de la Technologie Université Freres Mentouri Route Ain Elbey Constantine25000 Algeria
In classification task, kernel functions are used to make possible to partition data that are linearly non-separable. In this paper, a Particle Swarm Optimization (PSO) is used to obtain optimal cluster centres, their...
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The relationship among the large amount of biological data has become a hot research topic. It is desirable to have clustering methods to group similar data together so that, when a lot of data is needed, all data are...
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Image segmentation is a key step for many images analysis applications. So far, there does not exist a general method to segment suitable all images, regardless if these are corrupted or noise free. In this paper, we ...
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The integration of Data Mining algorithms to extract knowledge from Geographic Information Systems that hold large amounts of data and directly affects economics and social sectors, is becoming indispensable. In this ...
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ISBN:
(纸本)8517000277
The integration of Data Mining algorithms to extract knowledge from Geographic Information Systems that hold large amounts of data and directly affects economics and social sectors, is becoming indispensable. In this way, TerraLib, an important library for geographic applications development in geoprocessing world scenarios, appears as a strong candidate to incorporate these features. This work shows an experiment on the integration of a clustering algorithm with TerraLib to generate a classification on unidentified air traffic, aiming at applying this solution to an analysis of these traffics.
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