Intelligent Detection of Network Coverage Problems Based on the Clustering Algorithm

2020 ◽  
pp. 1010-1018
Author(s):  
Ma Yu ◽  
Yang Jieyan ◽  
Zhu Jiajia ◽  
Cheng Xinzhou
Keyword(s):  
Clustering Algorithm ◽  
Network Coverage ◽  
Intelligent Detection ◽  
Coverage Problems

Grid-Based Cluster Head Selection Algorithm in Wireless Sensor Networks

2013 ◽  
Vol 427-429 ◽  
pp. 1497-1501
Author(s):  
Cun Xiang Chen ◽  
Zun Wen He ◽  
Jing Ming Kuang ◽  
Hong Mei Sun
Keyword(s):  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Cluster Head Selection ◽  
Network Coverage ◽  
Distributed Clustering ◽  
Position Information ◽  
Adaptive Clustering ◽  
Short Lifecycle ◽  
Grid Based

Although low-energy adaptive clustering hierarchy (LEACH) protocol adopts distributed clustering algorithm and randomized rotation of Cluster Heads (CHs) mechanism to reduce energy consumption, election of CHs without residual energy and position information of each nodes brings about irregular distribution of CH, low network coverage and short lifecycle. In order to avoid these shortcomings, a Grid-based Cluster Head Selection (GCHS) is proposed. Referring to sensing distance, network is divided into several grids equivalent to independent clusters which can meet network coverage and connectivity. Furthermore, CH is selected ground on residual energy level of each node. Simulation taking full advantage of concept mentioned above manifests that it achieves a significant improvement in network coverage and lifecycle.

scholarly journals Performance Evaluation of a Dual Coverage System for Internet of Things Environments

2016 ◽  
Vol 2016 ◽  
pp. 1-20 ◽  
Author(s):  
Omar Said ◽  
Amr Tolba
Keyword(s):  
Internet Of Things ◽  
Packet Loss ◽  
The Internet ◽  
Network Coverage ◽  
Coverage Area ◽  
End To End Delay ◽  
High Altitude Platforms ◽  
Multiple Levels ◽  
Coverage Problems ◽  
Simulation Package

A dual coverage system for Internet of Things (IoT) environments is introduced. This system is used to connect IoT nodes regardless of their locations. The proposed system has three different architectures, which are based on satellites and High Altitude Platforms (HAPs). In case of Internet coverage problems, the Internet coverage will be replaced with the Satellite/HAP network coverage under specific restrictions such as loss and delay. According to IoT requirements, the proposed architectures should include multiple levels of satellites or HAPs, or a combination of both, to cover the global Internet things. It was shown that the Satellite/HAP/HAP/Things architecture provides the largest coverage area. A network simulation package, NS2, was used to test the performance of the proposed multilevel architectures. The results indicated that the HAP/HAP/Things architecture has the best end-to-end delay, packet loss, throughput, energy consumption, and handover.

scholarly journals Enhancement of Advanced Metering Infrastructure Performance Using Unsupervised K-Means Clustering Algorithm

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2732
Author(s):  
Daisy Nkele Molokomme ◽  
Chabalala S. Chabalala ◽  
Pitshou N. Bokoro
Keyword(s):  
Data Aggregation ◽  
Network Performance ◽  
Clustering Algorithm ◽  
Main Concern ◽  
Area Network ◽  
Network Coverage ◽  
Efficient Manner ◽  
Index Method ◽  
Silhouette Index ◽  
Aggregation Technique

Data aggregation may be considered as the technique through which streams of data gathered from Smart Meters (SMs) can be processed and transmitted to a Utility Control Center (UCC) in a reliable and cost-efficient manner without compromising the Quality of Service (QoS) requirements. In a typical Smart Grid (SG) paradigm, the UCC is usually located far away from the consumers (SMs), which has led to a degradation in network performance. Although the data aggregation technique has been recognized as a favorable solution to optimize the network performance of the SG, the underlying issue to date is to determine the optimal locations for the Data Aggregation Points (DAPs), where network coverage and full connectivity for all SMs deployed within the network are achieved. In addition, the main concern of the aggregation technique is to minimize transmission and computational costs. In this sense, the number of DAPs deployed should be as minimal as possible while satisfying the QoS requirements of the SG. This paper presents a Neighborhood Area Network (NAN) placement scheme based on the unsupervised K-means clustering algorithm with silhouette index method to determine the efficient number of DAPs required under different SM densities and find the best locations for the deployment of DAPs. Poisson Point Process (PPP) has been deployed to model the locations of the SMs. The simulation results presented in this paper indicate that the NAN placement scheme based on the ageless unsupervised K-means clustering algorithm not only improves the accuracy in determining the number of DAPs required and their locations but may also improve the network performance significantly in terms of network coverage and full connectivity.

scholarly journals Internal Defects Detection Method of the Railway Track Based on Generalization Features Cluster

2021 ◽  
Author(s):  
Fupei Wu ◽  
Xiaoyang Xie ◽  
Jiahua Guo ◽  
Qinghua Li
Keyword(s):  
Clustering Algorithm ◽  
Detection Method ◽  
Railway Track ◽  
Internal Defects ◽  
Head Region ◽  
Working Environments ◽  
Intelligent Detection ◽  
Maximum Tolerance ◽  
Reduced Dimension ◽  
Different Shapes

Abstract Many internal defects maybe arise in railway track working, which usually have different shapes and distribution rules. To solve the problem, an intelligent detection method is proposed for internal defects of railway track based on generalization features cluster in this paper. Firstly, defects are classified and counted according to their shapes and locations features. Then, generalized features of defects are extracted and formulated based on the maximum difference between different types of defects and the maximum tolerance among same types of defects. Finally, extracted generalized features are expressed by function constraints, and formulated as generalization feature clusters to classify and identify internal defects of the railway track. Furthermore, a reduced dimension method of the generalization features clusters is presented too in this paper. Based on the reduced dimension feature and strong constrained generalized features, the K-means clustering algorithm is developed for defects clustering, and good clustering results are achieved. To defects in the rail head region, its clustering accuracy is over 95%, and the Davies-Bouldin Index (DBI) index is small, which indicates the validation of the proposed generalization features with strong constraints. Experimental results show that accuracy of the proposed method based on generalization features clusters is up to 97.55%, and the average detection time is 0.12s/frame, which indicates it has good performance in adaptability, high accuracy and detection speed under the complex working environments.

scholarly journals Green Clustering Implementation Based on DPS-MOPSO

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Yang Lu ◽  
Xuezhi Tan ◽  
Yun Mo ◽  
Lin Ma
Keyword(s):  
Population Size ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Traffic Load ◽  
Network Coverage ◽  
Original Network ◽  
Swarm Optimization ◽  
Accurate Procedure ◽  
Dynamic Population ◽  
Tracing Method

A green clustering implementation is proposed to be as the first method in the framework of an energy-efficient strategy for centralized enterprise high-density WLANs. Traditionally, to maintain the network coverage, all of the APs within the WLAN have to be powered on. Nevertheless, the new algorithm can power off a large proportion of APs while the coverage is maintained as the always-on counterpart. The proposed algorithm is composed of two parallel and concurrent procedures, which are the faster procedure based onK-means and the more accurate procedure based on Dynamic Population Size Multiple Objective Particle Swarm Optimization (DPS-MOPSO). To implement green clustering efficiently and accurately, dynamic population size and mutational operators are introduced as complements for the classical MOPSO. In addition to the function of AP selection, the new green clustering algorithm has another new function as the reference and guidance for AP deployment. This paper also presents simulations in scenarios modeled with ray-tracing method and FDTD technique, and the results show that about 67% up to 90% of energy consumption can be saved while the original network coverage is maintained during periods when few users are online or when the traffic load is low.

Distributed Entropy Energy-Efficient Clustering algorithm for cluster head selection (DEEEC)

2020 ◽  
Vol 39 (6) ◽  
pp. 8139-8147
Author(s):  
Ranganathan Arun ◽  
Rangaswamy Balamurugan
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Second Stage ◽  
Energy Efficient Clustering ◽  
Two Stages ◽  
Ch Selection

In Wireless Sensor Networks (WSN) the energy of Sensor nodes is not certainly sufficient. In order to optimize the endurance of WSN, it is essential to minimize the utilization of energy. Head of group or Cluster Head (CH) is an eminent method to develop the endurance of WSN that aggregates the WSN with higher energy. CH for intra-cluster and inter-cluster communication becomes dependent. For complete, in WSN, the Energy level of CH extends its life of cluster. While evolving cluster algorithms, the complicated job is to identify the energy utilization amount of heterogeneous WSNs. Based on Chaotic Firefly Algorithm CH (CFACH) selection, the formulated work is named “Novel Distributed Entropy Energy-Efficient Clustering Algorithm”, in short, DEEEC for HWSNs. The formulated DEEEC Algorithm, which is a CH, has two main stages. In the first stage, the identification of temporary CHs along with its entropy value is found using the correlative measure of residual and original energy. Along with this, in the clustering algorithm, the rotating epoch and its entropy value must be predicted automatically by its sensor nodes. In the second stage, if any member in the cluster having larger residual energy, shall modify the temporary CHs in the direction of the deciding set. The target of the nodes with large energy has the probability to be CHs which is determined by the above two stages meant for CH selection. The MATLAB is required to simulate the DEEEC Algorithm. The simulated results of the formulated DEEEC Algorithm produce good results with respect to the energy and increased lifetime when it is correlated with the current traditional clustering protocols being used in the Heterogeneous WSNs.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

K-MEANS CLUSTERING ALGORITHM BASED CLASSIFICATION OF SOIL FERTILITY IN NORTH WEST NIGERIA

2020 ◽  
Vol 4 (2) ◽  
pp. 780-787
Author(s):  
Ibrahim Hassan Hayatu ◽  
Abdullahi Mohammed ◽  
Barroon Ahmad Isma’eel ◽  
Sahabi Yusuf Ali
Keyword(s):  
Soil Fertility ◽  
Crop Yield ◽  
Clustering Algorithm ◽  
Soil Samples ◽  
North West ◽  
R Programming ◽  
Available Information ◽  
Northwest Region ◽  
The Relationship

Soil fertility determines a plant's development process that guarantees food sufficiency and the security of lives and properties through bumper harvests. The fertility of soil varies according to regions, thereby determining the type of crops to be planted. However, there is no repository or any source of information about the fertility of the soil in any region in Nigeria especially the Northwest of the country. The only available information is soil samples with their attributes which gives little or no information to the average farmer. This has affected crop yield in all the regions, more particularly the Northwest region, thus resulting in lower food production.  Therefore, this study is aimed at classifying soil data based on their fertility in the Northwest region of Nigeria using R programming. Data were obtained from the department of soil science from Ahmadu Bello University, Zaria. The data contain 400 soil samples containing 13 attributes. The relationship between soil attributes was observed based on the data. K-means clustering algorithm was employed in analyzing soil fertility clusters. Four clusters were identified with cluster 1 having the highest fertility, followed by 2 and the fertility decreases with an increasing number of clusters. The identification of the most fertile clusters will guide farmers on where best to concentrate on when planting their crops in order to improve productivity and crop yield.

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