Equal Area User Clustering Algorithm for Energy Efficient Cellular Network

2016 ◽  
Author(s):  
Hailu Belay Kassa ◽  
Shanko Chura Aredo ◽  
Estifanos Yohannes ◽  
Dereje Hailemariam ◽  
Yacob Astatke ◽  
...  
Keyword(s):  
Energy Efficient ◽  
Cellular Network ◽  
Clustering Algorithm ◽  
Equal Area ◽  
User Clustering

scholarly journals A novel benefit-tree based dynamic clustering algorithm of comp in dense network

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1965-1972
Author(s):  
Gang Su ◽  
Lu Feng ◽  
Li Tan ◽  
Yunlong Liang
Keyword(s):  
Energy Efficient ◽  
Cellular Network ◽  
Clustering Algorithm ◽  
Base Stations ◽  
Frame Structure ◽  
Dynamic Clustering ◽  
Dense Network ◽  
Performance Simulation ◽  
Coordinated Multipoint ◽  
Save Energy

We focus here on the Energy Efficiency (EE), and propose an improved dynamic clustering algorithm that is incorporated into Coordinated MultiPoint (CoMP) with sleeping mode. Thanks to the frame structure of Benefit-tree employed in dynamic clustering of CoMP, the proposed algorithm can significantly improve the EE by choosing the most energy efficient competitive clustering strategy. At the same time, it can save energy by sleeping the underutilized base stations in the cellular network. In the performance simulation for EE, the proposed algorithm provides 10% higher in EE than the existing algorithm.

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.

scholarly journals Energy Efficient Operation of Cellular Network Using On/Off Base Stations

2015 ◽  
Vol 11 (8) ◽  
pp. 108210 ◽  
Author(s):  
Yong-Hoon Choi ◽  
Jungerl Lee ◽  
Juhoon Back ◽  
Suwon Park ◽  
Young-uk Chung ◽  
...  
Keyword(s):  
Energy Efficient ◽  
Cellular Network ◽  
Base Stations ◽  
Efficient Operation

scholarly journals An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks

Sensors ◽  
2015 ◽  
Vol 15 (8) ◽  
pp. 19783-19818 ◽  
Author(s):  
Ibrahim Mustapha ◽  
Borhanuddin Ali ◽  
Mohd Rasid ◽  
Aduwati Sali ◽  
Hafizal Mohamad
Keyword(s):  
Reinforcement Learning ◽  
Cognitive Radio ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cognitive Radio Sensor Networks
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