An Energy-Efficient Caching Strategy Based on Coordinated Caching for Green Content-Centric Network

2016 ◽  
pp. 168-175 ◽  
Author(s):  
Huiqing Xu ◽  
Hengjie Huang ◽  
Gaocai Wang
Keyword(s):  
Energy Efficient ◽  
Caching Strategy ◽  
Content Centric Network

scholarly journals Content-Centric Network Caching Strategy based on Node Situational Degree

2019 ◽  
Vol 15 (8) ◽  
pp. 2190
Author(s):  
Zhang Jianwei ◽  
Du Chunfeng ◽  
Cai Zengyu ◽  
Wang Wenqian ◽  
Wu Zuodong
Keyword(s):  
Caching Strategy ◽  
Content Centric Network

Performance Evaluation of AFOD Protocol for Energy Efficient Routing

2011 ◽  
Author(s):  
B. Smitha Shekar ◽  
M. Sudhakar Pillai ◽  
G. Narendra Kumar
Keyword(s):  
Performance Evaluation ◽  
Energy Efficient ◽  
Energy Efficient Routing

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.

Sign in / Sign up

Export Citation Format

Share Document