An optimal energy-efficient clustering method in wireless sensor networks using multi-objective genetic algorithm

2011 ◽  
Vol 26 (1) ◽  
pp. 114-126 ◽  
Author(s):  
Ali Peiravi ◽  
Habib Rajabi Mashhadi ◽  
S. Hamed Javadi
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Wireless Sensor ◽  
Clustering Method ◽  
Multi Objective ◽  
Optimal Energy ◽  
Multi Objective Genetic Algorithm ◽  
Energy Efficient Clustering

scholarly journals Threshold Balanced Sampled DEEC Model for Heterogeneous Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Sercan Vançin ◽  
Ebubekir Erdem
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Wireless Sensor ◽  
Distributed Energy ◽  
Clustering Method ◽  
Clustering Protocol ◽  
Energy Efficient Clustering

Due to the restricted hardware resources of the sensor nodes, modelling and designing energy efficient routing methods to increase the overall network lifetime have become one of the most significant strategies in wireless sensor networks (WSNs). Cluster-based heterogeneous routing protocols, a popular part of routing technology, have proven effective in management of topology, energy consumption, data collection or fusion, reliability, or stability in a distributed sensor network. In this article, an energy efficient three-level heterogeneous clustering method (DEEC) based distributed energy efficient clustering protocol named TBSDEEC (Threshold balanced sampled DEEC) is proposed. Contrary to most other studies, this study considers the effect of the threshold balanced sampled in the energy consumption model. Our model is compared with the DEEC, EDEEC (Enhanced Distributed Energy Efficient Clustering Protocol), and EDDEEC (Enhanced Developed Distributed Energy Efficient Clustering Protocol) using MATLAB as two different scenarios based on quality metrics, including living nodes on the network, network efficiency, energy consumption, number of packets received by base station (BS), and average latency. After, our new method is compared with artificial bee colony optimization (ABCO) algorithm and energy harvesting WSN (EH-WSN) clustering method. Simulation results demonstrate that the proposed model is more efficient than the other protocols and significantly increases the sensor network lifetime.

Sign in / Sign up

Export Citation Format

Share Document