scholarly journals Genetic Algorithm-Based Multi-Hop Routing to Improve the Lifetime of Wireless Sensor Networks

2021 ◽  
Vol 11 (6) ◽  
pp. 7770-7775
Author(s):  
A. Rajab
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Vital Role ◽  
Wireless Sensor ◽  
Energy Usage ◽  
Battery Lifetime ◽  
Application Specific ◽  
Key Parameter

Wireless sensor networks are known for their monitoring and tracking application-specific operations. These operations diversely demand improvement in existing strategies and their parameters. One key parameter is energy usage during operations. Energy plays a vital role in each application, as the wireless sensor networks lack battery lifetime and energy resources. So, there is a need for an optimized and efficient routing method with regard to energy consumption in wireless sensor networks. For multi-hop routing, the genetic algorithm serves as a robust algorithm with diverse optimized routing plans to improve the lifespan for large-scale wireless sensor networks. In this paper, the genetic algorithm provides the optimized routes for data operations and improves the lifetime of wireless sensor networks by saving energy. The performance of the genetic algorithm is compared with the TEEN algorithm.

scholarly journals Genetic Algorithm Application in Optimization of Wireless Sensor Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Ali Norouzi ◽  
A. Halim Zaim
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Fitness Function ◽  
Optimization Methods ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Final Formula ◽  
Nonlinear Optimization Methods

There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs.

Efficient Power Control and Clustering Method Based on Hybrid Clonal Genetic Algorithm in Wireless Sensor Networks

2012 ◽  
Vol 263-266 ◽  
pp. 889-897
Author(s):  
Xiang Xian Zhu ◽  
Su Feng Lu
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Clonal Selection ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Clonal Selection Algorithm ◽  
Clustering Methods

Wireless sensor networks (WSNs) lifetime for large-scale surveillance systems is defined as the time span that all targets can be covered. How to manage the combination of the sensor nodes efficiently to prolong the whole network’s lifetime while insuring the network reliability, it is one of the most important problems to research in WSNs. An effective optimization framework is then proposed, where genetic algorithm and clonal selection algorithm are hybridized to enhance the searching ability. Our goal can be described as minimizing the number of active nodes and the scheduling cost, thus reducing the overall energy consumption to prolong the whole network’s lifetime with certain coverage rate insured. We compare the proposed algorithm with different clustering methods used in the WSNs. The simulation results show that the proposed algorithm has higher efficiency and can achieve better network lifetime and data delivery at the base station.

scholarly journals An Improved Niche Chaotic Genetic Algorithm for Low-Energy Clustering Problem in Large-Scale Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Min Tian ◽  
Jie Zhou ◽  
Xin Lv
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Large Scale ◽  
Low Energy ◽  
Wireless Sensor ◽  
Mixed Integer ◽  
Clustering Problem ◽  
Systems Simulation

Large-scale wireless sensor networks consist of a large number of tiny sensors that have sensing, computation, wireless communication, and free-infrastructure abilities. The low-energy clustering scheme is usually designed for large-scale wireless sensor networks to improve the communication energy efficiency. However, the low-energy clustering problem can be formulated as a nonlinear mixed integer combinatorial optimization problem. In this paper, we propose a low-energy clustering approach based on improved niche chaotic genetic algorithm (INCGA) for minimizing the communication energy consumption. We formulate our objective function to minimize the communication energy consumption under multiple constraints. Although suboptimal for LSWSN systems, simulation results show that the proposed INCGA algorithm allows to reduce the communication energy consumption with lower complexity compared to the QEA (quantum evolutionary algorithm) and PSO (particle swarm optimization) approaches.

scholarly journals Genetic Algorithm Based Clustering for Large-Scale Sensor Networks

2015 ◽  
Vol 15 (6) ◽  
pp. 168-177
Author(s):  
Hai Lin ◽  
Ruoshan Kong ◽  
Jiali Liu
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Saving ◽  
Large Scale ◽  
Simulation Analysis ◽  
Clustering Algorithms ◽  
Optimal Performance ◽  
Wireless Sensor ◽  
Clustering Method

Abstract Despite the success of various clustering algorithms for Wireless Sensor Networks (WSNs), there are few works that consider the interference between clusters. Obviously, interference-free clustering makes the communication more efficient and achieves energy saving. In this paper we propose a new clustering method for large-scale sensor networks. With this method the network is partitioned into clusters. Intra-cluster communication in a cluster has no interference by its neighbor clusters. Moreover, the proposed clustering is based on a Genetic Algorithm (GA), which can achieve optimal performance in terms of the number of isolated nodes. This is demonstrated by the simulation analysis.

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