Throughput Enhancement in Wireless Sensor Network by Novel Genetic Algorithm-Fuzzy Method (NGA-F)

2021 ◽  
pp. 273-287
Author(s):  
N. Prakash ◽  
M. Rajalakshmi ◽  
R. Nedunchezhian
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Fuzzy Method ◽  
Throughput Enhancement

Routing Using Genetic Algorithm in a Wireless Sensor Network

2020 ◽  
Vol 111 (4) ◽  
pp. 2703-2732 ◽  
Author(s):  
Naveen Muruganantham ◽  
Hosam El-Ocla
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor

scholarly journals Node optimization coverage method under link model in passive monitoring system of three-dimensional wireless sensor network

2019 ◽  
Vol 15 (8) ◽  
pp. 155014771986987 ◽  
Author(s):  
Zhanjun Hao ◽  
Nanjiang Qu ◽  
Xiaochao Dang ◽  
Jiaojiao Hou
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Particle Swarm Optimization Algorithm ◽  
Three Dimensional ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Swarm Optimization ◽  
Passive Monitoring ◽  
Link Model

3D coverage is not only closer to the actual application environment, but also a research hotspot of sensor networks in recent years. For this reason, a node optimization coverage method under link model in passive monitoring system of three-dimensional wireless sensor network is proposed in this article. According to wireless link-aware area, the link coverage model in three-dimensional wireless sensor network is constructed, and the cube-based network coverage is used to represent the quality of service of the network. This model takes advantage of the principle that the presence of human beings can change the transmission channel of the link. On this basis, the intruder is detected by the data packets transmitted between the wireless links, and then the coverage area is monitored by monitoring the received signal strength of the wireless signal. Based on this new link awareness model, the problem of optimal coverage deployment of the receiving node is solved, that is, how to deploy the receiving node to achieve the optimal coverage of the monitoring area when the location of the sending node is given. In the process of optimal coverage, the traditional genetic algorithm and particle swarm optimization algorithm are introduced and improved. Based on the genetic algorithm, the particle swarm optimization algorithm which integrates the idea of simulated annealing is regarded as an important operator of the genetic algorithm, which can converge to the optimal solution quickly. The simulation results show that the proposed method can improve the network coverage, converge quickly, and reduce the network energy consumption. In addition, we set up a real experimental environment for coverage verification, and the experimental results verify the feasibility of the proposed method.

scholarly journals Exploration of Wireless Sensor Network Based on Cloud Computing

2018 ◽  
Vol 14 (11) ◽  
pp. 16
Author(s):  
Qing Wan ◽  
Ying Wang
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Energy State ◽  
Wireless Sensor ◽  
Cloud Environment ◽  
Application Service ◽  
Operation And Maintenance ◽  
Starting Point

To realize the exploration of wireless sensor network (WSN) based on cloud computing, the application service of WSN is taken as the starting point, the resource advantage of the cloud platform is used, and a WSN service framework based on cloud environment is proposed. Based on this framework, the problems of data management and reconstruction, network coverage optimization and monitoring, and edge recognition of holes are solved. In view of the node deployment of WSN and coverage problem of operation and maintenance optimization, the genetic algorithm is used to adjust the dormancy and energy state of nodes, and a parallel genetic algorithm for covering optimization in the cloud environment is proposed. For the operation and maintenance requirements of WSN, a parallel data statistics method for network monitoring is proposed. The experimental results show that the parallel algorithm is greatly improved in terms of the accuracy and time efficiency.

scholarly journals Wireless Sensor Network Route Optimization Based on Improved Ant Colony-Genetic

2015 ◽  
Vol 11 (9) ◽  
pp. 4 ◽  
Author(s):  
Wei Liu ◽  
Yongfeng Cui ◽  
Zhongyuan Zhao
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Optimization Problem ◽  
Ant Colony ◽  
Route Optimization ◽  
Wireless Sensor ◽  
Multi Objective Optimization ◽  
Mutation Operation

The objective of this paper is focuses on route optimization, for a given wireless sensor network. We detail the significance of route optimization problem and the corresponding mathematical model. After analyzing the complex multi-objective optimization problem, Ant Colony Optimization (ACO) algorithm was introduced to search the best route. Inspired by Genetic Algorithm (GA), we embed two operations into ACO to refine it. First, every ant after achieving sink will be regarded as an individual such as that in GA. The crossover operation will be applied and then, the generated new ants will replace the weaker parents. Second, we designed a mutation operation for ants selecting next nodes to visit. Experimental results demonstrate that the proposed combination algorithm has significant enhancements than both GA and ACO. The lifetime of WSN can be extended and the coverage speed can be accelerated.

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