LBR‐GWO : Layered based routing approach using grey wolf optimization algorithm in wireless sensor networks

2021 ◽  
Author(s):  
Bhanu Dwivedi ◽  
Bachu Dushmanta Kumar Patro ◽  
Vivek Srivastava ◽  
Shimpi Singh Jadon
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Wireless Sensor ◽  
Grey Wolf ◽  
Grey Wolf Optimization

scholarly journals A Novel Coverage Optimization Strategy Based on Grey Wolf Algorithm Optimized by Simulated Annealing for Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yong Zhang ◽  
Li Cao ◽  
Yinggao Yue ◽  
Yong Cai ◽  
Bo Hang
Keyword(s):  
Wireless Sensor Networks ◽  
Simulated Annealing ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Coverage Optimization ◽  
Grey Wolf Algorithm

The coverage optimization problem of wireless sensor network has become one of the hot topics in the current field. Through the research on the problem of coverage optimization, the coverage of the network can be improved, the distribution redundancy of the sensor nodes can be reduced, the energy consumption can be reduced, and the network life cycle can be prolonged, thereby ensuring the stability of the entire network. In this paper, a novel grey wolf algorithm optimized by simulated annealing is proposed according to the problem that the sensor nodes have high aggregation degree and low coverage rate when they are deployed randomly. Firstly, the mathematical model of the coverage optimization of wireless sensor networks is established. Secondly, in the process of grey wolf optimization algorithm, the simulated annealing algorithm is embedded into the grey wolf after the siege behavior ends and before the grey wolf is updated to enhance the global optimization ability of the grey wolf algorithm and at the same time improve the convergence rate of the grey wolf algorithm. Simulation experiments show that the improved grey wolf algorithm optimized by simulated annealing is applied to the coverage optimization of wireless sensor networks. It has better effect than particle swarm optimization algorithm and standard grey wolf optimization algorithm, has faster optimization speed, improves the coverage of the network, reduces the energy consumption of the nodes, and prolongs the network life cycle.

scholarly journals Sensor Duty Cycle for Prolonging Network Lifetime Using Quantum Clone Grey Wolf Optimization Algorithm in Industrial Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yang Liu ◽  
Jing Xiao ◽  
Chaoqun Li ◽  
Hu Qin ◽  
Jie Zhou
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Duty Cycle ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Maintenance Cost ◽  
Local Optimum ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Industrial Wireless Sensor Networks

The application of industrial wireless sensor networks (IWSNs) frequently appears in modern industry, and it is usually to deploy a large quantity of sensor nodes in the monitoring area. This way of deployment improves the robustness of the IWSNs but introduces many redundant nodes, thereby increasing unnecessary overhead. The purpose of this paper is to increase the lifetime of IWSNs without changing the physical facilities and ensuring the coverage of sensors as much as possible. Therefore, we propose a quantum clone grey wolf optimization (QCGWO) algorithm, design a sensor duty cycle model (SDCM) based on real factory conditions, and use the QCGWO to optimize the SDCM. Specifically, QCGWO combines the concept of quantum computing and the clone operation for avoiding the algorithm from falling into a local optimum. Subsequently, we compare the proposed algorithm with the genetic algorithm (GA) and simulated annealing (SA) algorithm. The experimental results suggest that the lifetime of the IWSNs based on QCGWO is longer than that of GA and SA, and the convergence speed of QCGWO is also faster than that of GA and SA. In comparison with the traditional IWSN working mode, our model and algorithm can effectively prolong the lifetime of IWSNs, thus greatly reducing the maintenance cost without replacing sensor nodes in actual industrial production.

scholarly journals Grey Wolf Optimization Based Energy Efficiency Management System for Wireless Sensor Networks

2021 ◽  
Author(s):  
S. Jaya Pratha ◽  
V. Asanambigai ◽  
S.R. Mugunthan
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Simulation Analysis ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Battery Lifetime ◽  
Selection Of

Abstract Wireless Sensor Networks (WSN) is the fundamental technology for the Internet of Things (IoT). It is a network formed from several sensor nodes to sense the changes in the environment. The nodes are battery powered that performs sensing and transmission of information to other nodes in the network. Thus, the energy of the sensor node plays a crucial role in WSN. Thus, intelligent models are anticipated to solve the network problems by optimizing or minimizing the mechanism inorder to improve the energy efficiency. In this paper, a combined meta-heuristic approach called Grey Wolf Optimization based Game theoretical Approach (GWOGA) is proposed that helps for clustering to find the best solutions for selection of aggregation points and this optimal selection of aggregation points lead the nodes to maximize its battery/lifetime. Experimental and simulation analysis shows that the GWOGA outperforms the existing models and retains the lifetime of the network.

Sign in / Sign up

Export Citation Format

Share Document