A Novel Sensor Deployment Approach Using Fruit Fly Optimization Algorithm in Wireless Sensor Networks

2015 ◽  
Author(s):  
Huan Zhao ◽  
Qian Zhang ◽  
Liang Zhang ◽  
Yan Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Fruit Fly ◽  
Wireless Sensor ◽  
Sensor Deployment ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization

Proficient Clustering algorithm for Wireless Sensor Networks

2020 ◽  
pp. 249-261
Author(s):  
Nivetha Gopal ◽  
Venkatalakshmi Krishnan
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
Fruit Fly ◽  
Wireless Sensor ◽  
Fruit Fly Optimization Algorithm ◽  
Matlab Simulation ◽  
Fruit Fly Optimization

Enhancing the energy efficiency and maximizing the networking lifetime are the major challenges in Wireless Sensor Networks (WSN).Swarm Intelligence based algorithms are very efficient in solving nonlinear design problems with real-world applications.In this paper a Swarm based Fruit Fly Optimization Algorithm (FFOA) with the concept of K-Medoid clustering and swapping is implemented to increase the energy efficiency and lifetime of WSN. A comparative analysis is performed in terms of cluster compactness,cluster error and convergence. MATLAB Simulation results show that K-Medoid Swapping and Bunching Fruit Fly optimization (KMSB-FFOA) outperforms FFOA and K-Medoid Fruit Fly Optimization Algorithm (KM-FFOA).

Proficient Clustering algorithm for Wireless Sensor Networks

2017 ◽  
pp. 345-357
Author(s):  
Nivetha Gopal ◽  
Venkatalakshmi Krishnan
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
Fruit Fly ◽  
Wireless Sensor ◽  
Fruit Fly Optimization Algorithm ◽  
Matlab Simulation ◽  
Fruit Fly Optimization

Enhancing the energy efficiency and maximizing the networking lifetime are the major challenges in Wireless Sensor Networks (WSN).Swarm Intelligence based algorithms are very efficient in solving nonlinear design problems with real-world applications.In this paper a Swarm based Fruit Fly Optimization Algorithm (FFOA) with the concept of K-Medoid clustering and swapping is implemented to increase the energy efficiency and lifetime of WSN. A comparative analysis is performed in terms of cluster compactness,cluster error and convergence. MATLAB Simulation results show that K-Medoid Swapping and Bunching Fruit Fly optimization (KMSB-FFOA) outperforms FFOA and K-Medoid Fruit Fly Optimization Algorithm (KM-FFOA).

Coverage enhancing of 3D underwater sensor networks based on improved fruit fly optimization algorithm

2017 ◽  
Vol 21 (20) ◽  
pp. 6019-6029 ◽  
Author(s):  
Ying Zhang ◽  
Mingxing Wang ◽  
Jixing Liang ◽  
Haiyang Zhang ◽  
Wei Chen ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Optimization Algorithm ◽  
Fruit Fly ◽  
Underwater Sensor Networks ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization
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