scholarly journals A Novel Energy Aware Node Clustering Algorithm for Wireless Sensor Networks Using a Modified Artificial Fish Swarm Algorithm

2015 ◽  
Vol 7 (3) ◽  
pp. 91-103 ◽  
Author(s):  
Reza Azizi ◽  
Hasan Sedghi ◽  
Hamid Shoja ◽  
Alireza Sepas-Moghaddam
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Clustering Algorithm ◽  
Wireless Sensor ◽  
Energy Aware ◽  
Artificial Fish Swarm Algorithm ◽  
Node Clustering ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

scholarly journals A novel topology optimization of coverage-oriented strategy for wireless sensor networks

2021 ◽  
Vol 17 (4) ◽  
pp. 155014772199229
Author(s):  
Shuxin Wang ◽  
Hairong You ◽  
Yinggao Yue ◽  
Li Cao
Keyword(s):  
Wireless Sensor Networks ◽  
Topology Optimization ◽  
Sensor Networks ◽  
Sensor Node ◽  
Particle Swarm Optimization Algorithm ◽  
Wireless Sensor ◽  
Swarm Optimization ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

Aiming at the key optimization problems of wireless sensor networks in complex industrial application environments, such as the optimum coverage and the reliability of the network, a novel topology optimization of coverage-oriented strategy for wireless sensor networks based on the wolf pack algorithm is proposed. Combining the characteristics of topology structure of wireless sensor networks and the optimization idea of the wolf pack algorithm redefines the group’s wandering and surprise behavior. A novel head wolf mutation strategy is proposed, which increases the neighborhood search range of the optimal solution, enhances the uniformity of wolf pack distribution and the ergodicity ability of the wolf pack search, and greatly improves the calculation speed and the accuracy of the wolf pack algorithm. With the same probability, the cluster heads are randomly selected periodically, and the overall energy consumption of wireless sensor networks is evenly distributed to the sensor node to realize the balanced distribution of the data of the member nodes in the cluster and complete the design of the topology optimization of wireless sensor networks. Through algorithm simulation and result analysis, compared with the particle swarm optimization algorithm and artificial fish swarm algorithm, the wolf swarm algorithm shows its advantages in terms of the residual energy of the sensor node, the average transmission delay, the average packet delivery rate, and the coverage of the network. Among them, compared with the particle swarm optimization algorithm and artificial fish swarm algorithm, the remaining energy of nodes has increased by 9.5% and 15.5% and the average coverage of the network has increased by 10.5% and 5.6%, respectively.

scholarly journals Energy-Aware Adaptive Weighted Grid Clustering Algorithm for Renewable Wireless Sensor Networks

2017 ◽  
Vol 9 (4) ◽  
pp. 54 ◽  
Author(s):  
Nelofar Aslam ◽  
Kewen Xia ◽  
Muhammad Haider ◽  
Muhammad Hadi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Clustering Algorithm ◽  
Wireless Sensor ◽  
Energy Aware
Sign in / Sign up

Export Citation Format

Share Document