An Improved Particle Swarm Optimization-Based Coverage Control Method for Wireless Sensor Network

2014 ◽  
pp. 114-124 ◽  
Author(s):  
Huimin Du ◽  
Qingjian Ni ◽  
Qianqian Pan ◽  
Yiyun Yao ◽  
Qing Lv
Keyword(s):  
Particle Swarm Optimization ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Control Method ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Coverage Control

scholarly journals Swarm intelligence–based energy efficient clustering with multihop routing protocol for sustainable wireless sensor networks

2020 ◽  
Vol 16 (9) ◽  
pp. 155014772094913
Author(s):  
Mohamed Elhoseny ◽  
R Sundar Rajan ◽  
Mohammad Hammoudeh ◽  
K Shankar ◽  
Omar Aldabbas
Keyword(s):  
Particle Swarm Optimization ◽  
Wireless Sensor Network ◽  
Swarm Intelligence ◽  
Sensor Network ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

Wireless sensor network is a hot research topic with massive applications in different domains. Generally, wireless sensor network comprises hundreds to thousands of sensor nodes, which communicate with one another by the use of radio signals. Some of the challenges exist in the design of wireless sensor network are restricted computation power, storage, battery and transmission bandwidth. To resolve these issues, clustering and routing processes have been presented. Clustering and routing processes are considered as an optimization problem in wireless sensor network which can be resolved by the use of swarm intelligence–based approaches. This article presents a novel swarm intelligence–based clustering and multihop routing protocol for wireless sensor network. Initially, improved particle swarm optimization technique is applied for choosing the cluster heads and organizes the clusters proficiently. Then, the grey wolf optimization algorithm–based routing process takes place to select the optimal paths in the network. The presented improved particle swarm optimization–grey wolf optimization approach incorporates the benefits of both the clustering and routing processes which leads to maximum energy efficiency and network lifetime. The proposed model is simulated under an extension set of experimentation, and the results are validated under several measures. The obtained experimental outcome demonstrated the superior characteristics of the improved particle swarm optimization–grey wolf optimization technique under all the test cases.

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