A Node Localization Algorithm for Wireless Sensor Network Based on Improved Particle Swarm Optimization

2013 ◽  
pp. 135-144 ◽  
Author(s):  
Qing-guo Zhang ◽  
Meng Cheng
Keyword(s):  
Particle Swarm Optimization ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Node Localization ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

scholarly journals On the Localization Algorithm of Wireless Sensor Network and Its Application

2017 ◽  
Vol 13 (03) ◽  
pp. 40 ◽  
Author(s):  
Honglei Jia ◽  
Jiaxin Zheng ◽  
Gang Wang ◽  
Yulong Chen ◽  
Dongyan Huang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Simulation Experiment ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Node Localization ◽  
Swarm Optimization

<span style="font-family: 'Times New Roman',serif; font-size: 12pt; mso-fareast-font-family: SimSun; mso-fareast-theme-font: minor-fareast; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;">This paper carries out in-depth and meticulous analysis of the DV-Hop localization algorithm for wireless sensor network. It improves the DV-Hop algorithm into a node localization algorithm based on one-hop range, and proposes the centroid particle swarm optimization localization algorithm based on RSSI by adding the RSSI and particle swarm optimization algorithm to the traditional centroid localization algorithm. Simulation experiment proves that the two algorithms have excellent effect.</span>

Research on Particle Swarm Optimization-Based Node Positioning Algorithm for Wireless Sensor Networks

2014 ◽  
Vol 548-549 ◽  
pp. 1415-1419 ◽  
Author(s):  
Jie He ◽  
La Yuan Li
Keyword(s):  
Wireless Sensor Networks ◽  
Particle Swarm Optimization ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Node Localization ◽  
Swarm Optimization

In many instances, as special applications of wireless sensor networks, wireless sensor networks need to know the location of nodes. A wireless sensor network localization algorithm based on Particle Swarm Optimization is proposed in this thesis to solve the problem of inaccurate positioning and large energy consumption for wireless sensor network node positioning. The algorithm combines the particle swarm optimization algorithm (PSO) and node localization algorithm to improve the positioning accuracy.

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.

Sign in / Sign up

Export Citation Format

Share Document