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 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>

scholarly journals Localization of Large-Scale Wireless Sensor Networks Using Niching Particle Swarm Optimization and Reliable Anchor Selection

2018 ◽  
Vol 2018 ◽  
pp. 1-18
Author(s):  
Huanqing Cui ◽  
Yongquan Liang ◽  
Chuanai Zhou ◽  
Ning Cao
Keyword(s):  
Wireless Sensor Networks ◽  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Large Scale ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Swarm Optimization ◽  
Localization Accuracy ◽  
Unknown Node

Due to uneven deployment of anchor nodes in large-scale wireless sensor networks, localization performance is seriously affected by two problems. The first is that some unknown nodes lack enough noncollinear neighbouring anchors to localize themselves accurately. The second is that some unknown nodes have many neighbouring anchors to bring great computing burden during localization. This paper proposes a localization algorithm which combined niching particle swarm optimization and reliable reference node selection in order to solve these problems. For the first problem, the proposed algorithm selects the most reliable neighbouring localized nodes as the reference in localization and using niching idea to cope with localization ambiguity problem resulting from collinear anchors. For the second problem, the algorithm utilizes three criteria to choose a minimum set of reliable neighbouring anchors to localize an unknown node. Three criteria are given to choose reliable neighbouring anchors or localized nodes when localizing an unknown node, including distance, angle, and localization precision. The proposed algorithm has been compared with some existing range-based and distributed algorithms, and the results show that the proposed algorithm achieves higher localization accuracy with less time complexity than the current PSO-based localization algorithms and performs well for wireless sensor networks with coverage holes.

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