A Particle Swarm Optimization Algorithm for Deployment of Sensor Nodes in WSN Network

Author:

Liang Jie12,Wang Lu1ORCID,Ji Qingchang1

Affiliation:

1. College of Electrical and Automation Engineering, Hebei University of Water Resources and Electric Engineering, Cangzhou 061001, China

2. Water Resources Automation and Informatization Application Technology Research and Development Center of Hebei Colleges, Cangzhou 061001, China

Abstract

In order to solve the problems of security hidden dangers in the process of node location due to the characteristics of limited resources, open deployment, and being unattended in wireless sensor networks, this paper proposes a mainstream location algorithm combined with the current WSN node. By reducing the error in network positioning, the wireless sensor network positioning technology is put into practical benefits, and the WSN emitter positioning based on node resources and limited capacity is realized. Some positioning technologies are applied to emitter positioning, and some meaningful results are obtained. Aiming at the problems existing in the positioning algorithm of the main nodes in the wireless sensor network, the power consumption and positioning accuracy of the positioning technology are deeply studied to reduce the positioning error. Experiments show that when nodes are sent to different states, the number of nodes 150 remains the same, the communication radius is the same, the environment output is the same, and the number of bones in the network can be changed. After many simulation experiments, both algorithms can see the positioning result curve of the positioning program affected by the anchor node part.

Funder

Youth Fund Projects of Water Resources Automation and Informatization Application Technology Research and Development Center of Hebei Colleges

Publisher

Hindawi Limited

Subject

Electrical and Electronic Engineering,General Computer Science,Signal Processing

Reference26 articles.

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2. Identifying Top-k Influential Nodes Based on Discrete Particle Swarm Optimization With Local Neighborhood Degree Centrality

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4. Deployment and optimization of wireless network node deployment and optimization in smart cities

5. Mobile Robot Path Planning Based on Improved Localized Particle Swarm Optimization

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