Earticle

초록

영어

With the development of sensor technology, network technology, embedded control technology and wireless communication technology, the application of wireless sensor networks (WSN) has become more and more widely. Wireless sensor networks have been named the most influential and important technology of the world in twenty-first Century. In wireless sensor networks, data fusion is an important research branch. In this paper, a data prediction model of wireless sensor network based on weight improved particle swarm optimization neural network algorithm is proposed. In view of the deficiency of the traditional BP neural network model, this paper combines with the characteristics of the data prediction model, and the BP neural network model is improved and integrated. After that, we train the neural network's sample set, and add the momentum item to correct the weight, so that the neural network can be predicted more quickly and accurately. The main idea of this paper is to predict the future data based on the historical data which are collected by sensor nodes, so as to achieve the purpose of reducing the amount of data transmission in the network and saving the energy of nodes. Finally, the experimental results show that the improved particle swarm optimization algorithm based on weight improved particle swarm optimization neural network algorithm has higher accuracy than the multiple regression method and the grey prediction method. In addition, the method can be used to effectively save energy in wireless sensor data transmission.

목차

Abstract
 1. Introduction
 2. Basic Knowledge of Wireless Sensor Networks
 3. Neural Network Mode
 4. Particle Swarm Optimization Algorithm
 5. The Weight Improved Particle Swarm Neural Network Algorithm
 6. Simulation Experiment and Result Analysis
  6.1. Parameter Definition and Simulation Flow
  6.2 Training Model
  6.3 Prediction and Performance Analysis
 7. Conclusion
 Acknowledgement
 Reference

키워드

저자

• Xiajun Ding [ College of Electrical and Information Engineering, Quzhou University, Quzhou, Zhejiang, China ]
• Hongbo Bi [ College of Electrical and Information Engineering, Quzhou University, Quzhou, Zhejiang, China ]
• Xiaodan Jiang [ College of Electrical and Information Engineering, Quzhou University, Quzhou, Zhejiang, China ]
• Lu zhang [ College of Electrical and Information Engineering, Quzhou University, Quzhou, Zhejiang, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Future Generation Communication and Networking

• 간기

  격월간

• pISSN

  2233-7857

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605