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Research on Node Location Algorithm of Zigbee Based on PSO-GRNN Neural Network

Published: 29 December 2018 Publication History

Abstract

The parameters of the traditional wireless signal propagation model are generally obtained by fitting or directly based on experience, and are affected by factors such as complex environment and multipath effect, which makes the positioning accuracy is not high. In view of this, a new node location algorithm of Particle Swarm Optimization-Generalized Regression Neural Network (PSO-GRNN) was presented. The RSSI received at each reference point is taken as the input of the network, the position coordinates of which are used as the output of the network to construct the GRNN, training the neural network by PSO algorithm with linear decreasing inertia weight, and the best Spread is cycled, avoid interference from human factors when adjusting this parameter, finally the trained model using the forecast point positioning. Simulation verified by MATLAB and Zigbee experiments, compared with the non-optimized GRNN model and BP neural network model, this algorithm has higher positioning accuracy in node localization.

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  1. Research on Node Location Algorithm of Zigbee Based on PSO-GRNN Neural Network

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      cover image ACM Other conferences
      ISBDAI '18: Proceedings of the International Symposium on Big Data and Artificial Intelligence
      December 2018
      365 pages
      ISBN:9781450365703
      DOI:10.1145/3305275
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      • International Engineering and Technology Institute, Hong Kong: International Engineering and Technology Institute, Hong Kong

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 29 December 2018

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      Author Tags

      1. GRNN
      2. Node location
      3. PSO
      4. RSSI
      5. Smoth factor
      6. wireless signal propagation model

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      ISBDAI '18

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      ISBDAI '18 Paper Acceptance Rate 70 of 340 submissions, 21%;
      Overall Acceptance Rate 70 of 340 submissions, 21%

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