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author:

Li, Q. (Li, Q..) [1] | Chen, Z. (Chen, Z..) [2] | Wu, L. (Wu, L..) [3] | Cheng, S. (Cheng, S..) [4] | Lin, P. (Lin, P..) [5]

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Scopus

Abstract:

Structural health monitoring (SHM) is a kind of data-intensive applications for wireless sensors networks, which usually requires high network bandwidth. However, the bandwidth of traditional single-radio single-channel (SR-SC) WSN is quite limited. In order to meet the requirement of structural monitoring, multi-radio multi-channel (MR-MC) WSN is emerging. In this paper, we address the optimal data collection problem in MR-MC WSN by modelling it as an integer linear programming problem. Combining the advantages of the particle swarm optimization (PSO) algorithm and flower pollination optimization (FPA) algorithm, we propose a new hybrid algorithm BFPA-PSO to solve the optimization problem under the constraint of time slot and multi-power multi-radio multi-channel (MP-MR-MC). Theoretical analysis and simulation experiments are carried out and the results show that the proposed method has good performance in improving network capacity as well as reducing energy consumption. © Springer Nature Switzerland AG 2018.

Keyword:

BFPA-PSO; Data collection; MR-MC; Structural health monitoring

Community:

  • [ 1 ] [Li, Q.]College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350116, China
  • [ 2 ] [Chen, Z.]College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350116, China
  • [ 3 ] [Wu, L.]College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350116, China
  • [ 4 ] [Cheng, S.]College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350116, China
  • [ 5 ] [Lin, P.]College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350116, China

Reprint 's Address:

  • [Chen, Z.]College of Physics and Information Engineering, Fuzhou UniversityChina

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Source :

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISSN: 0302-9743

Year: 2018

Volume: 11248 LNAI

Page: 92-103

Language: English

0 . 4 0 2

JCR@2005

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 6

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