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Appl. Sci. 2016, 6(2), 37; doi:10.3390/app6020037

Distributed Global Function Model Finding for Wireless Sensor Network Data

1,* , 2,†
,
1,†
,
3,†
and
4,†
1
Institute of Advanced Technology, Nanjing University Post & Telecommunication, Nanjing 210003, China
2
International Institute for Earth System Science, Nanjing University, Nanjing 210093, China
3
School of Computer, Nanjing University Post & Telecommunication, Nanjing 210003, China
4
School of Engineering and Built Environment, Glasgow Caledonian University, Scotland G40BA, UK
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Christos Verikoukis
Received: 7 December 2015 / Revised: 12 January 2016 / Accepted: 15 January 2016 / Published: 28 January 2016
View Full-Text   |   Download PDF [5062 KB, uploaded 28 January 2016]   |  

Abstract

Function model finding has become an important tool for analysis of data collected from wireless sensor networks (WSNs). With the development of WSNs, a large number of sensors have been widely deployed so that the collected data show the characteristics of distribution and mass. For distributed and massive sensor data, traditional centralized function model finding algorithms would lead to a significant decrease in performance. To solve this problem, this paper proposes a distributed global function model finding algorithm for wireless sensor network data (DGFMF-WSND). In DGFMF-WSND, on the basis of gene expression programming (GEP), an adaptive population generation strategy based on sub-population associated evolution is applied to improve the convergence speed of GEP. Secondly, to solve the generation of global function model in distributed wireless sensor networks data, this paper provides a global model generation algorithm based on unconstrained nonlinear least squares. Four representative datasets are used to evaluate the performance of the proposed algorithm. The comparative results show that the improved GEP with adaptive population generation strategy outperforms all other algorithms on the average convergence speed, time-consumption, value of R-square, and prediction accuracy. Meanwhile, experimental results also show that DGFMF-WSND has a clear advantage in terms of time-consumption and error of fitting. Moreover, with increasing of dataset size, DGFMF-WSND also demonstrates good speed-up ratio and scale-up ratio. View Full-Text
Keywords: global function model; gene expression programming; unconstrained nonlinear least squares; wireless sensor network global function model; gene expression programming; unconstrained nonlinear least squares; wireless sensor network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Deng, S.; Yang, L.-C.; Yue, D.; Fu, X.; Ma, Z. Distributed Global Function Model Finding for Wireless Sensor Network Data. Appl. Sci. 2016, 6, 37.

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