Next Article in Journal
Novel Low-Cost Sensor for Human Bite Force Measurement
Next Article in Special Issue
Practical Performance Analysis for Multiple Information Fusion Based Scalable Localization System Using Wireless Sensor Networks
Previous Article in Journal
Generation of Localized Surface Plasmon Resonance Using Hybrid Au–Ag Nanoparticle Arrays as a Sensor of Polychlorinated Biphenyls Detection
Previous Article in Special Issue
Gaussian Process Regression Plus Method for Localization Reliability Improvement
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(8), 1043; doi:10.3390/s16081043

A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications

1
Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor 43600, Malaysia
2
College of Electrical and Electronic Engineering Techniques, Middle Technical University, Al Doura 10022, Baghdad, Iraq
*
Author to whom correspondence should be addressed.
Academic Editor: Lyudmila Mihaylova
Received: 15 April 2016 / Revised: 14 June 2016 / Accepted: 27 June 2016 / Published: 6 August 2016
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)

Abstract

In this paper, we propose two soft computing localization techniques for wireless sensor networks (WSNs). The two techniques, Neural Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), focus on a range-based localization method which relies on the measurement of the received signal strength indicator (RSSI) from the three ZigBee anchor nodes distributed throughout the track cycling field. The soft computing techniques aim to estimate the distance between bicycles moving on the cycle track for outdoor and indoor velodromes. In the first approach the ANFIS was considered, whereas in the second approach the ANN was hybridized individually with three optimization algorithms, namely Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Backtracking Search Algorithm (BSA). The results revealed that the hybrid GSA-ANN outperforms the other methods adopted in this paper in terms of accuracy localization and distance estimation accuracy. The hybrid GSA-ANN achieves a mean absolute distance estimation error of 0.02 m and 0.2 m for outdoor and indoor velodromes, respectively. View Full-Text
Keywords: cycling; distance estimation; optimization technique; soft computing; WSN cycling; distance estimation; optimization technique; soft computing; WSN
Figures

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Gharghan, S.K.; Nordin, R.; Ismail, M. A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications. Sensors 2016, 16, 1043.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top