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Sensors 2016, 16(8), 1043;

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

Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor 43600, Malaysia
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)
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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

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

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