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

Sadik Kamel Gharghan; Rosdiadee Nordin; Mahamod Ismail

doi:10.3390/s16081043

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

Sensors (Basel). 2016 Aug 6;16(8):1043. doi: 10.3390/s16081043.

Authors

Sadik Kamel Gharghan^{1

2}, Rosdiadee Nordin³, Mahamod Ismail⁴

Affiliations

¹ Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor 43600, Malaysia. sadiq@siswa.ukm.edu.my.
² College of Electrical and Electronic Engineering Techniques, Middle Technical University, Al Doura 10022, Baghdad, Iraq. sadiq@siswa.ukm.edu.my.
³ Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor 43600, Malaysia. adee@ukm.edu.my.
⁴ Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor 43600, Malaysia. mahamod@ukm.edu.my.

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.

Keywords: WSN; cycling; distance estimation; optimization technique; soft computing.