A wireless sensor network with soft computing localization techniques for track cycling applications

Sadik Kamel Gharghan, Rosdiadee Nordin, Mahamod Ismail

Research output: Contribution to journalArticle

16 Citations (Scopus)

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.

Original languageEnglish
Article number1043
JournalSensors (Switzerland)
Volume16
Issue number8
DOIs
Publication statusPublished - 6 Aug 2016

Fingerprint

Soft computing
Wireless sensor networks
cycles
sensors
Neural networks
Track and Field
bicycle
optimization
Bicycles
Zigbee
Fuzzy inference
Anchors
inference
Error analysis
Particle swarm optimization (PSO)
estimates

Keywords

  • Cycling
  • Distance estimation
  • Optimization technique
  • Soft computing
  • WSN

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics
  • Analytical Chemistry
  • Biochemistry

Cite this

A wireless sensor network with soft computing localization techniques for track cycling applications. / Gharghan, Sadik Kamel; Nordin, Rosdiadee; Ismail, Mahamod.

In: Sensors (Switzerland), Vol. 16, No. 8, 1043, 06.08.2016.

Research output: Contribution to journalArticle

@article{72ce940100114582963e851d383b566f,
title = "A wireless sensor network with soft computing localization techniques for track cycling applications",
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 = "Cycling, Distance estimation, Optimization technique, Soft computing, WSN",
author = "Gharghan, {Sadik Kamel} and Rosdiadee Nordin and Mahamod Ismail",
year = "2016",
month = "8",
day = "6",
doi = "10.3390/s16081043",
language = "English",
volume = "16",
journal = "Sensors (Switzerland)",
issn = "1424-8220",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "8",

}

TY - JOUR

T1 - A wireless sensor network with soft computing localization techniques for track cycling applications

AU - Gharghan, Sadik Kamel

AU - Nordin, Rosdiadee

AU - Ismail, Mahamod

PY - 2016/8/6

Y1 - 2016/8/6

N2 - 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.

AB - 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.

KW - Cycling

KW - Distance estimation

KW - Optimization technique

KW - Soft computing

KW - WSN

UR - http://www.scopus.com/inward/record.url?scp=84982833255&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84982833255&partnerID=8YFLogxK

U2 - 10.3390/s16081043

DO - 10.3390/s16081043

M3 - Article

VL - 16

JO - Sensors (Switzerland)

JF - Sensors (Switzerland)

SN - 1424-8220

IS - 8

M1 - 1043

ER -