Hybrid indoor-based WLAN-WSN localization scheme for improving accuracy based on artificial neural network

Zahid Farid, Rosdiadee Nordin, Mahamod Ismail, Nor Fadzilah Abdullah

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

In indoor environments, WiFi (RSS) based localization is sensitive to various indoor fading effects and noise during transmission, which are the main causes of localization errors that affect its accuracy. Keeping in view those fading effects, positioning systems based on a single technology are ineffective in performing accurate localization. For this reason, the trend is toward the use of hybrid positioning systems (combination of two or more wireless technologies) in indoor/outdoor localization scenarios for getting better position accuracy. This paper presents a hybrid technique to implement indoor localization that adopts fingerprinting approaches in both WiFi and Wireless Sensor Networks (WSNs). This model exploits machine learning, in particular Artificial Natural Network (ANN) techniques, for position calculation. The experimental results show that the proposed hybrid system improved the accuracy, reducing the average distance error to 1.05 m by using ANN. Applying Genetic Algorithm (GA) based optimization technique did not incur any further improvement to the accuracy. Compared to the performance of GA optimization, the nonoptimized ANN performed better in terms of accuracy, precision, stability, and computational time. The above results show that the proposed hybrid technique is promising for achieving better accuracy in real-world positioning applications.

Original languageEnglish
Article number6923931
JournalMobile Information Systems
Volume2016
DOIs
Publication statusPublished - 2016

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Wireless local area networks (WLAN)
Wireless sensor networks
Genetic algorithms
RSS
Neural networks
Hybrid systems
Learning systems

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

Cite this

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title = "Hybrid indoor-based WLAN-WSN localization scheme for improving accuracy based on artificial neural network",
abstract = "In indoor environments, WiFi (RSS) based localization is sensitive to various indoor fading effects and noise during transmission, which are the main causes of localization errors that affect its accuracy. Keeping in view those fading effects, positioning systems based on a single technology are ineffective in performing accurate localization. For this reason, the trend is toward the use of hybrid positioning systems (combination of two or more wireless technologies) in indoor/outdoor localization scenarios for getting better position accuracy. This paper presents a hybrid technique to implement indoor localization that adopts fingerprinting approaches in both WiFi and Wireless Sensor Networks (WSNs). This model exploits machine learning, in particular Artificial Natural Network (ANN) techniques, for position calculation. The experimental results show that the proposed hybrid system improved the accuracy, reducing the average distance error to 1.05 m by using ANN. Applying Genetic Algorithm (GA) based optimization technique did not incur any further improvement to the accuracy. Compared to the performance of GA optimization, the nonoptimized ANN performed better in terms of accuracy, precision, stability, and computational time. The above results show that the proposed hybrid technique is promising for achieving better accuracy in real-world positioning applications.",
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AU - Nordin, Rosdiadee

AU - Ismail, Mahamod

AU - Abdullah, Nor Fadzilah

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