Abstract
Access points in wireless local area networks are deployed in many indoor environments. Device-free wireless localization systems based on available received signal strength indicators have gained considerable attention recently because they can localize the people using commercial off-the-shelf equipment. Majority of localization algorithms consider two-dimensional models that cause low positioning accuracy. Although three-dimensional localization models are available, they possess high computational and localization errors, given their use of numerous reference points. In this work, we propose a three-dimensional indoor localization system based on a Bayesian graphical model. The proposed model has been tested through experiments based on fingerprinting technique which collects received signal strength indicators from each access point in an offline training phase and then estimates the user location in an online localization phase. Results indicate that the proposed model achieves a high localization accuracy of more than 25% using reference points fewer than that of benchmarked algorithms.
Highlights
Positioning systems are a compelling area of research because they are part of the Internet of things technology
The results demonstrate that the proposed 3D Bayesian graphical model (3D-BGM) achieves a significant reduction of localization error in comparison with other algorithms for all different sets
The 3DBGM based on the radio frequency (RF) fingerprinting technique used available access points (APs) already deployed in the environment to estimate user location without additional external devices
Summary
Positioning systems are a compelling area of research because they are part of the Internet of things technology. Indoor positioning issues have received considerable research attention, given the extensive use of wireless local area networks (WLANs) in most indoor environments, such as shopping malls, hospitals and universities. Several indoor localization technologies require dedicated infrastructures, such as Wi-Fi, ultrasound signal, ZigBee and ultra-wideband.[5] These technologies have high requirements for the environment and require additional equipment, thereby providing them and other proposed systems with a high level of complexity and inferior accuracy. These technologies mainly focus on two-dimensional (2D) planes, whereas three-dimensional (3D) environments are more complicated and significantly increase computational complexity. The conclusion drawn from the current work and brief discussion of future work are presented in section ‘Conclusion’
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