Adapted WLAN Fingerprint Indoor Positioning System (IPS) Based on User Orientations

Firdaus, -; Ahmad, Noor Azurati; Sahibuddin, Shamsul

doi:10.1007/978-3-319-59427-9_25

Cited by 4 publications

(5 citation statements)

References 15 publications

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“…Although fingerprinting is one of the most accurate positioning methods, the accuracy can decline due to many factors such as: occurrence of environmental changes, received signal strength (RSS) variation due to MDs’ heterogeneity [ 14 ], and the people presence effect (PPE) on RSS [ 15 , 16 , 17 ]. Such factors will affect the physical effects (reflection, refraction, diffraction, etc.)…”

Section: Introductionmentioning

confidence: 99%

Adaptive Indoor Positioning Model Based on WLAN-Fingerprinting for Dynamic and Multi-Floor Environments

Alshami

Ahmad

Sahibuddin

et al. 2017

Sensors

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The Global Positioning System demonstrates the significance of Location Based Services but it cannot be used indoors due to the lack of line of sight between satellites and receivers. Indoor Positioning Systems are needed to provide indoor Location Based Services. Wireless LAN fingerprints are one of the best choices for Indoor Positioning Systems because of their low cost, and high accuracy, however they have many drawbacks: creating radio maps is time consuming, the radio maps will become outdated with any environmental change, different mobile devices read the received signal strength (RSS) differently, and peoples’ presence in LOS between access points and mobile device affects the RSS. This research proposes a new Adaptive Indoor Positioning System model (called DIPS) based on: a dynamic radio map generator, RSS certainty technique and peoples’ presence effect integration for dynamic and multi-floor environments. Dynamic in our context refers to the effects of people and device heterogeneity. DIPS can achieve 98% and 92% positioning accuracy for floor and room positioning, and it achieves 1.2 m for point positioning error. RSS certainty enhanced the positioning accuracy for floor and room for different mobile devices by 11% and 9%. Then by considering the peoples’ presence effect, the error is reduced by 0.2 m. In comparison with other works, DIPS achieves better positioning without extra devices.

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Section: Introductionmentioning

confidence: 99%

Adaptive Indoor Positioning Model Based on WLAN-Fingerprinting for Dynamic and Multi-Floor Environments

Alshami

Ahmad

Sahibuddin

et al. 2017

Sensors

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“…Firdaus [24] proposed a method to adapt user orientation in IPS to improve the accuracy of IPS. This method was based on path loss model, including the signal attenuation that comes from human budy.…”

Section: Methodsmentioning

confidence: 99%

“…Users (people who hold MDs) and people around the user will influence the accuracy of IPS because people affect the RSSI. Firdaus et al [24] proposed a method to adapt user orientation in IPS to improve the accuracy of IPS. However they did not discuss computation time in the system, even though it is a very important parameter in smartphone application because it can decrease the power consumption by minimizing the computation time.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint indoor positioning based on user orientations and minimum computation time

2019

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Indoor Positioning System (IPS) has an important role in the field of Internet of Thing. IPS works based on many existing radio frequency technologies. One of the most popular methods is WLAN Fingerprint because this technology has been installed widely inside buildings and it provides a high level of accuracy. The performance is affected by people who hold mobile devices (user) and also people around the users. This research aimed to minimize the computation time of kNN searching process. The results showed that when the value of k in kNN was greater, the computation time increased, especially when using Cityblock and Minkowski distance function. The smallest average computation time was 2.14 ms, when using Cityblock. Then the computational time for Euclidean and Chebychev were relatively stable, i.e. 2.2 ms and 2.23 ms, respectively.

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“…Firdaus et al [28] introduced signal strength values' adaptation based on user orientation to overcome its effect on the indoor positioning system (IPS) accuracy. Abkari [29] provided a compromise between feasibility and accuracy.…”

Section: Positioning Using Time-delay Multi-layer Perception Neural Networkmentioning

confidence: 99%

Real-time RSS-based positioning system using neural network algorithm

Abkari

Mhamdi

Abkari

2021

IJEECS

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Locating services have come under the spotlight in recent years in various applications. However, locating methods that use received signal strength have low accuracy due to signal fluctuations. For this purpose, we present a Wi-Fi based locating system using artificial neural network to enhance the positioning process performances. We optimized the Levenberg Marquardt algorithm to propose the better configuration of the multi-layer time-delay perception neural network. We achieved an average error of 10.3 centimeters with a grid of 0.4 meter in four tests. Yet, due to the instability of the received signal strength RSS-based locating systems present a limitation in the resolution finesse that depends on the grid size.

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Adapted WLAN Fingerprint Indoor Positioning System (IPS) Based on User Orientations

Cited by 4 publications

References 15 publications

Adaptive Indoor Positioning Model Based on WLAN-Fingerprinting for Dynamic and Multi-Floor Environments

Adaptive Indoor Positioning Model Based on WLAN-Fingerprinting for Dynamic and Multi-Floor Environments

Fingerprint indoor positioning based on user orientations and minimum computation time

Real-time RSS-based positioning system using neural network algorithm

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