The effect of people presence on WLAN RSS is governed by influence distance

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and high accuracy, however they have many drawbacks: creating radio maps is time consuming, the radio maps will become outdated with any environmental change, different mobile devices read the received signal strength (RSS) differently, and peoples’ presence in LOS between access points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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“…The model of the proposed system is illustrated in Figure 1 . It combines and extends our previous works described in [ 47 , 48 , 49 , 50 , 51 ].…”

Section: Methodssupporting

confidence: 60%

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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“…An experimental has been presented by Alshami et al [20] and showed that RSSI reduced in value (-2dBm to -5dBm) because of human's position between AP and MD. The value of RSSI is affected by the distance between the MD and the people [28]. The result is the improvement of positioning accuracy from 1.9m to 1.7m.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling the Effect of Human Body around User on Signal Strength and Accuracy of Indoor Positioning

Firdaus

Ahmad

Sahibuddin

et al. 2020

IJIE

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WLAN indoor positioning system (IPS) has high accurate of position estimation and minimal cost. However, environmental conditions such as the people presence effect (PPE) greatly influence WLAN signal and it will decrease the accuracy. This research modelled the effect of people around user on signal strength and the accuracy. We have modelled the human body around user effects by proposed a general equation of decrease in signal strength as function of position, distance, and number of people. Signal strength decreased from 5 dBm to 1 dBm when people in line of sight (LOS) position, and start from 0.5 dBm to 0.3 dBm when people in non-line of sight (NLOS) position. The system accuracy decreases due to the presence of people. When the system is in NLOS case, the presence of people causes a decrease in accuracy from 33% to 57%. Then the accuracy decrease from 273% to 334% in LOS case.

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“…Iyad et al [28] presented an experiment and found that people's presence between the AP and the MD reduced the received signal strength by −2 dBm to −5 dBm. Following that, Iyad et al [47] also performed other experiments to prove that the effect of human presence could be determined based on the distance between the MD and the people, also termed as the influence distance. Both studies explained the effect of people presence around the MD on RSSI and attempted to determine this effect on the quality of IPS.…”

Section: People Presence Effectmentioning

confidence: 99%

“…The result was improved positioning accuracy from 1.9 m to 1.7 m. Although the technique succeeded in improving accuracy, it still needs further research to obtain significant accuracy improvements. Iyad [47] subsequently used a multi-wall propagation model that only focuses on signals directly from the transmitter to the receiver. This current research proposes new techniques that consider the multipath effect such as reflection to overcome radio map generation and the issues of the people presence effect (PPE), to obtain a signal accuracy of less than 1 m.…”

Section: People Presence Effectmentioning

confidence: 99%

Accurate Indoor-Positioning Model Based on People Effect and Ray-Tracing Propagation

Firdaus

Ahmad

Sahibuddin

2019

Sensors

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Wireless local area networks (WLAN)-fingerprinting has been highlighted as the preferred technology for indoor positioning due to its accurate positioning and minimal infrastructure cost. However, its accuracy is highly influenced by obstacles that cause fluctuation in the signal strength. Many researchers have modeled static obstacles such as walls and ceilings, but few studies have modeled the people’s presence effect (PPE), although the human body has a great impact on signal strength. Therefore, PPE must be addressed to obtain accurate positioning results. Previous research has proposed a model to address this issue, but these studies only considered the direct path signal between the transmitter and the receiver whereas multipath effects such as reflection also have a significant influence on indoor signal propagation. This research proposes an accurate indoor-positioning model by considering people’s presence and multipath using ray-tracing, we call it (AIRY). This study proposed two solutions to construct AIRY: an automatic radio map using ray tracing and a constant of people’s effect for the received signal strength indicator (RSSI) adaptation. The proposed model was simulated using MATLAB software and tested at Level 3, Menara Razak, Universiti Teknologi Malaysia. A K-nearest-neighbor (KNN) algorithm was used to define a position. The initial accuracy was 2.04 m, which then reduced to 0.57 m after people’s presence and multipath effects were considered.

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The effect of people presence on WLAN RSS is governed by influence distance

Cited by 8 publications

References 26 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

Modelling the Effect of Human Body around User on Signal Strength and Accuracy of Indoor Positioning

Accurate Indoor-Positioning Model Based on People Effect and Ray-Tracing Propagation

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