Weight-RSS: A Calibration-Free and Robust Method for WLAN-Based Indoor Positioning

Zheng, Zengwei; Chen, Yuanyi; He, Tao; Li, Fēi; Chen, Dan

doi:10.1155/2015/573582

Cited by 21 publications

(29 citation statements)

References 22 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and high accuracy, however they have many drawbacks: creating radio maps is time consuming, the radio maps will become outdated with any environmental change, different mobile devices read the received signal strength (RSS) differently, and peoples’ presence in LOS between access points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

View full text Add to dashboard Cite

show abstract

“…Figure 8 shows the recommendation hit rate of the six recommendation algorithms. Note that we only perform experiments where the recommendation list ∈ [1,8], due to a greater value of , is usually ignored for top-k recommendation task since there is 60 POIs in total. It is apparent that all the six algorithms have significant performance disparity in terms of top-k hit rate.…”

Section: Resultsmentioning

confidence: 99%

“…This improvement is achieved by utilizing random walk with restart to derive pairwise score between each pair of users, which can solve the problem of data sparsity to a certain extent. Figure 9 reports the recommendation performance of the six recommendation algorithms; similarly, we only perform experiments where the recommendation list ∈ [1,8]. From this figure, we observe the following: (1) the performance of all the six algorithms for cold-start users degrades significantly compared to all users, showing data sparsity caused by cold-start users bring serious challenge for indoor POI recommendation.…”

Section: Resultsmentioning

confidence: 99%

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Location-Aware POI Recommendation for Indoor Space by Exploiting WiFi Logs

Zheng

Chen

et al. 2017

Mobile Information Systems

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Indoor shopping trajectories provide us with a new approach to understanding user's behaviour pattern in urban shopping mall, which can be derived from user-generated WiFi logs using indoor localization technology. In this paper, we propose a locationaware Point-of-Interest (POI) recommendation service in urban shopping mall that offers a user a set of indoor POIs by considering both personal interest and location preference. The POI recommendation service cannot only improve user's shopping experience but also help the store owner better understand user's shopping preference and intent. Specifically, the proposed method consists of two phases: offline modelling and online recommendation. The offline modelling phase is designed to learn user preference by mining his/her historical shopping trajectories. The online recommendation phase automatically produces top-recommended POIs based on the learnt preference. To demonstrate the utility of our proposed approach, we have performed a comprehensive experiment evaluation on a real-world dataset collected by 468 users over 33 days. The experimental results show that the proposed recommendation service achieves much better recommendation performance than several existing benchmark methods.

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“…Unlike the geometric SSD approach, Hossain et al [13] proposed a location fingerprint with SSD and demonstrated that it outperforms the traditional RSS fingerprint analytically and experimentally. Nevertheless, the SSD fingerprint amplifies the noise level and loses the discriminative information, leading to a potential reduction in accuracy compared to the raw RSS fingerprint with homogenous devices [28,29]. In this study, our PSSD system modifies the SSD fingerprint to make it more robust, and leverages the similarity metric to optimize the localization as well.…”

Section: Related Workmentioning

confidence: 99%

A Pairwise SSD Fingerprinting Method of Smartphone Indoor Localization for Enhanced Usability

et al. 2019

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Smartphone indoor localization has attracted considerable attention over the past decade because of the considerable business potential in terms of indoor navigation and location-based services. In particular, Wi-Fi RSS (received signal strength) fingerprinting for indoor localization has received significant attention in the industry, for its advantage of freely using off-the-shelf APs (access points). However, RSS measured by heterogeneous mobile devices is generally biased due to the variety of embedded hardware, leading to a systematical mismatch between online measures and the pre-established radio maps. Additionally, the fingerprinting method based on a single RSS measurement usually suffers from signal fluctuations due to environmental changes or human body blockage, leading to possible large localization errors. In this context, this study proposes a space-constrained pairwise signal strength differences (PSSD) strategy to improve Wi-Fi fingerprinting reliability, and mitigate the effect of hardware bias of different smartphone devices on positioning accuracy without requiring a calibration process. With the efforts of these two aspects, the proposed solution enhances the usability of Wi-Fi fingerprint positioning. The PSSD approach consists of two critical operations in constructing particular fingerprints. First, we construct the signal strength difference (SSD) radio map of the area of interest, which uses the RSS differences between APs to minimize the device-dependent effect. Then, the pairwise RSS fingerprints are constructed by leveraging the time-series RSS measurements and potential spatial topology of pedestrian locations of these measurement epochs, and consequently reducing possible large positioning errors. To verify the proposed PSSD method, we carry out extensive experiments with various Android smartphones in a campus building. In the case of heterogeneous devices, the experimental results demonstrate that PSSD fingerprinting achieves a mean error ∼20% less than conventional RSS fingerprinting. In addition, PSSD fingerprinting achieves a 90-percentile accuracy of no greater than 5.5 m across the tested heterogeneous smartphones

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Weight-RSS: A Calibration-Free and Robust Method for WLAN-Based Indoor Positioning

Cited by 21 publications

References 22 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

Location-Aware POI Recommendation for Indoor Space by Exploiting WiFi Logs

A Pairwise SSD Fingerprinting Method of Smartphone Indoor Localization for Enhanced Usability

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