A Radio-Map Automatic Construction Algorithm Based on Crowdsourcing

Yu, Ning; Xiao, Chenxian; Yue, Wu; Ran, Feng

doi:10.3390/s16040504

Cited by 20 publications

(10 citation statements)

References 34 publications

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“…Both methods, aim to reduce the number of reference points (RP) and RSS measurements. Also, [15][16][17] explore crowdsourcing-based methods to reduce the deployment workload by engaging the users to participate in radio map construction.…”

Section: Of 22mentioning

confidence: 99%

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Graph-based Semi-supervised Learning for Indoor Localization Using Crowdsourced Data

Zhang¹,

Valaee²,

Xu³

et al. 2017

Preprint

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Indoor positioning based on the received signal strength (RSS) of the WiFi signal has become the most popular solution for indoor localization. In order to realize the rapid deployment of indoor localization systems, solutions based on crowdsourcing have been proposed. However, compared to conventional methods, crowdsourced RSS values are more erroneous and can result in large localization errors. To mitigate the negative effect of the erroneous measurements, a graph-based semi-supervised learning (G-SSL) method is used to exploit the correlation between the RSS values at nearby locations to estimate an optimal RSS value at each location. Before using the G-SSL method, the Linear Regression (LR) algorithm is proposed to solve the device diversity problem in crowdsourcing system. Since the spatial distribution of the APs is sparse, the Compressed Sensing (CS) method is applied to precisely estimate the location of the APs. Based on the location of the APs and a simple signal propagation model, the RSS difference between different locations is calculated and used as an additional constraint to improve the performance of G-SSL. Furthermore, to exploit the sparsity of the weights used in the G-SSL, we use the CS method to reconstruct these weights more accurately and make a further improvement on the performance of the G-SSL. Experimental results show improved results in terms of the smoothness of the radio map and the localization accuracy.

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Section: Of 22mentioning

confidence: 99%

“…Several works have been proposed to reduce the workload of the offline phase [12][13][14]. The crowdsourcing method has been shown to be a promising approach to solving this problem [15][16][17]. In a crowdsourcing-based system, each user can contribute to the construction and updating of the radio map.…”

Section: Introductionmentioning

confidence: 99%

Graph-based Semi-supervised Learning for Indoor Localization Using Crowdsourced Data

Zhang¹,

Valaee²,

Xu³

et al. 2017

Preprint

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show abstract

“…By using the built-in sensors of smartphone, the collected data can be employed to estimate the trajectory of people and generate WiFi fingerprints for radio map construction. Many researchers [ 12 , 13 , 14 ] have proposed methods that connect independent fingerprints to radio maps by leverage user motions. Most of these methods use a pedestrian dead reckoning (PDR) technique for restoring the indoor trajectories of pedestrians.…”

Section: Introductionmentioning

confidence: 99%

A Visual-Based Approach for Indoor Radio Map Construction Using Smartphones

Liu

Fang

2017

Sensors

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Localization of users in indoor spaces is a common issue in many applications. Among various technologies, a Wi-Fi fingerprinting based localization solution has attracted much attention, since it can be easily deployed using the existing off-the-shelf mobile devices and wireless networks. However, the collection of the Wi-Fi radio map is quite labor-intensive, which limits its potential for large-scale application. In this paper, a visual-based approach is proposed for the construction of a radio map in anonymous indoor environments. This approach collects multi-sensor data, e.g., Wi-Fi signals, video frames, inertial readings, when people are walking in indoor environments with smartphones in their hands. Then, it spatially recovers the trajectories of people by using both visual and inertial information. Finally, it estimates the location of fingerprints from the trajectories and constructs a Wi-Fi radio map. Experiment results show that the average location error of the fingerprints is about 0.53 m. A weighted k-nearest neighbor method is also used to evaluate the constructed radio map. The average localization error is about 3.2 m, indicating that the quality of the constructed radio map is at the same level as those constructed by site surveying. However, this approach can greatly reduce the human labor cost, which increases the potential for applying it to large indoor environments.

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“…Both methods, aim to reduce the number of reference points (RP) and RSS measurements. Also, [14][15][16] explore crowdsourcing-based methods to reduce the deployment workload by engaging the users to participate in radio map construction.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Several works have been proposed to reduce the workload of the offline phase [11][12][13]. The crowdsourcing method has been shown to be a promising approach to solving this problem [14][15][16]. In a crowdsourcing-based system, each user can contribute to the construction and updating of the radio map.…”

Section: Introductionmentioning

confidence: 99%

Graph-Based Semi-Supervised Learning for Indoor Localization Using Crowdsourced Data

Zhang

Valaee

et al. 2017

Applied Sciences

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Abstract:Indoor positioning based on the received signal strength (RSS) of the WiFi signal has become the most popular solution for indoor localization. In order to realize the rapid deployment of indoor localization systems, solutions based on crowdsourcing have been proposed. However, compared to conventional methods, lots of different devices are used in crowdsourcing system and less RSS values are collected by each device. Therefore, the crowdsourced RSS values are more erroneous and can result in significant localization errors. In order to eliminate the signal strength variations across diverse devices, the Linear Regression (LR) algorithm is proposed to solve the device diversity problem in crowdsourcing system. After obtaining the uniform RSS values, a graph-based semi-supervised learning (G-SSL) method is used to exploit the correlation between the RSS values at nearby locations to estimate an optimal RSS value at each location. As a result, the negative effect of the erroneous measurements could be mitigated. Since the AP locations need to be known in G-SSL algorithm, the Compressed Sensing (CS) method is applied to precisely estimate the location of the APs. Based on the location of the APs and a simple signal propagation model, the RSS difference between different locations is calculated and used as an additional constraint to improve the performance of G-SSL. Furthermore, to exploit the sparsity of the weights used in the G-SSL, we use the CS method to reconstruct these weights more accurately and make a further improvement on the performance of the G-SSL. Experimental results show improved results in terms of the smoothness of the radio map and the localization accuracy.

show abstract

A Radio-Map Automatic Construction Algorithm Based on Crowdsourcing

Cited by 20 publications

References 34 publications

Graph-based Semi-supervised Learning for Indoor Localization Using Crowdsourced Data

Graph-based Semi-supervised Learning for Indoor Localization Using Crowdsourced Data

A Visual-Based Approach for Indoor Radio Map Construction Using Smartphones

Graph-Based Semi-Supervised Learning for Indoor Localization Using Crowdsourced Data

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