Achieving Cost-Efficient Indoor Fingerprint Localization on WLAN Platform: A Hypothetical Test Approach

Zhou, Mu; Wei, Yacong; Tian, Zengshan; Yang, Xiaolong; Li, Lingxia

doi:10.1109/access.2017.2737651

Cited by 28 publications

(13 citation statements)

References 26 publications

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“…Indoor magnetic fields are steadily warped by the steel structures of buildings. 16,26 Therefore, indoor magnetic fields are static, as Figure 12 shows, and the azimuth deviations at the same positions are similar in different months. If we compensate azimuth measurements with the offsets at relative positions, the compensated azimuths become much closer to ground truth, as the magenta line in Figure 12 shows.…”

Section: The Direction Compensation Of Particle Movementsmentioning

confidence: 90%

“…13,14 Systems based on Wi-Fi and BLE signals do not achieve high accuracies due to the fluctuation of these signals. [15][16][17][18][19][20][21] The performances of acoustic-based 4,5 and CSI methods are quite well; however, the two methods need dense sampling, thus making it difficult to be deployed in large environments. The UWB needs to deploy extra infrastructures.…”

Section: Introductionmentioning

confidence: 99%

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Toward improving indoor magnetic field–based positioning system using pedestrian motion models

Shao

Luo

Zhao

et al. 2018

International Journal of Distributed Sensor Networks

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Indoor magnetic field has attracted considerable attention in indoor location-based services, because of its pervasive and stable attributes. Generally, in order to harness the location features of the magnetic field, particle filters are introduced to simulate the possibilities of user locations. Real-time magnetic field fingerprints are matched with model fingerprints to adjust the location possibilities. However, the computation overheads of the magnetic matching are rather high, thus limiting their applications to mobile computing platforms and indoor location-based service providers that serve massive users. In order to reduce the computation overhead, the article presents a low-cost magnetic field fingerprint matching scheme. Based on the low-frequency features of the magnetic field, the scheme updates particle weights according to the mass center of the magnetic field deltas of pedestrian steps. The proposed low-cost scheme decreases the complexity of real-time fingerprints without harming the positioning performance. In order to further improve the positioning accuracy, not asking users to hold the smartphone in fixed attitudes, we also present a smartphone attitude detection method that enables the proposed scheme to automatically select proper fingerprints. Experiments convincingly reveal that the proposed scheme achieves about 1 m accuracy at 80% with low computation overheads.

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Section: The Direction Compensation Of Particle Movementsmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Toward improving indoor magnetic field–based positioning system using pedestrian motion models

Shao

Luo

Zhao

et al. 2018

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…Whenever the source requests services, the position can be quickly estimated by matching the sensing data with the previously constructed fingerprint database and finding the best item during the online phase. Due to the learning mechanism used, the fingerprinting method is less sensitive to complex conditions and can provide relatively accurate localization performance [17]- [19].…”

Section: Introductionmentioning

confidence: 99%

Efficient Kernel Based 3-D Source Localization via Tensor Completion

2019

KSII TIIS

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Source localization in three-dimensional (3-D) wireless sensor networks (WSNs) is becoming a major research focus. Due to the complicated air-ground environments in 3-D positioning, many of the traditional localization methods, such as received signal strength (RSS) may have relatively poor accuracy performance. Benefit from prior learning mechanisms, fingerprinting-based localization methods are less sensitive to complex conditions and can provide relatively accurate localization performance. However, fingerprinting-based methods require training data at each grid point for constructing the fingerprint database, the overhead of which is very high, particularly for 3-D localization. Also, some of measured data may be unavailable due to the interference of a complicated environment. In this paper, we propose an efficient kernel based 3-D localization algorithm via tensor completion. We first exploit the spatial correlation of the RSS data and demonstrate the low rank property of the RSS data matrix. Based on this, a new training scheme is proposed that uses tensor completion to recover the missing data of the fingerprint database. Finally, we propose a kernel based learning technique in the matching phase to improve the sensitivity and accuracy in the final source position estimation. Simulation results show that our new method can effectively eliminate the impairment caused by incomplete sensing data to improve the localization performance.

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“…For uplink direction, let , be the data transmission rate between the CU and the BS and , be the data transmission rate between D-Tx and D-Rx. Since the transmission rate of each cellular link and D2D link can be determined by the Shannon capacity [27].…”

Section: System Modelmentioning

confidence: 99%

Game‐Theoretic Social‐Aware Resource Allocation for Device‐to‐Device Communications Underlaying Cellular Network

Wang

Zhang

et al. 2018

Wireless Communications and Mobile Computing

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Device-to-Device communication underlaying cellular network can increase the spectrum efficiency due to direct proximity communication and frequency reuse. However, such performance improvement is influenced by the power interference caused by spectrum sharing and social characteristics in each social community jointly. In this investigation, we present a dynamic game theory with complete information based D2D resource allocation scheme for D2D communication underlaying cellular network. In this resource allocation method, we quantify both the rate influence from the power interference caused by the D2D transmitter to cellular users and rate enhancement brought by the social relationships between mobile users. Then, the utility function maximization game is formulated to optimize the overall transmission rate performance of the network, which synthetically measures the final influence from both power interference and sociality enhancement. Simultaneously, we discuss the Nash Equilibrium of the proposed utility function maximization game from a theoretical point of view and further put forward a utility priority searching algorithm based resource allocation scheme. Simulation results show that our proposed scheme attains better performance compared with the other two advanced proposals.

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Achieving Cost-Efficient Indoor Fingerprint Localization on WLAN Platform: A Hypothetical Test Approach

Cited by 28 publications

References 26 publications

Toward improving indoor magnetic field–based positioning system using pedestrian motion models

Toward improving indoor magnetic field–based positioning system using pedestrian motion models

Efficient Kernel Based 3-D Source Localization via Tensor Completion

Game‐Theoretic Social‐Aware Resource Allocation for Device‐to‐Device Communications Underlaying Cellular Network

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