Indoor localization by WiFi

Bagosi, Timea; Baruch, Z.

doi:10.1109/iccp.2011.6047914

Cited by 25 publications

(13 citation statements)

References 2 publications

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“…The abundant sensors embedded in today’s mobile devices have greatly enhanced their ability to sense the indoor environment, thus providing possibilities for indoor positioning. Many indoor positioning methods rely on pre-installed infrastructures and can provide reasonable accuracy, such as UWB ranging anchor-based [1,2], Wi-Fi access points (APs)-based [3,4], Ultrasound [5,6]-based and so on. Among these methods, Bluetooth low-energy (BLE) beacons have great potential due to their advantages:

The positioning process is independent of extra hardware.

…”

Section: Introductionmentioning

confidence: 99%

Indoor Positioning Based on Bluetooth Low-Energy Beacons Adopting Graph Optimization

Zuo

Liu

Zhang

et al. 2018

Sensors

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Bluetooth Low-Energy (BLE) beacons-based indoor positioning is a promising method for indoor positioning, especially in applications of position-based services (PbS). It has low deployment cost and it is suitable for a wide range of mobile devices. Existing BLE beacon-based positioning methods can be categorized as range-based methods and fingerprinting-based methods. For range-based methods, the positions of the beacons should be known before positioning. For fingerprinting-based methods, a pre-requisite is the reference fingerprinting map (RFM). Many existing methods focus on how to perform the positioning assuming the beacon positions or RFM are known. However, in practical applications, determining the beacon positions or RFM in the indoor environment is normally a difficult task. This paper proposed an efficient and graph optimization-based way for estimating the beacon positions and the RFM, which combines the range-based method and the fingerprinting-based method. The method exists without need for any dedicated surveying instruments. A user equipped with a BLE-enabled mobile device walks in the region collecting inertial readings and BLE received signal strength indication (RSSI) readings. The inertial measurements are processed through the pedestrian dead reckoning (PDR) method to generate the constraints at adjacent poses. In addition, the BLE fingerprints are adopted to generate constraints between poses (with similar fingerprints) and the RSSIs are adopted to generate distance constraints between the poses and the beacon positions (according to a pre-defined path-loss model). The constraints are then adopted to form a cost function with a least square structure. By minimizing the cost function, the optimal user poses at different times and the beacon positions are estimated. In addition, the RFM can be generated through the pose estimations. Experiments are carried out, which validates that the proposed method for estimating the pre-requisites (including beacon positions and the RFM). These estimated pre-requisites are of sufficient quality for both range-based and fingerprinting-based positioning.

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The positioning process is independent of extra hardware.

…”

Section: Introductionmentioning

confidence: 99%

Indoor Positioning Based on Bluetooth Low-Energy Beacons Adopting Graph Optimization

Zuo

Liu

Zhang

et al. 2018

Sensors

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“…Step 3 Flip_1Degree. Let, SUM_MDS_ Flip_Distance_1Degree be used to represent the sum of these distances.…”

Section: Step 3: Translating Mds Locationsmentioning

confidence: 99%

“…For personal navigation systems, the low-precision localization result would likely guide users to an incorrect destination. Technically, the large error of WiFi-based localization results from the fact that different locations with similar WiFi signal strength do exist in typical indoor environments [2] [3].…”

Section: Introductionmentioning

confidence: 99%

HILL: A Hybrid Indoor Localization Scheme

Kharidia

Sampalli

et al. 2014

2014 10th International Conference on Mobile Ad-Hoc and Sensor Networks

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Localization is a fundamental operation in wireless networks. Location determination is normally accomplished using the Global Positioning System (GPS) for outdoor applications. For indoor localization, GPS does not work due to the lack of the line of sight to satellites. High-precision indoor localization is critical to many personal and business applications. WiFi-based indoor localization was proposed to be a practical method to locate WiFi-enabled devices due to the popularity of WiFi networks. However, it suffers from large localization errors. Our experimental results indicate that this scheme consistently leads to an average error around 3 meters. The existence of different locations with similar WiFi signal strength is the reason behind the large errors. To improve the localization precision, a hybrid indoor localization scheme, HILL, is proposed in this paper. Inspired by the fact that a large number of WiFi-enabled mobile devices have been deployed, HILL uses 3 phases to improve the precision ofWiFi-based localization. First of all, it measures the distances between each pair of peer devices through acoustic ranging. Secondly, the Classical Metric Multidimensional Scaling (MDS) method is applied to the collected distances, which results in a graph consistent with the distances. Finally, the graph generated by MDS is embedded onto the graph corresponding to WiFi-based localization in order to achieve high localization precision. Our experimental results indicate that the average localization error of HILL is about 1 meter.

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“…Many schemes have been proposed to classify WiFi APs by using correlation coefficients of listened RSSI values, such as centroid [24], WDF [25], KNN [28,29], R-KNN [17], WKNN [18]. Among which, WKNN is most reasonable in certain cases by focusing on estimating reference locations and their weights.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, to overcome these limitations, a novel scheme called Fingerprint Signature Reorganizing (FSR) is introduced, with Overlap-based Weighted Nearest Neighbors (OWKNN) algorithm, which is based from WKNN [17,18]. By using the same interval and density of sampling sites as original scheme, both accuracy and standard deviation of localization can be improved significantly.…”

Section: Introductionmentioning

confidence: 99%

Improving the Indoor Localization Accuracy for CPS by Reorganizing the Fingerprint Signatures

Qiu

Wang

Dai

2014

International Journal of Distributed Sensor Networks

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This paper firstly represents a survey of recent researches on localization of CPS and analyzes the advantages and limitations of the existing schemes. To overcome the limitations, a novel localization scheme is then proposed, which includes site survey, received signal comparison, and location correction phases. The main improvement of our proposed scheme is to filter the noises and interference added to the received RSSI signal; the detailed procedure is then provided with mathematical analysis. Finally, the experiment and simulation results are presented with analysis.

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Indoor localization by WiFi

Cited by 25 publications

References 2 publications

Indoor Positioning Based on Bluetooth Low-Energy Beacons Adopting Graph Optimization

Indoor Positioning Based on Bluetooth Low-Energy Beacons Adopting Graph Optimization

HILL: A Hybrid Indoor Localization Scheme

Improving the Indoor Localization Accuracy for CPS by Reorganizing the Fingerprint Signatures

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