A scalable indoor localization algorithm based on distance fitting and fingerprint mapping in Wi-Fi environments

Zhang, Hao; Li, Kai; Jin, Feiyu; Feng, Liang; Lee, Victor; Ng, Joseph Kee‐Yin

doi:10.1007/s00521-018-3961-8

Cited by 26 publications

(4 citation statements)

References 18 publications

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“…Therefore, choosing the Gaussian kernel function in the KGLPP algorithm can improve the accuracy and stability of the model and better handle complex datasets. The Gaussian kernel function can be expressed mathematically as shown in Equation (33).…”

Section: Kglpp Transform Of Original Position Fingerprintmentioning

confidence: 99%

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Drone-Assisted Fingerprint Localization Based on Kernel Global Locally Preserving Projection

Pan

Tan

et al. 2023

Drones

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To improve the limited number of fixed access points (APs) and the inability to dynamically adjust them in fingerprint localization, this paper attempted to use drones to replace these APs. Drones have higher flexibility and accuracy, can hover in different locations, and can adapt to different scenarios and user needs, thereby improving localization accuracy. When performing fingerprint localization, it is often necessary to consider various factors such as environmental complexity, large-scale raw data collection, and signal strength variation. These factors can lead to high-dimensional and complex nonlinear relationships in location fingerprints, thereby greatly affecting localization accuracy. In order to overcome these problems, this paper proposes a kernel global locally preserving projection (KGLPP) algorithm. The algorithm can reduce the dimensionality of location fingerprint data while preserving its most-important structural information, and it combines global and local information to avoid the problem of reduced information and poor dimensionality reduction effects, which may arise from considering only one. In the process of location estimation, an improved weighted k-nearest neighbor (IWKNN) algorithm is adopted to more accurately estimate the target’s position. Unlike the traditional KNN or WKNN algorithms, the IWKNN algorithm can choose the optimal number of nearest neighbors autonomously, perform location estimation and weight calculation based on the actual situation, and thus, obtain more-accurate location estimation results. The experimental results showed that the algorithm outperformed other algorithms in terms of both the average error and localization accuracy.

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Section: Kglpp Transform Of Original Position Fingerprintmentioning

confidence: 99%

“…(1) KNN [33]: During online localization, the Euclidean distance is used to find the RPs closest to the target, and the average position of these RPs is used to estimate the position of the target. (2) WKNN [34]: WKNN differs from KNN in that it assigns different weights to different RPs when estimating the target location.…”

Section: Simulation and Experimentsmentioning

confidence: 99%

Drone-Assisted Fingerprint Localization Based on Kernel Global Locally Preserving Projection

Pan

Tan

et al. 2023

Drones

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“…The work reported in [ 22 ] described the development of fingerprinting mapping using an RSSI online K-nearest-neighbor algorithm for indoor WiFi services. In [ 23 ], the authors used a feature-adaptive online sequential extreme learning machine (another machine-learning technique) for lifelong indoor WiFi localization that could improve its accuracy even with fewer data.…”

Section: Related Workmentioning

confidence: 99%

Using Smart Virtual-Sensor Nodes to Improve the Robustness of Indoor Localization Systems

Pedrollo

Konzen

Morais

et al. 2021

Sensors

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Young, older, frail, and disabled individuals can require some form of monitoring or assistance, mainly when critical situations occur, such as falling and wandering. Healthcare facilities are increasingly interested in e-health systems that can detect and respond to emergencies on time. Indoor localization is an essential function in such e-health systems, and it typically relies on wireless sensor networks (WSN) composed of fixed and mobile nodes. Nodes in the network can become permanently or momentarily unavailable due to, for example, power failures, being out of range, and wrong placement. Consequently, unavailable sensors not providing data can compromise the system’s overall function. One approach to overcome the problem is to employ virtual sensors as replacements for unavailable sensors and generate synthetic but still realistic data. This paper investigated the viability of modelling and artificially reproducing the path of a monitored target tracked by a WSN with unavailable sensors. Particularly, the case with just a single sensor was explored. Based on the coordinates of the last measured positions by the unavailable node, a neural network was trained with 4 min of not very linear data to reproduce the behavior of a sensor that become unavailable for about 2 min. Such an approach provided reasonably successful results, especially for areas close to the room’s entrances and exits, which are critical for the security monitoring of patients in healthcare facilities.

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“…A K-nearest neighbors (KNN) is a mathematical data classification algorithm for determining the most similar points in a reference set to a test point with certain characteristics (Kuhn & Johnson 2016). (Zhang, et al, 2020) proposed a scalable indoor positioning technique based on distance fitting and fingerprinting approach. The experiments were conducted in a university building using eight Wi-Fi signal scanners.…”

Section: Rssi-based Positioningmentioning

confidence: 99%

Application of Wireless Signal Scanning in Traffic Studies

Mohammadi¹

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With advances in communication technologies, innovative traffic data collection approaches have been developed. A means for this purpose is wireless technology which is sufficiently widespread among road users. Traffic data may be collected using signal scanners detecting wireless signals in the vicinity of them. As the data provided by signal scanners involve no direct information about the position of the signal source, the applications of this technology in traffic studies have been limited to finding some parameters in certain situations. The purpose is developing the applications of wireless signal scanning in traffic studies which require positional data of road users. This is achieved utilizing the potentials of received signal strength indicator (RSSI) of wireless signals transmitted by personal smart devices. Wi-Fi, Bluetooth Classic and Bluetooth Low Energy are three widespread signal modes, transmitted by popular beacons used in daily life. A comparative study of the field performance of these signal modes is conducted, investigating their characteristics important in gathering traffic flow parameters whenever positional data of road users are required. This provides the possibility of selecting the most suitable signal mode for the intended applications of the technology based on the requirements of the methods. A technique for positioning of beacons based on their transmitted signals, applicable in transportation studies is developed. This technique provides the possibility of positioning in intersections and their surrounding areas as well as congested road segments. The technique is based on the strength of signals transmitted by beacons, creating radio maps, and applying an algorithm called k-nearest neighbors. The procedure is optimized, iii and the accuracy and functionality of the technique is improved via modification of the system arrangement and application of proper filtering algorithms. A method for detection and classification of turning movements applicable in small urban intersections is developed based on wireless signals. The method utilizes the time profiles of the RSSI values of the signals emitted by beacons carried by turning vehicles. The signals are collected by an array of signal scanners carefully located on the intersection approaches. Turning movements are classified comparing signature points of the RSSItime profiles and their occurrence moments.

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A scalable indoor localization algorithm based on distance fitting and fingerprint mapping in Wi-Fi environments

Cited by 26 publications

References 18 publications

Drone-Assisted Fingerprint Localization Based on Kernel Global Locally Preserving Projection

Drone-Assisted Fingerprint Localization Based on Kernel Global Locally Preserving Projection

Using Smart Virtual-Sensor Nodes to Improve the Robustness of Indoor Localization Systems

Application of Wireless Signal Scanning in Traffic Studies

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