Practical server-side WiFi-based indoor localization: Addressing cardinality &amp; outlier challenges for improved occupancy estimation

Ravi, Anuradha; Misra, Archan

doi:10.1016/j.adhoc.2021.102443

Cited by 10 publications

(3 citation statements)

References 17 publications

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“…Basically, four types of information can be extracted from the Syslog data: (1) when: the event occurrence time, (2) who: anonymized MAC address of the given device, (3) where: name of the AP(s) the device interacts with, and (4) what: event category which indicates the device joining/leaving the network or roaming from one AP to another AP. Besides the above-mentioned 4Ws, the RSSI values strongly correlate with the distance between devices and APs [29], which can help further determine the position of a given device with a higher accuracy [9]. Devices' operating systems (OS) are also monitored by Wi-Fi network in the Syslog, such as Windows, Android, iOS, or others, which can be used to distinguish the type of a mobile device.…”

Section: Wi-fi Syslog Processingmentioning

confidence: 99%

Geo-Visualization of Spatial Occupancy on Smart Campus Using Wi-Fi Connection Log Data

Zhao,

Wang,

Zhang

et al. 2023

IJGI

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As a typical and special type of urban setting, the university campus usually faces similar challenges as cities raised by high-density inhabitants. The smart campus has been introduced based on the smart city, as concepts, technologies, and solutions to improve livability and energy efficiency. Inhabitants’ occupancy in buildings and open spaces on campus is critical to optimize campus management and services. Information about spatial occupancy of campus inhabitants can be produced based on various location-based solutions, such as global navigation satellite systems (GNSS), campus cameras, Bluetooth, and Wi-Fi. As an essential component in campus information infrastructure, Wi-Fi network covers almost the entire university campus and has advantages in collecting locations of campus inhabitants. In this paper, geo-visualization of spatial occupancy of campus inhabitants is designed and implemented using anonymized Wi-Fi network log data. First, 3-dimension building models are reconstructed based on LiDAR point clouds and construction drawings. Then, the Wi-Fi network log data are cleaned and preprocessed. Campus inhabitants’ locations are extracted from structural Wi-Fi data. Geo-visualization at room, floor, and building levels is designed and implemented. On a temporal dimension, spatial occupancy can be visualized by each second, minute, hour, or day of the week in 3D buildings. The implementation of the geo-visualization is based on CesiumJS, which offers an interface for 3D-animated visualization and interaction. The research can be used to support university management and educators to implement the smart campus and optimize pedagogical research.

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Section: Wi-fi Syslog Processingmentioning

confidence: 99%

Geo-Visualization of Spatial Occupancy on Smart Campus Using Wi-Fi Connection Log Data

Zhao,

Wang,

Zhang

et al. 2023

IJGI

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“…The traditional indoor positioning methods such as angle of arrival (AoA), time of arrival (ToA), and time difference of arrival (TDoA) all require line-of-sight (LoS) measurements. The process of collecting fingerprint signals and correlating indoor locations without measuring LoS has become one of the prevalent research methods in the field of indoor positioning [ 18 ]. Fingerprint-based positioning methods are usually divided into two stages, the offline and the online [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Calibration-Free 3D Indoor Positioning Algorithms Based on DNN and DIFF

Yang

Deng

et al. 2022

Sensors

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The heterogeneity of wireless receiving devices, co-channel interference, and multi-path effect make the received signal strength indication (RSSI) of Wi-Fi fluctuate greatly, which seriously degrades the RSSI-based positioning accuracy. Signal strength difference (DIFF), a calibration-free solution for handling the received signal strength variance between diverse devices, can effectively reduce the negative impact of signal fluctuation. However, DIFF also leads to the explosion of the RSSI data dimension, expanding the number of dimensions from m to Cm2, which reduces the positioning efficiency. To this end, we design a data hierarchical processing strategy based on a building-floor-specific location, which effectively improves the efficiency of high-dimensional data processing. Moreover, based on a deep neural network (DNN), we design three different positioning algorithms for multi-building, multi-floor, and specific-location respectively, extending the indoor positioning from the single plane to three dimensions. Specifically, in the stage of data preprocessing, we first create the original RSSI database. Next, we create the optimized RSSI database by identifying and deleting the unavailable data in the RSSI database. Finally, we perform DIFF processing on the optimized RSSI database to create the DIFF database. In the stage of positioning, firstly, we design an improved multi-building positioning algorithm based on a denoising autoencoder (DAE). Secondly, we design an enhanced DNN for multi-floor positioning. Finally, the newly deep denoising autoencoder (DDAE) used for specific location positioning is proposed. The experimental results show that the proposed algorithms have better positioning efficiency and accuracy compared with the traditional machine learning algorithms and the current advanced deep learning algorithms.

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“…Approaches applying various sensor modalities are challenged by specific problems. On the one hand, Wireless-Sensor-Network (WSN)-based methods utilizing Ultra Wide Band (UWB) [ 2 ], WiFi [ 3 , 4 ], and Bluetooth Low Energy (BLE) [ 5 ]) can localize the robot with the Received Signal Strength Indicator (RSSI), which is unique at specific location. Such methods rely on Access Point (AP) deployment, and the accuracy is sensitive to the surrounding noise [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Vision-Sensor-Assisted Probabilistic Localization Method for Indoor Environment

Shi

Yang

Shi

et al. 2022

Sensors

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Among the numerous indoor localization methods, Light-Detection-and-Ranging (LiDAR)-based probabilistic algorithms have been extensively applied to indoor localization due to their real-time performance and high accuracy. Nevertheless, these methods are challenged in symmetrical environments when tackling global localization and the robot kidnapping problem. In this paper, a novel hybrid method that combines visual and probabilistic localization results is proposed. Augmented Monte Carlo Localization (AMCL) is improved for position tracking continually. LiDAR-based measurements’ uncertainty is evaluated to incorporate discrete visual-based results; therefore, a better diversity of the particle can be maintained. The robot kidnapping problem can be detected and solved by preventing premature convergence of the particle filter. Extensive experiments were implemented to validate the robustness and accuracy performance. Meanwhile, the localization error was reduced from 30 mm to 9 mm during a 600 m tour.

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Practical server-side WiFi-based indoor localization: Addressing cardinality & outlier challenges for improved occupancy estimation

Cited by 10 publications

References 17 publications

Geo-Visualization of Spatial Occupancy on Smart Campus Using Wi-Fi Connection Log Data

Geo-Visualization of Spatial Occupancy on Smart Campus Using Wi-Fi Connection Log Data

Calibration-Free 3D Indoor Positioning Algorithms Based on DNN and DIFF

Vision-Sensor-Assisted Probabilistic Localization Method for Indoor Environment

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