WiFi RSS fingerprinting indoor localization for mobile devices

Zegeye, Wondimu K.; Amsalu, Seifemichael B.; Astatke, Yacob; Moazzami, Farzad

doi:10.1109/uemcon.2016.7777834

Cited by 48 publications

(25 citation statements)

References 6 publications

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“…Further investigations about RFID localization are found in the literature, such as a multi-tag cooperative localization algorithm based We have witnessed significant technical progress and plenty of applications in recent years. Zegeye et al [23] implement WiFi received signal strength indicator (RSSI) fingerprinting and location estimation algorithms running on a server and a secure digital memory (SD) card of a mobile device. In order to manage the time-varying RSSI, Shu et al [24] proposed gradient fingerprinting (GIFT) to leverage a more stable RSSI gradient, which first builds a gradient-based fingerprint map (Gmap) by comparing the absolute RSSI values at nearby positions, and then runs an online extended particle filter (EPF) to localize the user/device.…”

Section: (2) Rfid-based Indoor Localizationmentioning

confidence: 99%

Cost-Effective Wearable Indoor Localization and Motion Analysis via the Integration of UWB and IMU

Zhang

Gao

et al. 2020

Sensors

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Wearable indoor localization can now find applications in a wide spectrum of fields, including the care of children and the elderly, sports motion analysis, rehabilitation medicine, robotics navigation, etc. Conventional inertial measurement unit (IMU)-based position estimation and radio signal indoor localization methods based on WiFi, Bluetooth, ultra-wide band (UWB), and radio frequency identification (RFID) all have their limitations regarding cost, accuracy, or usability, and a combination of the techniques has been considered a promising way to improve the accuracy. This investigation aims to provide a cost-effective wearable sensing solution with data fusion algorithms for indoor localization and real-time motion analysis. The main contributions of this investigation are: (1) the design of a wireless, battery-powered, and light-weight wearable sensing device integrating a low-cost UWB module-DWM1000 and micro-electromechanical system (MEMS) IMU-MPU9250 for synchronized measurement; (2) the implementation of a Mahony complementary filter for noise cancellation and attitude calculation, and quaternions for frame rotation to obtain the continuous attitude for displacement estimation; (3) the development of a data fusion model integrating the IMU and UWB data to enhance the measurement accuracy using Kalman-filter-based time-domain iterative compensations; and (4) evaluation of the developed sensor module by comparing it with UWB- and IMU-only solutions. The test results demonstrate that the average error of the integrated module reached 7.58 cm for an arbitrary walking path, which outperformed the IMU- and UWB-only localization solutions. The module could recognize lateral roll rotations during normal walking, which could be potentially used for abnormal gait recognition.

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Section: (2) Rfid-based Indoor Localizationmentioning

confidence: 99%

Cost-Effective Wearable Indoor Localization and Motion Analysis via the Integration of UWB and IMU

Zhang

Gao

et al. 2020

Sensors

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“…The basic idea of creating fingerprint maps for Wi-Fi positioning is explained in [40,41]. In this localization approach, we use a grid-based representation of the indoor environment.…”

Section: Wi-fi Fingerprint Algorithmmentioning

confidence: 99%

A Sensor Fusion Framework for Indoor Localization Using Smartphone Sensors and Wi-Fi RSSI Measurements

2019

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Sensor fusion frameworks for indoor localization are developed with the specific goal of reducing positioning errors. Although many conventional localization frameworks without fusion have been improved to reduce positioning error, sensor fusion frameworks generally provide a further improvement in positioning accuracy. In this paper, we propose a sensor fusion framework for indoor localization using the smartphone inertial measurement unit (IMU) sensor data and Wi-Fi received signal strength indication (RSSI) measurements. The proposed sensor fusion framework uses location fingerprinting and trilateration for Wi-Fi positioning. Additionally, a pedestrian dead reckoning (PDR) algorithm is used for position estimation in indoor scenarios. The proposed framework achieves a maximum of 1.17 m localization error for the rectangular motion of a pedestrian and a maximum of 0.44 m localization error for linear motion.

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“…Different techniques are developed, such as Triangulation, Trilateration, Proximity, and Fingerprinting, using Angle of Arrival (AoA), Time difference of Arrival (TDoA), Time of Arrival (ToA), and Receive Signal Strength Identification (RSSI) [1]. All these techniques except fingerprinting require LoS, which is not possible in an indoor environment which makes fingerprinting the most reasonable technique for indoor localization [4]. On the contrary, fingerprinting is laborious and time-consuming and the radio map is venerable to environmental changes, leading to high position estimation error.…”

Section: Introductionmentioning

confidence: 99%

Cuckoo Search-based SVM (CS-SVM) Model for Real-Time Indoor Position Estimation in IoT Networks

Khan

Bangash

et al. 2021

Security and Communication Networks

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Internet of Things (IoT), an emerging technology, is becoming an essential part of today’s world. Machine learning (ML) algorithms play an important role in various applications of IoT. For decades, the location information has been extremely useful for humans to navigate both in outdoor and indoor environments. Wi-Fi access point-based indoor positioning systems get more popularity, as it avoids extra calibration expenses. The fingerprinting technique is preferred in an indoor environment as it does not require a signal’s Line of Sight (LoS). It consists of two phases: offline and online phase. In the offline phase, the Wi-Fi RSSI radio map of the site is stored in a database, and in the online phase, the object is localized using the offline database. To avoid the radio map construction which is expensive in terms of labor, time, and cost, machine learning techniques may be used. In this research work, we proposed a hybrid technique using Cuckoo Search-based Support Vector Machine (CS-SVM) for real-time position estimation. Cuckoo search is a nature-inspired optimization algorithm, which solves the problem of slow convergence rate and local minima of other similar algorithms. Wi-Fi RSSI fingerprint dataset of UCI repository having seven classes is used for simulation purposes. The dataset is preprocessed by min-max normalization to increase accuracy and reduce computational speed. The proposed model is simulated using MATLAB and evaluated in terms of accuracy, precision, and recall with K-nearest neighbor (KNN) and support vector machine (SVM). Moreover, the simulation results show that the proposed model achieves high accuracy of 99.87%.

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WiFi RSS fingerprinting indoor localization for mobile devices

Cited by 48 publications

References 6 publications

Cost-Effective Wearable Indoor Localization and Motion Analysis via the Integration of UWB and IMU

Cost-Effective Wearable Indoor Localization and Motion Analysis via the Integration of UWB and IMU

A Sensor Fusion Framework for Indoor Localization Using Smartphone Sensors and Wi-Fi RSSI Measurements

Cuckoo Search-based SVM (CS-SVM) Model for Real-Time Indoor Position Estimation in IoT Networks

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