LOCALI: Calibration-Free Systematic Localization Approach for Indoor Positioning

Ali, Muhammad Usman; Hur, Soojung; Park, Yongwan

doi:10.3390/s17061213

Cited by 32 publications

(38 citation statements)

References 35 publications

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“…The research focusing on indoor and outdoor localization is increasing exponentially especially those that utilize smartphone sensors [ 25 , 26 , 27 , 28 , 29 ]. Such localization systems mostly work with the assumption that the system clearly has the knowledge of the indoor and outdoor environment, which is hardly true in practice.…”

Section: Related Workmentioning

confidence: 99%

MagIO: Magnetic Field Strength Based Indoor- Outdoor Detection with a Commercial Smartphone

2018

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A wide range of localization techniques has been proposed recently that leverage smartphone sensors. Context awareness serves as the backbone of these localization techniques, which helps them to shift the localization technologies to improve efficiency and energy utilization. Indoor-outdoor (IO) context sensing plays a vital role for such systems, which serve both indoor and outdoor localization. IO systems work with collaborative technologies including the Global Positioning System (GPS), cellular tower signals, Wi-Fi, Bluetooth and a variety of smartphone sensors. GPS- and Wi-Fi-based systems are power hungry, and their accuracy is severed by limiting factors like multipath, shadowing, etc. On the other hand, various built-in smartphone sensors can be deployed for environmental sensing. Although these sensors can play a crucial role, yet they are very less studied. This research aims at investigating the use of ambient magnetic field data alone from a smartphone for IO detection. The research first investigates the feasibility of utilizing magnetic field data alone for IO detection and then extracts different features suitable for IO detection to be used in machine learning-based classifiers to discriminate between indoor and outdoor environments. The experiments are performed at three different places including a subway station, a shopping mall and Yeungnam University (YU), Korea. The training data are collected from one spot of the campus, and testing is performed with data from various locations of the above-mentioned places. The experiment involves Samsung Galaxy S8, LG G6 and Samsung Galaxy Round smartphones. The results show that the magnetic data from smartphone magnetic sensor embody enough information and can discriminate the indoor environment from the outdoor environment. Naive Bayes (NB) outperforms with a classification accuracy of 83.26%, as against Support vector machines (SVM), random induction (RI), gradient boosting machines (GBM), random forest (RF), k-nearest neighbor (kNN) and decision trees (DT), whose accuracies are 67.21%, 73.38%, 73.40%, 78.59%, 69.53% and 68.60%, respectively. kNN, SVM and DT do not perform well when noisy data are used for classification. Additionally, other dynamic scenarios affect the attitude of magnetic data and degrade the performance of SVM, RI and GBM. NB and RF prove to be more noise tolerant and environment adaptable and perform very well in dynamic scenarios. Keeping in view the performance of these classifiers, an ensemble-based stacking scheme is presented, which utilizes DT and RI as the base learners and naive Bayes as the ensemble classifier. This approach is able to achieve an accuracy of 85.30% using the magnetic data of the smartphone magnetic sensor. Moreover, with an increase in training data, the accuracy of the stacking scheme can be elevated by 0.83%. The performance of the proposed approach is compared with GPS-, Wi-Fi- and light sensor-based IO detection.

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Section: Related Workmentioning

confidence: 99%

MagIO: Magnetic Field Strength Based Indoor- Outdoor Detection with a Commercial Smartphone

2018

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“…Ali et al [ 29 ] proposed a systematic localization approach, “LOCALI”, for indoor positioning. LOCALI generates RSS maps based on the environment plan, so it does not require a calibration database and extensive updates.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Indoor Positioning Model Based on WLAN-Fingerprinting for Dynamic and Multi-Floor Environments

Alshami

Ahmad

Sahibuddin

et al. 2017

Sensors

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The Global Positioning System demonstrates the significance of Location Based Services but it cannot be used indoors due to the lack of line of sight between satellites and receivers. Indoor Positioning Systems are needed to provide indoor Location Based Services. Wireless LAN fingerprints are one of the best choices for Indoor Positioning Systems because of their low cost, and high accuracy, however they have many drawbacks: creating radio maps is time consuming, the radio maps will become outdated with any environmental change, different mobile devices read the received signal strength (RSS) differently, and peoples’ presence in LOS between access points and mobile device affects the RSS. This research proposes a new Adaptive Indoor Positioning System model (called DIPS) based on: a dynamic radio map generator, RSS certainty technique and peoples’ presence effect integration for dynamic and multi-floor environments. Dynamic in our context refers to the effects of people and device heterogeneity. DIPS can achieve 98% and 92% positioning accuracy for floor and room positioning, and it achieves 1.2 m for point positioning error. RSS certainty enhanced the positioning accuracy for floor and room for different mobile devices by 11% and 9%. Then by considering the peoples’ presence effect, the error is reduced by 0.2 m. In comparison with other works, DIPS achieves better positioning without extra devices.

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“…Travi-Navi [65] captured images and collected Wi-Fi samples and IMU-sensed data to pack them into navigation traces, as these traces can be shared with the user directly or through a cloud server. To achieve this aim, some lightweight trace-merging algorithms were employed in the study to discover the overlapping segments and merge multiple traces.…”

Section: Wlan Fingerprinting Positioning Supported By Image Processingmentioning

confidence: 99%

A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing

Firdaus

Ahmad

Sahibuddin

2020

Int. j. electr. comput. eng. syst. (Online)

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Location-based services (LBS) are a significant permissive technology. One of the main components in indoor LBS is the indoor positioning system (IPS). IPS utilizes many existing technologies such as radio frequency, images, acoustic signals, as well as magnetic sensors, thermal sensors, optical sensors, and other sensors that are usually installed in a mobile device. The radio frequency technologies used in IPS are WLAN, Bluetooth, Zig Bee, RFID, frequency modulation, and ultra-wideband. This paper explores studies that have combined WLAN fingerprinting and image processing to build an IPS. The studies on combined WLAN fingerprinting and image processing techniques are divided based on the methods used. The first part explains the studies that have used WLAN fingerprinting to support image positioning. The second part examines works that have used image processing to support WLAN fingerprinting positioning. Then, image processing and WLAN fingerprinting are used in combination to build IPS in the third part. A new concept is proposed at the end for the future development of indoor positioning models based on WLAN fingerprinting and supported by image processing to solve the effect of people presence around users and the user orientation problem.

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LOCALI: Calibration-Free Systematic Localization Approach for Indoor Positioning

Cited by 32 publications

References 35 publications

MagIO: Magnetic Field Strength Based Indoor- Outdoor Detection with a Commercial Smartphone

MagIO: Magnetic Field Strength Based Indoor- Outdoor Detection with a Commercial Smartphone

Adaptive Indoor Positioning Model Based on WLAN-Fingerprinting for Dynamic and Multi-Floor Environments

A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing

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