Smartphone-Based User Positioning in a Multiple-User Context with Wi-Fi and Bluetooth

Ta, Viet-Cuong; Dao, Thiên-My; Vaufreydaz, Dominique; Castelli, Eric

doi:10.1109/ipin.2018.8533809

Cited by 12 publications

(9 citation statements)

References 16 publications

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“…The study selection process, with step-wise results, is schematically depicted using the PRISMA flow diagram in Figure 2 . As a final set of eligible studies, 84 articles [ 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 63 , 77 , 79 , 80 , 81 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 ,…”

Section: Methodsmentioning

confidence: 99%

“…Algorithm D I-L E PA+E [ 131 ] IMU Bluetooth, Acoustic DR RSSI PDR-B. M. B. Propagation D I-L E PA+CC+R [ 130 ] Wi-Fi Wi-Fi RSSI RSSI Ranging B. Propagation N/S I-L S PA+CC [ 44 ] Wi-Fi Bluetooth F. printing RSSI K-mean clustering+R. Forest P. Filter C W/I E PA [ 128 ] IMU UWB DR TWR PDR-B.…”

Section: Appendix A1 Search Queriesmentioning

confidence: 99%

“…The study selection process, with step-wise results, is schematically depicted using the PRISMA flow diagram in Figure 2. As a final set of eligible studies, 84 articles [40][41][42][43][44][45][46][47][48][49][50]63,77,[79][80][81] were included in our review for full analysis.…”

Section: Study Selection Processmentioning

confidence: 99%

“…They expand the coverage area of stand-alone IPSs by sharing the position of users [ 40 , 41 , 42 ]. They reduce the use of expensive and/or complex positioning infrastructure while enhancing the position accuracy of users [ 43 , 44 , 45 ]. They reduce positioning ambiguities due to poor geometric location of anchors [ 46 , 47 ].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Collaborative Indoor Positioning Systems: A Systematic Review

Pascacio

Casteleyn

Torres-Sospedra

et al. 2021

Sensors

109

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Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of 84 works, published between 2006 and 2020, have been identified. These articles were analyzed and classified according to the described system’s architecture, infrastructure, technologies, techniques, methods, and evaluation. The results indicate a growing interest in collaborative positioning, and the trend tend to be towards the use of distributed architectures and infrastructure-less systems. Moreover, the most used technologies to determine the collaborative positioning between users are wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the basis of the analysis and results, several promising future research avenues and gaps in research were identified.

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Section: Methodsmentioning

confidence: 99%

Section: Appendix A1 Search Queriesmentioning

confidence: 99%

Section: Study Selection Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Collaborative Indoor Positioning Systems: A Systematic Review

Pascacio

Casteleyn

Torres-Sospedra

et al. 2021

Sensors

109

View full text Add to dashboard Cite

show abstract

“…In [15], the authors experiment with a wide range of KNN parameters to get a set of models. An ensemble result of the generated KNN models has a mean distance error of around 6 m. Besides KNN-based learning methods, decision tree-based learning methods can be used for learning the Wi-Fi signal characteristics with prominent results [4,16]. In [17], a comparison between a LDPL-based approach and fingerprinting-based approaches is introduced, showing that fingerprinting-based approaches have better performance.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Smartphone-Based User Positioning in a Multiple-User Context Using Wireless Technologies

Dao

Vaufreydaz

et al. 2020

Sensors

Self Cite

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For the localization of multiple users, Bluetooth data from the smartphone is able to complement Wi-Fi-based methods with additional information, by providing an approximation of the relative distances between users. In practice, both positions provided by Wi-Fi data and relative distance provided by Bluetooth data are subject to a certain degree of noise due to the uncertainty of radio propagation in complex indoor environments. In this study, we propose and evaluate two approaches, namely Non-temporal and Temporal ones, of collaborative positioning to combine these two cohabiting technologies to improve the tracking performance. In the Non-temporal approach, our model establishes an error observation function in a specific interval of the Bluetooth and Wi-Fi output. It is then able to reduce the positioning error by looking for ways to minimize the error function. The Temporal approach employs an extended error model that takes into account the time component between users’ movements. For performance evaluation, several multi-user scenarios in an indoor environment are set up. Results show that for certain scenarios, the proposed approaches attain over 40% of improvement in terms of average accuracy.

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A hybrid indoor/outdoor detection approach for smartphone-based seamless positioning

et al. 2022

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Indoor/Outdoor (IO) detection (IOD) using Wi-Fi- and smartphone-based technologies is in high demand and interest in both the industrial and research fields. This paper proposes a novel and effective hybrid IOD (HIOD) approach for detecting a smartphone user's IO location. The HIOD approach uses signals received from both Wi-Fi and GPS as well as the latest positioning technologies such as multilateration, fingerprinting and machine learning. This paper proposes and implements two-level signal-to-noise ratio (SNR) threshold parameters for the first time, which are specifically derived from GPS signals through 42 empirical tests at seven test sites with adequate environmental factors considered. Using the newly derived IOD threshold parameters and a set of IO detection rules, the HIOD approach is then tested at 20 test points (TPs) in a city canyon area, where most of the TPs are under semi-indoor or semi-outdoor conditions. The final test results show that a 100% IOD rate is achieved.

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Smartphone-Based User Positioning in a Multiple-User Context with Wi-Fi and Bluetooth

Cited by 12 publications

References 16 publications

Collaborative Indoor Positioning Systems: A Systematic Review

Collaborative Indoor Positioning Systems: A Systematic Review

Collaborative Smartphone-Based User Positioning in a Multiple-User Context Using Wireless Technologies

A hybrid indoor/outdoor detection approach for smartphone-based seamless positioning

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