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

Abstract

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.

Collaborative Indoor Positioning Systems: A Systematic Review

“…Another remarkable finding in the non-collaborative part is the lack of details of some key aspects of the CIPSs. Around a third of the reviewed papers did not provide enough details about the method used to provide the position estimate, only being cataloged as fingerprint-based [ 42 , 83 , 98 , 100 , 102 , 143 , 147 , 149 ], ranging [ 42 , 46 , 47 , 81 , 93 , 109 , 111 , 113 , 130 , 132 , 144 , 150 ] and RSS-based [ 40 , 41 , 43 , 48 , 77 , 84 , 89 , 103 , 123 , 129 ] methods. In those works, the authors considered the user’s positioning method in the non-collaborative part irrelevant, i.e., the main focus of the CIPS was to improve the user’s position in the collaborative part, regardless of the approach used in the non-collaborative part.…”

“…As with the non-collaborative part, the most used collaborative methods present advantages and drawbacks. The six most used methods were Particle Filter [ 83 , 89 , 93 , 98 , 102 , 117 , 118 , 120 , 123 , 126 , 129 , 136 , 146 , 147 , 149 , 149 ]; Belief Propagation [ 46 , 47 , 96 , 109 , 130 , 131 , 132 , 135 ]; EKF [ 106 , 107 , 115 , 125 , 140 , 141 , 150 ]; Geometric Algorithm [ 43 , 124 , 127 , 134 , 142 , 143 ]; LS [ 45 , 49 , 133 , 137 , 148 ]; Trilateration [ 40 , 41 , 42 , 94 ]. One of the main advantages of the methods based on Particle Filter is their capability of handling non-Gaussian and non-linear estimations; however, their computational complexity increases (increment of the number of particles) as the position accuracy increases.…”

“…M., F. printing-B P.Filter D I-L E PA [ 148 ] 5G 5G RSSI RSSI Coop. Algorithm LS N/S W/I S PA [ 149 ] Wi-Fi Bluetooth F. printing RSSI F. printing-B P. Filter C I-L E PA 2019 [ 141 ] IMU UWB DR TWR PDR-B. M. EKF D W/I E PA [ 140 ] UWB UWB TWR TWR Coop.…”

Sensors and Sensing Technologies for Indoor Positioning and Indoor Navigation

“…The authors in [ 17 ] report and evaluate two approaches of collaborative positioning by combining Wi-Fi and Bluetooth data in order to improve the overall performances in tracking a device. These two approaches are distinguished in non-temporal and temporal.…”

Decision Support Algorithm Based on the Concentrations of Air Pollutants Visualization

“…Through an API, the platform loads the data provided by the Garmin mobile application. Smartphones for detecting and reporting the presence of a user in an outdoor area using Global Positioning System (GPS) technology or in an indoor area using BLE beacons [ 40 , 41 ]. These sensors are also defined in the platform as mobile.…”