An Ensemble Filter for Indoor Positioning in a Retail Store Using Bluetooth Low Energy Beacons

Abstract

This paper has developed and deployed a Bluetooth Low Energy (BLE) beacon-based indoor positioning system in a two-floor retail store. The ultimate purpose of this study was to compare the different indoor positioning techniques towards achieving efficient position determination of moving customers in the retail store. The innovation of this research lies in its context (the retail store) and the fact that this is not a laboratory, controlled experiment. Retail stores are challenging environments with multiple sources of noise (e.g., shoppers’ moving) that impede indoor localization. To the best of the authors’ knowledge, this is the first work concerning indoor localization of consumers in a real retail store. This study proposes an ensemble filter with lower absolute mean and root mean squared errors than the random forest. Moreover, the localization error is approximately 2 m, while for the random forest, it is 2.5 m. In retail environments, even a 0.5 m deviation is significant because consumers may be positioned in front of different store shelves and, thus, different product categories. The more accurate the consumer localization, the more accurate and rich insights on the customers’ shopping behavior. Consequently, retailers can offer more effective customer location-based services (e.g., personalized offers) and, overall, better consumer localization can improve decision making in retailing.

A Novel IoT Based Positioning and Shadowing System for Dementia Training

“…Therefore, machine learning algorithms have been added in many studies to estimate the final position with improved accuracy. For example, Stavrou et al [ 20 ] used a BLE beacon as an ensemble filter for indoor positioning. In the study, a fingerprint positioning combined with random forest algorithm was used in a retail store positioning application, and the positioning error results were 2 m–2.5 m. The authors noted that interference in the environment should be evaluated when performing indoor positioning, and the interference should be eliminated to achieve more satisfactory results.…”

“…In the study, a fingerprint positioning combined with random forest algorithm was used in a retail store positioning application, and the positioning error results were 2 m–2.5 m. The authors noted that interference in the environment should be evaluated when performing indoor positioning, and the interference should be eliminated to achieve more satisfactory results. In the future, WiFi technology can be integrated to explore the effectiveness of positioning [ 20 ]. Ho and Chan [ 21 ] proposed a decentralized BLE-based positioning protocol.…”

Improved Bluetooth Low Energy Sensor Detection for Indoor Localization Services

“…Artificial intelligence-based localization techniques include methods such as artificial neural networks, neural fuzzy inference systems, and particle swarm optimization. Each technique has certain advantages and disadvantages, and the selection of the most appropriate one depends on the application context [13]. In existing research, studies have often combined different techniques (e.g., [19,[31][32][33]) that can provide better performance.…”

“…The existing literature has provided a vast plethora of studies that have dealt with the performance evaluation or performance improvement of RSSI-based localization methods. BLE localization performance studies have mostly reported results from measurements in laboratory settings (e.g., [37,40,41]), although several studies have reported performance evaluation in real-world environments, such as museums [27], office environments (e.g., [14,[42][43][44]), a construction site [14], a retail store [13], and underground parking [28].…”

“…In existing research, several innovative solutions based on BLE-based localization systems have been proposed and evaluated, including indoor guidance and location [12], indoor positioning in a retail store [13], driver assistance in indoor parking areas [14], navigation systems for car searching in indoor parking garages [15], the real-time tracking of construction laborers [16], location-enhanced activity recognition in indoor environments [17], multi-residential activity labeling for smart homes solutions [18], location system for smart home control [19], the navigation of visually impaired people in buildings [20], and elderly people monitoring [21].…”