scholarly journals Indoor localization systems for retail stores

2019 ◽  
Author(s):  
Βασίλειος Σταύρου
Keyword(s):  
Indoor Localization ◽  
Design Science ◽  
Low Energy ◽  
Retail Stores ◽  
Bluetooth Low Energy ◽  
Localization Systems

Η συμπεριφορά του καταναλωτή μέσα στο κατάστημα αποτελεί ερευνητική περιοχή ενδιαφέροντος για περισσότερα από 60 χρόνια με στόχο να εντοπίσει διάφορα μοτίβα που μπορούν να προσφέρουν αξία. Η κατανόηση της συμπεριφοράς του καταναλωτή μπορεί να οδηγήσει σε σημαντικές πληροφορίες για τους λιανέμπορους και να ενισχύσει την εμπειρία των καταναλωτών μέσα στο κατάστημα. Παρά τη δημοτικότητα της Χωρικής Αναλυτικής και των υπηρεσιών βάσει τοποθεσίας, και οι δύο αντιμετωπίζουν ένα κοινό πρόβλημα, την ακρίβεια του μηχανισμού εντοπισμού που χρησιμοποιούν. Για την αντιμετώπιση των ερευνητικών ερωτημάτων της παρούσας διατριβής, (α) υιοθετούμε ως μεθοδολογική προσέγγιση το μοντέλο Design Science, (β) αναπτύσσουμε ένα σύστημα εντοπισμού θέσης για εσωτερικά περιβάλλοντα και (γ) υιοθετούμε μια προσέγγιση μηχανικής μάθησης που εκτελεί εντοπισμό θέσης. Συνοπτικά, σχεδιάζουμε και αναπτύσσουμε ένα σύστημα που παράγει και επεξεργάζεται δεδομένα σήματος και αναπτύσσουμε μια προσέγγιση μηχανικής μάθησης για τον εντοπισμό θέσης που μπορεί να εφαρμοστεί σε χωροχρονικά δεδομένα από συσκευές Διαδικτύου των Πραγμάτων (IoT). Αξιολογούμε δύο διαφορετικές ασύρματες τεχνολογίες ((α) Wi-Fi και (β) Bluetooth Low Energy Beacons) και εφαρμόζουμε τεχνικές τεχνητής νοημοσύνης για την αντιμετώπιση των ερευνητικών ερωτημάτων. Επιπλέον, για την αντιμετώπιση των ερευνητικών ζητημάτων προτείνουμε ένα artifact συστήματος το οποίο είναι υπεύθυνο για τη δημιουργία, την καταγραφή και την επεξεργασία των δεδομένων για τον εντοπισμό θέσης σε εσωτερικά περιβάλλοντα. Το αποτέλεσμα της προτεινόμενης προσέγγισης είναι η θέση του χρήστη της ασύρματης υποδομής μέσα στο κατάστημα. Εφαρμόζουμε αυτήν την προσέγγιση σε δύο διαφορετικές περιπτώσεις. Η πρώτη περίπτωση αφορά την τεχνολογία BLE Beacons, ενώ η δεύτερη αφορά τεχνολογία Wi-Fi. Στη συνέχεια αξιολογούμε τα ευρήματα χρησιμοποιώντας τεχνική αποτίμηση της επίδοσης της και επίσης εξετάζουμε την επιχειρηματική ερμηνεία των αποτελεσμάτων. Για το σκοπό αυτό, αξιοποιούμε την αξιολόγηση βάσει δεδομένων και την αξιολόγηση βάσει χρηστών, προκειμένου να αξιολογήσουμε τα αποτελέσματα της προσέγγισης εντοπισμού θέσης. Τέλος, παραθέτουμε μια σειρά πρακτικές εφαρμογές με βάση τη διάσταση της θέσης.

scholarly journals Indoor Positioning for Monitoring Older Adults at Home: Wi-Fi and BLE Technologies in Real Scenarios

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 728 ◽  
Author(s):  
Raul Montoliu ◽  
Emilio Sansano ◽  
Arturo Gascó ◽  
Oscar Belmonte ◽  
Antonio Caballer
Keyword(s):  
Older Adults ◽  
Indoor Localization ◽  
Indoor Positioning ◽  
Low Energy ◽  
Real Case ◽  
Bluetooth Low Energy ◽  
Localization System ◽  
Localization Systems ◽  
At Home

This paper presents our experience on a real case of applying an indoor localization system for monitoring older adults in their own homes. Since the system is designed to be used by real users, there are many situations that cannot be controlled by system developers and can be a source of errors. This paper presents some of the problems that arise when real non-expert users use localization systems and discusses some strategies to deal with such situations. Two technologies were tested to provide indoor localization: Wi-Fi and Bluetooth Low Energy. The results shown in the paper suggest that the Bluetooth Low Energy based one is preferable in the proposed task.

scholarly journals Indoor Bluetooth Low Energy Dataset for Localization, Tracking, Occupancy, and Social Interaction

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4462 ◽  
Author(s):  
Paolo Baronti ◽  
Paolo Barsocchi ◽  
Stefano Chessa ◽  
Fabio Mavilia ◽  
Filippo Palumbo
Keyword(s):  
Social Interaction ◽  
Indoor Localization ◽  
Meaningful Use ◽  
Research Area ◽  
Low Energy ◽  
Bluetooth Low Energy ◽  
Novel Technologies ◽  
Wide Availability ◽  
Low Costs ◽  
Open Datasets

Indoor localization has become a mature research area, but further scientific developments are limited due to the lack of open datasets and corresponding frameworks suitable to compare and evaluate specialized localization solutions. Although several competitions provide datasets and environments for comparing different solutions, they hardly consider novel technologies such as Bluetooth Low Energy (BLE), which is gaining more and more importance in indoor localization due to its wide availability in personal and environmental devices and to its low costs and flexibility. This paper contributes to cover this gap by: (i) presenting a new indoor BLE dataset; (ii) reviewing several, meaningful use cases in different application scenarios; and (iii) discussing alternative uses of the dataset in the evaluation of different positioning and navigation applications, namely localization, tracking, occupancy and social interaction.

Bluetooth Low-Energy-Based Applications

2018 ◽  
pp. 95-112
Author(s):  
Smita Sanjay Ambarkar ◽  
Rakhi Dattatraya Akhare
Keyword(s):  
Indoor Localization ◽  
Low Energy ◽  
Smart Devices ◽  
The Internet ◽  
Bluetooth Low Energy ◽  
Iot Applications ◽  
Proximity Detection ◽  
Battery Power ◽  
Commercial Market ◽  
The Internet Of Things

This chapter focuses on the comprehensive contents of various applications and principles related to Bluetooth low energy (BLE). The internet of things (IoT) applications like indoor localization, proximity detection problem by using Bluetooth low energy, and enhancing the sales in the commercial market by using BLE have the same database requirement and common implementation idea. The real-world applications are complex and require intensive computation. These computations should take less time, cost, and battery power. The chapter mainly focuses on the usage of BLE beacons for indoor localization. The motive behind the study of BLE devices is that it is supported by mobile smart devices that augment its application exponentially.

STUPEFY: Set-Valued Box Particle Filtering for Bluetooth Low Energy-Based Indoor Localization

2019 ◽  
Vol 26 (12) ◽  
pp. 1773-1777 ◽  
Author(s):  
Parvin Malekzadeh ◽  
Arash Mohammadi ◽  
Mihai Barbulescu ◽  
Konstantinos N. Plataniotis
Keyword(s):  
Particle Filtering ◽  
Indoor Localization ◽  
Low Energy ◽  
Bluetooth Low Energy

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

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4550 ◽  
Author(s):  
Vasilis Stavrou ◽  
Cleopatra Bardaki ◽  
Dimitris Papakyriakopoulos ◽  
Katerina Pramatari
Keyword(s):  
Random Forest ◽  
Indoor Localization ◽  
Indoor Positioning ◽  
Retail Store ◽  
Low Energy ◽  
Location Based Services ◽  
Shopping Behavior ◽  
Multiple Sources ◽  
Bluetooth Low Energy ◽  
Product Categories

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.

scholarly journals Improving Accuracy and Reliability of Bluetooth Low-Energy-Based Localization Systems Using Proximity Sensors

2019 ◽  
Vol 9 (19) ◽  
pp. 4081 ◽  
Author(s):  
Marcin Kolakowski
Keyword(s):  
Assisted Living ◽  
Experimental Studies ◽  
Low Energy ◽  
Elderly Persons ◽  
Bluetooth Low Energy ◽  
Localization Accuracy ◽  
Hybrid Positioning ◽  
Improving Accuracy ◽  
The Cost ◽  
Localization Systems

One of the functionalities which are desired in Ambient and Assisted Living systems is accurate user localization at their living place. One of the best-suited solutions for this purpose from the cost and energy efficiency points of view are Bluetooth Low Energy (BLE)-based localization systems. Unfortunately, their localization accuracy is typically around several meters and might not be sufficient for detection of abnormal situations in elderly persons behavior. In this paper, a concept of a hybrid positioning system combining typical BLE-based infrastructure and proximity sensors is presented. The proximity sensors act a supporting role by additionally covering vital places, where higher localization accuracy is needed. The results from both parts are fused using two types of hybrid algorithms. The paper contains results of simulation and experimental studies. During the experiment, an exemplary proximity sensor VL53L1X has been tested and its basic properties modeled for use in the proposed algorithms. The results of the study have shown that employing proximity sensors can significantly improve localization accuracy in places of interest.

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