Indoor positioning of shoppers using a network of bluetooth low energy beacons

In this paper we present our work on the indoor positioning of users (shoppers), using a network of Bluetooth Low Energy (BLE) beacons deployed in a large wholesale shopping store. Our objective is to accurately determine which product sections a user is adjacent to while traversing the store, using RSSI readings from multiple beacons, measured asynchronously on a standard commercial mobile device. We further wish to leverage the store layout (which imposes natural constraints on the movement of users) and the physical configuration of the beacon network, to produce a robust and efficient solution. We start by describing our application context and hardware configuration, and proceed to introduce our node-graph model of user location. We then describe our experimental work which begins with an investigation of signal characteristics along and across aisles. We propose three methods of localization, using a “nearest-beacon” approach as a base-line; exponentially averaged weighted range estimates; and a particle-filter method based on the RSSI attenuation model and Gaussian-noise. Our results demonstrate that the particle filter method significantly out-performs the others. Scalability also makes this method ideal for applications run on mobile devices with more limited computational capabilitie

Understanding collaborative workspaces:spatial affordances & time constraints

Abstract. This thesis presents a generic solution for indoor positioning and movement monitoring, positioning data collection and analysis with the aim of improving the interior design of collaborative workspaces. Since the nature of the work and the work attitude of employees varies in different workspaces, no general workspace layout can be applied to all situations. Tailoring workspaces according to the exact needs and requirements of the employees can improve collaboration and productivity. Here, an indoor positioning system based on Bluetooth Low Energy technology was designed and implemented in a pilot area (an IT company), and the position of the employees was monitored during a two months period. The pilot area consisted of an open workplace with workstations for nine employees and two sets of coffee tables, four meeting rooms, two coffee rooms and a soundproof phone booth. Thirteen remixes (BLE signal receivers) provided full coverage over the pilot area, while light durable BLE beacons, which were carried by employees acted as BLE signal broadcasters. The RSSIs of the broadcasted signals from the beacons were recorded by each remix within the range of the signal and the gathered data was stored in a database. The gathered RSSI data was normalized to decrease the effect of workspace obstacles on the signal strength. To predict the position of beacons based on the recorded RSSIs, a few approaches were tested, and the most accurate one was chosen, which provided an above 95% accuracy in predicting the position of each beacon every 3 minutes. This approach was a combination of fingerprinting with a Machine Learning-based Random Forest Classifier. The obtained position results were then used to extract various information about the usage pattern of different workspace areas to accurately access the current layout and the needs of the employees

Analysis and evaluation of Wi-Fi indoor positioning systems using smartphones

This paper attempts to analyze the main algorithms used in Machine Learning applied to the indoor location. New technologies are facing new challenges. Satellite positioning has become a typical application of mobile phones, but stops working satisfactorily in enclosed spaces. Currently there is a problem in positioning which is unresolved. This circumstance motivates the research of new methods. After the introduction, the first chapter presents current methods of positioning and the problem of positioning indoors. This part of the work shows globally the current state of the art. It mentions a taxonomy that helps classify the different types of indoor positioning and a selection of current commercial solutions. The second chapter is more focused on the algorithms that will be analyzed. It explains how the most widely used of Machine Learning algorithms work. The aim of this section is to present mathematical algorithms theoretically. These algorithms were not designed for indoor location but can be used for countless solutions. In the third chapter, we learn gives tools work: Weka and Python. the results obtained after thousands of executions with different algorithms and parameters showing main problems of Machine Learning shown. In the fourth chapter the results are collected and the conclusions drawn are shown

Eavesdropping Whilst You're Shopping: Balancing Personalisation and Privacy in Connected Retail Spaces

Physical retailers, who once led the way in tracking with loyalty cards and `reverse appends', now lag behind online competitors. Yet we might be seeing these tables turn, as many increasingly deploy technologies ranging from simple sensors to advanced emotion detection systems, even enabling them to tailor prices and shopping experiences on a per-customer basis. Here, we examine these in-store tracking technologies in the retail context, and evaluate them from both technical and regulatory standpoints. We first introduce the relevant technologies in context, before considering privacy impacts, the current remedies individuals might seek through technology and the law, and those remedies' limitations. To illustrate challenging tensions in this space we consider the feasibility of technical and legal approaches to both a) the recent `Go' store concept from Amazon which requires fine-grained, multi-modal tracking to function as a shop, and b) current challenges in opting in or out of increasingly pervasive passive Wi-Fi tracking. The `Go' store presents significant challenges with its legality in Europe significantly unclear and unilateral, technical measures to avoid biometric tracking likely ineffective. In the case of MAC addresses, we see a difficult-to-reconcile clash between privacy-as-confidentiality and privacy-as-control, and suggest a technical framework which might help balance the two. Significant challenges exist when seeking to balance personalisation with privacy, and researchers must work together, including across the boundaries of preferred privacy definitions, to come up with solutions that draw on both technology and the legal frameworks to provide effective and proportionate protection. Retailers, simultaneously, must ensure that their tracking is not just legal, but worthy of the trust of concerned data subjects.Comment: 10 pages, 1 figure, Proceedings of the PETRAS/IoTUK/IET Living in the Internet of Things Conference, London, United Kingdom, 28-29 March 201

Digitalization of Retail Stores using Bluetooth Low Energy Beacons

This thesis explores the domains of retail stores and the Internet of Things, with a focus on Bluetooth Low Energy beacons. It investigates how one can use the technology to improve physical stores, for the benefit of both the store and the customers. It does this by going through literature and information from academia and the relevant industry. Additionally, an interview with an expert in the retail domain is conducted, and a survey consisting of a series of interviews and questionnaire with what can be considered experts in the IT domain. A prototype app called Stass is developed, the app demonstrates some of the usages of the technology and is also used for evaluating the performance of the beacons.Masteroppgave i informasjonsvitenskapINFO39

Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application

[EN] The study presents some results of customer paths¿ analysis in a shopping mall. Bluetooth-based technology is used to collect data. The event log containing spatiotemporal information is analyzed with process mining. Process mining is a technique that enables one to see the whole process contrary to data-centric methods. The use of process mining can provide a readily-understandable view of the customer paths. We installed iBeacon devices, a Bluetooth-based positioning system, in the shopping mall. During December 2017 and January and February 2018, close to 8000 customer data were captured. We aim to investigate customer behaviors regarding gender by using their paths. We can determine the gender of customers if they go to the men¿s bathroom or women¿s bathroom. Since the study has a comprehensive scope, we focused on male and female customers¿ behaviors. This study shows that male and female customers have different behaviors. Their duration and paths, in general, are not similar. In addition, the study shows that the process mining technique is a viable way to analyze customer behavior using Bluetooth-based technology.Dogan, O.; Bayo-Monton, JL.; Fernández Llatas, C.; Oztaysi, B. (2019). Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application. Sensors. 19(3):1-20. https://doi.org/10.3390/s19030557S120193Oosterlinck, D., Benoit, D. F., Baecke, P., & Van de Weghe, N. (2017). Bluetooth tracking of humans in an indoor environment: An application to shopping mall visits. Applied Geography, 78, 55-65. doi:10.1016/j.apgeog.2016.11.005Merad, D., Aziz, K.-E., Iguernaissi, R., Fertil, B., & Drap, P. (2016). Tracking multiple persons under partial and global occlusions: Application to customers’ behavior analysis. Pattern Recognition Letters, 81, 11-20. doi:10.1016/j.patrec.2016.04.011Wu, Y., Wang, H.-C., Chang, L.-C., & Chou, S.-C. (2015). 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LOCATION-BASED MARKETING: CONCEPTS, TECHNOLOGIES AND SERVICES

siirretty Doriast

Радиочастотные технологии локального позиционирования в здравоохранении

Introduction. Localization of objects position in closed space plays an important role in many areas of human activity, including medicine. Using indoor-positioning technologies as a part of telemedicine systems allows one to improve the quality of medical care and to reduce mortality of patients. Therefore, indoor-positioning technologies contribute to achieve the goals outlined in the Russian Federation government`s program "Healthcare development". Aim. To study the applicability of modern radiofrequency technologies for localization of patients inside a hospital building. Materials and methods. Scientific sources devoted to indoor-positioning based on radiofrequency technologies were analyzed. The methods used included: - bibliographic retrieval; - selection and verification of sources based on their relevance; - analysis of sources by methods of deconstruction and comparative analysis . Results. The result of the analysis indicated that radiofrequency positioning technologies allow one to locate objects using radio waves properties. The disadvantage of the technology is the penetration of radio signal through walls and floors. Given this, it is necessary to use complex algorithms to detect an object with accuracy to a specific room. Despite this disadvantage, radiofrequency technologies can be used for positioning in medical facilities since they are easy in deployment and service. Also, they are used in ready-made commercial solutions. ZigBee technology is an exception because it does not allow one to track moving objects in real-time. Conclusion. Based on the study it was concluded that BLE technology is the most suitable for indoor-positioning in medical facilities. It is energy-efficient, it has sufficiently fast data transfer rate, good communication radius and a large range of ready-made communication equipment. It is also worth noting that most wireless medical sensors exchange data via the BLE interface.Введение. Определение местоположения объектов в закрытом помещении играет большую роль во многих сферах деятельности человека, в том числе и в медицине. Использование технологий локального позиционирования в составе телемедицинских систем позволяет повысить качество оказания медицинской помощи и снизить смертность пациентов, что способствует достижению целей, обозначенных в государственной программе Российской Федерации "Развитие здравоохранения". Цель работы. Анализ применимости современных радиочастотных технологий для определения местоположения пациента в здании стационара. Материалы и методы. В ходе выполнения данной работы анализируются научные источники, посвященные локальному позиционированию на базе радиочастотных технологий. Используемые методы включают в себя: – библиографический поиск; – отбор и проверку источников с учетом их актуальности, соответствия тематике и авторитетности; – анализ источников с использованием методов деконструкции и сравнительного анализа. Результаты. Результат анализа показывает, что радиочастотные технологии позиционирования позволяют определять местоположение объектов, используя свойства радиоволн. Основной недостаток данной технологии заключается в том, что из-за проникновения радиосигналов сквозь стены и перекрытия приходится использовать сложные вычислительные алгоритмы для обнаружения объекта с точностью до конкретного помещения. Несмотря на данный недостаток, радиочастотные технологии могут применяться для позиционирования в медицинских учреждениях, так как они просты в развертывании и обслуживании и используются в готовых коммерческих решениях. Исключением является технология ZigBee, так как она не позволяет отслеживать подвижные объекты в режиме реального времени. Заключение. На основе проведенного исследования сделан вывод о том, что технология BLE является наиболее подходящей для позиционирования в медицинских учреждениях, так как она обладает низким энергопотреблением, достаточно высокой скоростью передачи данных, хорошим радиусом связи и большим выбором готового коммуникационного оборудования. Также стоит отметить, что большинство беспроводных медицинских датчиков осуществляют обмен данными через интерфейс BLE