Smart Parking System Based on Bluetooth Low Energy Beacons with Particle Filtering

Urban centers and dense populations are expanding, hence, there is a growing demand for novel applications to aid in planning and optimization. In this work, a smart parking system that operates both indoor and outdoor is introduced. The system is based on Bluetooth Low Energy (BLE) beacons and uses particle filtering to improve its accuracy. Through simple BLE connectivity with smartphones, an intuitive parking system is designed and deployed. The proposed system pairs each spot with a unique BLE beacon, providing users with guidance to free parking spaces and a secure and automated payment scheme based on real-time usage of the parking space. Three sets of experiments were conducted to examine different aspects of the system. A particle filter is implemented in order to increase the system performance and improve the credence of the results. Through extensive experimentation in both indoor and outdoor parking spaces, the system was able to correctly predict which spot the user has parked in, as well as estimate the distance of the user from the beacon

BLE Beacons for Indoor Positioning at an Interactive IoT-Based Smart Museum

The Internet of Things (IoT) can enable smart infrastructures to provide advanced services to the users. New technological advancement can improve our everyday life, even simple tasks as a visit to the museum. In this paper, an indoor localization system is presented, to enhance the user experience in a museum. In particular, the proposed system relies on Bluetooth Low Energy (BLE) beacons proximity and localization capabilities to automatically provide the users with cultural contents related to the observed artworks. At the same time, an RSS-based technique is used to estimate the location of the visitor in the museum. An Android application is developed to estimate the distance from the exhibits and collect useful analytics regarding each visit and provide a recommendation to the users. Moreover, the application implements a simple Kalman filter in the smartphone, without the need of the Cloud, to improve localization precision and accuracy. Experimental results on distance estimation, location, and detection accuracy show that BLE beacon is a promising solution for an interactive smart museum. The proposed system has been designed to be easily extensible to the IoT technologies and its effectiveness has been evaluated through experimentation

Ibeacon based proximity and indoor localization system

User location can be leveraged to provide a wide range of services in a variety of indoor locations including retails stores, hospitals, airports, museums and libraries etc. The widescale proliferation of user devices such as smart phones and the interconnectivity among different entities, powered by Internet of Things (IoT), makes user device-based localization a viable approach to provide Location Based Services (LBS). Location based services can be broadly classified into 1) Proximity based services that provides services based on a rough estimate of users distance to any entity, and 2) Indoor localization that locates a user\u27s exact location in the indoor environment rather than a rough estimate of the distance. The primary requirements of these services are higher energy efficiency, localization accuracy, wide reception range, low cost and availability. Technologies such as WiFi, Radio Frequency Identification (RFID) and Ultra Wideband (UWB) have been used to provide both indoor localization and proximity based services. Since these technologies are not primarily intended for LBS, they do not fulfill the aforementioned requirements. Bluetooth Low Energy (BLE) enabled beacons that use Apple\u27s proprietary iBeacon protocol are mainly intended to provide proximity based services. iBeacons satisfy the energy efficiency, wide reception range and availability requirements of LBS. However, iBeacons are prone to noise due to their reliance on Received Signal Strength Indicator (RSSI), which drastically fluctuates in indoor environments due to interference from different obstructions. This limits its proximity detection accuracy. In this thesis, we present an iBeacon based proximity and indoor localization system. We present our two server-based algorithms to improve the proximity detection accuracy by reducing the variation in the RSSI and using the RSSI-estimated distance, rather than the RSSI itself, for proximity classification. Our algorithms Server-side Running Average and Server-side Kalman Filter improves the proximity detection accuracy by 29% and 32% respectively in contrast to Apple\u27s current approach of using moving average of RSSI values for proximity classification. We utilize a server-based approach because of the greater computing power of servers. Furthermore, server-based approach helps reduce the energy consumption of user device. We describe our cloud based architecture for iBeacon based proximity detection. We also use iBeacons for indoor localization. iBeacons are not primarily intended for indoor localization as their reliance on RSSI makes them unsuitable for accurate indoor localization. To improve the localization accuracy, we use Bayesian filtering algorithms such as Particle Filter (PF), Kalman Filter (KF), and Extended Kalman Filter (EKF). We show that by cascading Kalman Filter and Extended Kalman Filter with Particle Filter, the indoor localization accuracy can be improved by 28% and 33.94% respectively when compared with only using PF. The PF, KFPF and PFEKF algorithm on the server side have average localization error of 1.441 meters, 1.0351 meters and 0.9519 meters respectively

Low-cost indoor localization system combining multilateration and Kalman filter

Indoor localization systems play an important role to track objects during their life-cycle in indoor environments, e.g., related to retail, logistics and mobile robotics. These positioning systems use several techniques and technologies to estimate the position of each object, and face several requirements such as position accuracy, security, range of coverage, energy consumption and cost. This paper describes a practical implementation of a BLE (Bluetooth Low Energy) based localization system that combines multilateration and Kalman filter techniques to achieve a low cost solution, maintaining a good position accuracy. The proposed approach was experimentally tested in an indoor environment, with the achieved results showing a clear low cost system presenting an increase of the estimated position accuracy by 10% for an average error of 2.33 metersThis work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope UIDB/05757/2020.info:eu-repo/semantics/publishedVersio

A Safety System based on Bluetooth Low Energy (BLE) to prevent the misuse of Personal Protection Equipment (PPE) in construction

In this paper we address the issue of safety in the use of Personal Protection Equipment (PPE) in construction, industrial, or similar sites where power tools are used. We propose a novel solution that can control actively the power of the tool depending on the worker–tool distance. It is based on RSSI information transmitted by BLE devices arranged in a particular rig, combined with a Bayesian distance estimator. Such an approach minimizes the required instrumentation of the workplace and also the number of configuration parameters; therefore it enables a wide range of applications. Our aim is not only to signal risky situations caused by the misuse of the PPE (either due to its bad fitting or a wrong distance to the tool), but to intervene in a fast and robust way to avoid the safety risk. This solution is built upon previous results on the statistically sound measurement of distances and closeness in construction sites. Here, we contribute with a thorough analysis of collocating several BLE transmitters near orthogonally, which reduces interferences while avoiding the cost of more advanced technologies. We study how many transmitters are needed and what parameters are the best in the Bayesian filter for the optimal performance of the system. Real experiments with a prototype have been conducted in a construction workshop where a person operates a miter saw. The results show how the correct use of the PPE (an earmuff equipped with the BLE transmitters) can be inferred from the distance estimation in a robust and reliable way.This research received funding from Plan Propio-Universidad de Málaga and it is associated to the Proyecto Puente “Integración de dispositivos basados en el paradigma loT para la mejora de seguridad laboral en proyectos de contrucción (IoTcons)”. Funding for open access charge: Universidad de Málaga / CBU