Bluetooth low energy based occupancy detection for emergency management

A reliable estimation of an area’s occupancy can be beneficial to a large variety of applications, and especially in relation to emergency management. For example, it can help detect areas of priority and assign emergency personnel in an efficient manner. However, occupancy detection can be a major challenge in indoor environments. A recent technology that can prove very useful in that respect is Bluetooth Low Energy (BLE), which is able to provide the location of a user using information from beacons installed in a building. Here, we evaluate BLE as the primary means of occupancy estimation in an indoor environment, using a prototype system composed of BLE beacons, a mobile application and a server. We employ three machine learning approaches (k-nearest neighbours, logistic regression and support vector machines) to determine the presence of occupants inside specific areas of an office space and we evaluate our approach in two independent experimental settings. Our experimental results indicate that combining BLE with machine learning is certainly promising as the basis for occupancy estimation

A bluetooth low energy indoor positioning system with channel diversity, weighted trilateration and Kalman filtering

Indoor Positioning Systems (IPS) using Bluetooth Low Energy (BLE) technology are currently becoming real and available, which has made them grow in popularity and use. However, there are still plenty of challenges related to this technology, especially in terms of Received Signal Strength Indicator (RSSI) fluctuations due to the behaviour of the channels and the multipath effect, that lead to poor precision. In order to mitigate these effects, in this paper we propose and implement a real Indoor Positioning System based on Bluetooth Low Energy, that improves accuracy while reducing power consumption and costs. The three main proposals are: frequency diversity, Kalman filtering and a trilateration method what we have denominated “weighted trilateration”. The analysis of the results proves that all the proposals improve the precision of the system, which goes up to 1.82 m 90% of the time for a device moving in a middle-size room and 0.7 m for static devices. Furthermore, we have proved that the system is scalable and efficient in terms of cost and power consumption. The implemented approach allows using a very simple device (like a SensorTag) on the items to locate. The system enables a very low density of anchor points or references and with a precision better than existing solutionsPeer ReviewedPostprint (published version

Indoor positioning technology assessment using analytic hierarchy process for pedestrian navigation services

Indoor positioning is one of the biggest challenges of many Location Based Services (LBS), especially if the target users are pedestrians, who spend most of their time in roofed areas such as houses, offices, airports, shopping centres and in general indoors. Providing pedestrians with accurate, reliable, cheap, low power consuming and continuously available positional data inside the buildings (i.e. indoors) where GNSS signals are not usually available is difficult. Several positioning technologies can be applied as stand-alone indoor positioning technologies. They include Wireless Local Area Networks (WLAN), Bluetooth Low Energy (BLE), Ultra-Wideband (UWB), Radio Frequency Identification (RFID), Tactile Floor (TF), Ultra Sound (US) and High Sensitivity GNSS (HSGNSS). This paper evaluates the practicality and fitness-to-the-purpose of pedestrian navigation for these stand-alone positioning technologies to identify the best one for the purpose of indoor pedestrian navigation. In this regard, the most important criteria defining a suitable positioning service for pedestrian navigation are identified and prioritised. They include accuracy, availability, cost, power consumption and privacy. Each technology is evaluated according to each criterion using Analytic Hierarchy Process (AHP) and finally the combination of all weighted criteria and technologies are processed to identify the most suitable solution

An IoT-aware AAL System to Capture Behavioral Changes of Elderly People

The ageing of population is a phenomenon that is affecting the majority of developed countries around the world and will soon affect developing economies too. In recent years, both industry and academia are focused on the development of several solutions aimed to guarantee a healthy and safe lifestyle to the elderly. In this context, the behavioral analysis of elderly people can help to prevent the occurrence of Mild Cognitive Impairment (MCI) and frailty problems. The innovative technologies enabling the Internet of Things (IoT) can be used in order to capture personal data for automatically recognizing changes in elderly people behavior in an unobtrusive, low-cost and low-power modality. This work aims to describe the ongoing activities within the City4Age project, funded by the Horizon 2020 Programme of the European Commission, mainly focused on the use of IoT technologies to develop an innovative AAL system able to capture personal data of elderly people in their home and city environments. The proposed architecture has been validated through a proof-of-concept focused mainly on localization issues, collection of ambient parameters, and user-environment interaction aspects

Bluetooth Low Energy based proximity detection and localization in smart communities

Internet of things will bring connected devices to a new level of pervasiveness, where any tangible thing of our daily life may embed some electronics. From a sophisticated smartwatch that embeds complex sensing and communication technologies, to the use of a basic electronic component to implement a digital signature, such as RFIDs. All these smart things worn or distributed around us enables multiple functionalities, when they can interact with each other. In this thesis, I describe the design, characterization and validation of a monitoring system based on Internet of Things technologies, for managing groups moving together in a city. Communication and energy efficiency aspects are firstly explored, to identify Bluetooth Low Energy as a promising protocol enabling scalable and energy efficient networks of things. In the thesis, the protocol has been stressed to demonstrate trade-offs between throughput, energy efficiency, scalability and the possibility to perform multi-hop communication. The potential of the protocol has been exploited within the framework of the CLIMB project. Here, the application requirements and constraints fostered the use of Bluetooth for localization and proximity detection, leading to the investigation of novel strategies to improve accuracy without affecting power consumption and ease of use

Sistema de Vigilancia de Niños por Medio de Alertas Usando Beacons para Prevenir Accidentes

El problema que afecta mayormente a las familias son los accidentes que sufren los niños. Accidentes como: caídas por subir a un estante, cortes por manipular objetos cortantes, inhalar productos tóxicos, quemaduras por tocar objetos calientes, entre otros. Como solución a este problema se propone un sistema que hace uso de alertas en el dispositivo móvil del tutor (persona encargada del cuidado del menor), para informar que un niño puede sufrir un accidente. Esta alerta es activada bajo dos condiciones la primera condición es que el niño camine en dirección a un objeto considerado como peligroso y la segunda condición es que a partir de una distancia determinada por el tutor entre el niño y el objeto, esta se acorte. El sistema además de utilizar dispositivos móviles, utiliza un beacon (dispositivo tecnológico) el cual se ubicara al frente de un objeto considerado como peligroso, el beacon emitirá señales que serán recibidas por el dispositivo móvil del niño para convertirlas en una distancia que luego se enviara a un motor de base de datos. El dispositivo móvil del tutor consultará el motor de base de datos para obtener la información y saber si el niño está cerca de un objeto peligroso; en caso el menor este cerca se activará una alerta informando que un posible accidente puede suceder. Una alerta, es el sonido que emite el dispositivo móvil del tutor para informar de un posible accidente. Entre otras características el sistema mostrara la ubicación (lugar) y la distancia entre el niño y el objeto considerado como peligroso. De esta forma se busca generar un ambiente donde la seguridad del menor sea lo más importante en la familia.Tesi

An IoT-Aware Architecture for Collecting and Managing Data Related to Elderly Behavior

The world population will be made up of a growing number of elderly people in the near future. Aged people are characterized by some physical and cognitive diseases, like mild cognitive impairment (MCI) and frailty, that, if not timely diagnosed, could turn into more severe diseases, like Alzheimer disease, thus implying high costs for treatments and cares. Information and Communication Technologies (ICTs) enabling the Internet of Things (IoT) can be adopted to create frameworks for monitoring elderly behavior which, alongside normal clinical procedures, can help geriatricians to early detect behavioral changes related to such pathologies and to provide customized interventions. As part of the City4Age project, this work describes a novel approach for collecting and managing data about elderly behavior during their normal activities. The data capturing layer is an unobtrusive and low-cost sensing infrastructure abstracting the heterogeneity of physical devices, while the data management layer easily manages the huge quantity of sensed data, giving them semantic meaning and fostering data shareability. This work provides a functional validation of the proposed architecture and introduces how the data it manages can be used by the whole City4Age platform to early identify risks related to MCI/frailty and promptly intervene