Enhancing Indoor Localisation: a Bluetooth Low Energy (BLE) Beacon Placement approach

Indoor location-based services have become increasingly vital in various sectors, including industries, healthcare, airports, and crowded infrastructures, facilitating asset tracking and user navigation. This project addresses the critical challenge of optimising beacon placement for indoor location, employing Bluetooth technology as the communication protocol. The significance of this research lies in the effi ciency and accuracy that an optimised beacon layout can provide, enhancing the effectiveness of indoor positioning systems. The algorithm developed takes into con sideration materials attenuation, coverage and Line of Sight (LOS) conditions to optimise its layouts. Experimental validation of the algorithm’s performance was conducted by comparing two beacon layouts: one optimised by the algorithm and the other manually arranged by individuals with empirical knowledge in the field. The experiment considered three distinct positions within the schematic, allowing for a comprehensive assessment of the optimised layout’s superior performance. The results of this research offer insights into the potential of the algorithm to revolu tionise indoor location services, providing a more reliable and cost-effective solution for a multitude of applications.Os serviços de localização em ambientes internos tornaram-se cada vez mais essenciais em vários setores, incluindo indústrias, cuidados de saúde, aeroportos e infraestruturas movimentadas, facilitando o rastreamento de objetos e a navegação de utilizadores. Este projeto aborda o desafio crítico da otimização da colocação de beacons para localização em ambientes internos, utilizando a tecnologia Bluetooth como protocolo de comunicação. A importância desta pesquisa reside na eficiência e precisão que uma disposição otimizada de beacons pode proporcionar, melhorando a eficácia de sistemas de posicionamento em ambientes internos. O algoritmo desenvolvido leva em consideração a atenuação de materiais, a cobertura e as condições de visão direta para otimizar as suas disposições. A validação experimental do desempenho do algoritmo foi realizada ao comparar duas disposições de beacons: uma otimizada pelo algoritmo e outra organizada manualmente por indivíduos com conhecimento empírico na área. A experiência considerou três posições distintas no esquema, permitindo uma avaliação abrangente do desempenho superior da disposição otimizada. Os resultados desta pesquisa oferecem descobertas importantes sobre o potencial do algoritmo para revolucionar os serviços de localização em ambientes internos, proporcionando uma solução mais confiável e econômica para uma variedade de aplicações

Low-Cost Indoor Localisation Based on Inertial Sensors, Wi-Fi and Sound

The average life expectancy has been increasing in the last decades, creating the need for new technologies to improve the quality of life of the elderly. In the Ambient Assisted Living scope, indoor location systems emerged as a promising technology capable of sup porting the elderly, providing them a safer environment to live in, and promoting their autonomy. Current indoor location technologies are divided into two categories, depend ing on their need for additional infrastructure. Infrastructure-based solutions require expensive deployment and maintenance. On the other hand, most infrastructure-free systems rely on a single source of information, being highly dependent on its availability. Such systems will hardly be deployed in real-life scenarios, as they cannot handle the absence of their source of information. An efficient solution must, thus, guarantee the continuous indoor positioning of the elderly. This work proposes a new room-level low-cost indoor location algorithm. It relies on three information sources: inertial sensors, to reconstruct users’ trajectories; environ mental sound, to exploit the unique characteristics of each home division; and Wi-Fi, to estimate the distance to the Access Point in the neighbourhood. Two data collection protocols were designed to resemble a real living scenario, and a data processing stage was applied to the collected data. Then, each source was used to train individual Ma chine Learning (including Deep Learning) algorithms to identify room-level positions. As each source provides different information to the classification, the data were merged to produce a more robust localization. Three data fusion approaches (input-level, early, and late fusion) were implemented for this goal, providing a final output containing complementary contributions from all data sources. Experimental results show that the performance improved when more than one source was used, attaining a weighted F1-score of 81.8% in the localization between seven home divisions. In conclusion, the evaluation of the developed algorithm shows that it can achieve accurate room-level indoor localization, being, thus, suitable to be applied in Ambient Assisted Living scenarios.O aumento da esperança média de vida nas últimas décadas, criou a necessidade de desenvolvimento de tecnologias que permitam melhorar a qualidade de vida dos idosos. No âmbito da Assistência à Autonomia no Domicílio, sistemas de localização indoor têm emergido como uma tecnologia promissora capaz de acompanhar os idosos e as suas atividades, proporcionando-lhes um ambiente seguro e promovendo a sua autonomia. As tecnologias de localização indoor atuais podem ser divididas em duas categorias, aquelas que necessitam de infrastruturas adicionais e aquelas que não. Sistemas dependentes de infrastrutura necessitam de implementação e manutenção que são muitas vezes dispendiosas. Por outro lado, a maioria das soluções que não requerem infrastrutura, dependem de apenas uma fonte de informação, sendo crucial a sua disponibilidade. Um sistema que não consegue lidar com a falta de informação de um sensor dificilmente será implementado em cenários reais. Uma solução eficiente deverá assim garantir o acompanhamento contínuo dos idosos. A solução proposta consiste no desenvolvimento de um algoritmo de localização indoor de baixo custo, baseando-se nas seguintes fontes de informação: sensores inerciais, capazes de reconstruir a trajetória do utilizador; som, explorando as características dis tintas de cada divisão da casa; e Wi-Fi, responsável pela estimativa da distância entre o ponto de acesso e o smartphone. Cada fonte sensorial, extraída dos sensores incorpora dos no dispositivo, foi, numa primeira abordagem, individualmente otimizada através de algoritmos de Machine Learning (incluindo Deep Learning). Como os dados das diversas fontes contêm informação diferente acerca das mesmas características do sistema, a sua fusão torna a classificação mais informada e robusta. Com este objetivo, foram implementadas três abordagens de fusão de dados (input data, early and late fusion), fornecendo um resultado final derivado de contribuições complementares de todas as fontes de dados. Os resultados experimentais mostram que o desempenho do algoritmo desenvolvido melhorou com a inclusão de informação multi-sensor, alcançando um valor para F1- score de 81.8% na distinção entre sete divisões domésticas. Concluindo, o algoritmo de localização indoor, combinando informações de três fontes diferentes através de métodos de fusão de dados, alcançou uma localização room-level e está apto para ser aplicado num cenário de Assistência à Autonomia no Domicílio

Location estimation in smart homes setting with RFID systems

Indoor localisation technologies are a core component of Smart Homes. Many applications within Smart Homes benefit from localisation technologies to determine the locations of things, objects and people. The tremendous characteristics of the Radio Frequency Identification (RFID) systems have become one of the enabler technologies in the Internet of Things (IOT) that connect objects and things wirelessly. RFID is a promising technology in indoor positioning that not only uniquely identifies entities but also locates affixed RFID tags on objects or subjects in stationary and real-time. The rapid advancement in RFID-based systems has sparked the interest of researchers in Smart Homes to employ RFID technologies and potentials to assist with optimising (non-) pervasive healthcare systems in automated homes. In this research localisation techniques and enabled positioning sensors are investigated. Passive RFID sensors are used to localise passive tags that are affixed to Smart Home objects and track the movement of individuals in stationary and real-time settings. In this study, we develop an affordable passive localisation platform using inexpensive passive RFID sensors. To fillful this aim, a passive localisation framework using minimum tracking resources (RFID sensors) has been designed. A localisation prototype and localisation application that examined the affixed RFID tag on objects to evaluate our proposed locaisation framework was then developed. Localising algorithms were utilised to achieve enhanced accuracy of localising one particular passive tag which that affixed to target objects. This thesis uses a general enough approach so that it could be applied more widely to other applications in addition to Health Smart Homes. A passive RFID localising framework is designed and developed through systematic procedures. A localising platform is built to test the proposed framework, along with developing a RFID tracking application using Java programming language and further data analysis in MATLAB. This project applies localisation procedures and evaluates them experimentally. The experimental study positively confirms that our proposed localisation framework is capable of enhancing the accuracy of the location of the tracked individual. The low-cost design uses only one passive RFID target tag, one RFID reader and three to four antennas

Minimal Infrastructure Radio Frequency Home Localisation Systems

The ability to track the location of a subject in their home allows the provision of a number of location based services, such as remote activity monitoring, context sensitive prompts and detection of safety critical situations such as falls. Such pervasive monitoring functionality offers the potential for elders to live at home for longer periods of their lives with minimal human supervision. The focus of this thesis is on the investigation and development of a home roomlevel localisation technique which can be readily deployed in a realistic home environment with minimal hardware requirements. A conveniently deployed Bluetooth ® localisation platform is designed and experimentally validated throughout the thesis. The platform adopts the convenience of a mobile phone and the processing power of a remote location calculation computer. The use of Bluetooth ® also ensures the extensibility of the platform to other home health supervision scenarios such as wireless body sensor monitoring. Central contributions of this work include the comparison of probabilistic and nonprobabilistic classifiers for location prediction accuracy and the extension of probabilistic classifiers to a Hidden Markov Model Bayesian filtering framework. New location prediction performance metrics are developed and signicant performance improvements are demonstrated with the novel extension of Hidden Markov Models to higher-order Markov movement models. With the simple probabilistic classifiers, location is correctly predicted 80% of the time. This increases to 86% with the application of the Hidden Markov Models and 88% when high-order Hidden Markov Models are employed. Further novelty is exhibited in the derivation of a real-time Hidden Markov Model Viterbi decoding algorithm which presents all the advantages of the original algorithm, while producing location estimates in real-time. Significant contributions are also made to the field of human gait-recognition by applying Bayesian filtering to the task of motion detection from accelerometers which are already present in many mobile phones. Bayesian filtering is demonstrated to enable a 35% improvement in motion recognition rate and even enables a floor recognition rate of 68% using only accelerometers. The unique application of time-varying Hidden Markov Models demonstrates the effect of integrating these freely available motion predictions on long-term location predictions

Indoor navigation for the visually impaired : enhancements through utilisation of the Internet of Things and deep learning

Wayfinding and navigation are essential aspects of independent living that heavily rely on the sense of vision. Walking in a complex building requires knowing exact location to find a suitable path to the desired destination, avoiding obstacles and monitoring orientation and movement along the route. People who do not have access to sight-dependent information, such as that provided by signage, maps and environmental cues, can encounter challenges in achieving these tasks independently. They can rely on assistance from others or maintain their independence by using assistive technologies and the resources provided by smart environments. Several solutions have adapted technological innovations to combat navigation in an indoor environment over the last few years. However, there remains a significant lack of a complete solution to aid the navigation requirements of visually impaired (VI) people. The use of a single technology cannot provide a solution to fulfil all the navigation difficulties faced. A hybrid solution using Internet of Things (IoT) devices and deep learning techniques to discern the patterns of an indoor environment may help VI people gain confidence to travel independently. This thesis aims to improve the independence and enhance the journey of VI people in an indoor setting with the proposed framework, using a smartphone. The thesis proposes a novel framework, Indoor-Nav, to provide a VI-friendly path to avoid obstacles and predict the user s position. The components include Ortho-PATH, Blue Dot for VI People (BVIP), and a deep learning-based indoor positioning model. The work establishes a novel collision-free pathfinding algorithm, Orth-PATH, to generate a VI-friendly path via sensing a grid-based indoor space. Further, to ensure correct movement, with the use of beacons and a smartphone, BVIP monitors the movements and relative position of the moving user. In dark areas without external devices, the research tests the feasibility of using sensory information from a smartphone with a pre-trained regression-based deep learning model to predict the user s absolute position. The work accomplishes a diverse range of simulations and experiments to confirm the performance and effectiveness of the proposed framework and its components. The results show that Indoor-Nav is the first type of pathfinding algorithm to provide a novel path to reflect the needs of VI people. The approach designs a path alongside walls, avoiding obstacles, and this research benchmarks the approach with other popular pathfinding algorithms. Further, this research develops a smartphone-based application to test the trajectories of a moving user in an indoor environment

A review of smartphones based indoor positioning: challenges and applications

The continual proliferation of mobile devices has encouraged much effort in using the smartphones for indoor positioning. This article is dedicated to review the most recent and interesting smartphones based indoor navigation systems, ranging from electromagnetic to inertia to visible light ones, with an emphasis on their unique challenges and potential real-world applications. A taxonomy of smartphones sensors will be introduced, which serves as the basis to categorise different positioning systems for reviewing. A set of criteria to be used for the evaluation purpose will be devised. For each sensor category, the most recent, interesting and practical systems will be examined, with detailed discussion on the open research questions for the academics, and the practicality for the potential clients

Improving the Vertical Accuracy of Indoor Positioning for Emergency Communication

The emergency communication systems are undergoing a transition from the PSTN-based legacy system to an IP-based next generation system. In the next generation system, GPS accurately provides a user's location when the user makes an emergency call outdoors using a mobile phone. Indoor positioning, however, presents a challenge because GPS does not generally work indoors. Moreover, unlike outdoors, vertical accuracy is critical indoors because an error of few meters will send emergency responders to a different floor in a building. This paper presents an indoor positioning system which focuses on improving the accuracy of vertical location. We aim to provide floor-level accuracy with minimal infrastructure support. Our approach is to use multiple sensors available in today's smartphones to trace users' vertical movements inside buildings. We make three contributions. First, we present the elevator module for tracking a user's movement in elevators. The elevator module addresses three core challenges that make it difficult to accurately derive displacement from acceleration. Second, we present the stairway module which determines the number of floors a user has traveled on foot. Unlike previous systems that track users' foot steps, our stairway module uses a novel landing counting technique. Third, we present a hybrid architecture that combines the sensor-based components with minimal and practical infrastructure. The infrastructure provides initial anchor and periodic corrections of a user's vertical location indoors. The architecture strikes the right balance between the accuracy of location and the feasibility of deployment for the purpose of emergency communication

An indoor positioning system using Bluetooth Low Energy

In this paper, we present a Bluetooth Low Energy (BLE) based indoor positioning system developed for monitoring the daily living pattern of old people (e.g. people living with dementia) or individuals with disabilities. The proposed sensing system is composed of multiple sensors that are installed in different locations in a home environment. The specific location of the user in the building has been pre-recorded into the proposed sensing system that captures the raw Received Signal Strength Indicator (RSSI) from the BLE beacon that is attached on the user. Two methods are proposed to determine the indoor location and the tracking of the users: a trilateration-based method and fingerprinting-based method. Experiments have been carried out in different home environments to verify the proposed system and methods. The results show that our system is able to accurately track the user location in home environments and can track the living patterns of the user which, in turn, may be used to infer the health status of the user. Our results also show that the positions of the BLE beacons on the user and different quality of BLE beacons do not affect the tracking accuracy