632 research outputs found

    Group-In: Group Inference from Wireless Traces of Mobile Devices

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    This paper proposes Group-In, a wireless scanning system to detect static or mobile people groups in indoor or outdoor environments. Group-In collects only wireless traces from the Bluetooth-enabled mobile devices for group inference. The key problem addressed in this work is to detect not only static groups but also moving groups with a multi-phased approach based only noisy wireless Received Signal Strength Indicator (RSSIs) observed by multiple wireless scanners without localization support. We propose new centralized and decentralized schemes to process the sparse and noisy wireless data, and leverage graph-based clustering techniques for group detection from short-term and long-term aspects. Group-In provides two outcomes: 1) group detection in short time intervals such as two minutes and 2) long-term linkages such as a month. To verify the performance, we conduct two experimental studies. One consists of 27 controlled scenarios in the lab environments. The other is a real-world scenario where we place Bluetooth scanners in an office environment, and employees carry beacons for more than one month. Both the controlled and real-world experiments result in high accuracy group detection in short time intervals and sampling liberties in terms of the Jaccard index and pairwise similarity coefficient.Comment: This work has been funded by the EU Horizon 2020 Programme under Grant Agreements No. 731993 AUTOPILOT and No.871249 LOCUS projects. The content of this paper does not reflect the official opinion of the EU. Responsibility for the information and views expressed therein lies entirely with the authors. Proc. of ACM/IEEE IPSN'20, 202

    Integração de localização baseada em movimento na aplicação móvel EduPARK

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    More and more, mobile applications require precise localization solutions in a variety of environments. Although GPS is widely used as localization solution, it may present some accuracy problems in special conditions such as unfavorable weather or spaces with multiple obstructions such as public parks. For these scenarios, alternative solutions to GPS are of extreme relevance and are widely studied recently. This dissertation studies the case of EduPARK application, which is an augmented reality application that is implemented in the Infante D. Pedro park in Aveiro. Due to the poor accuracy of GPS in this park, the implementation of positioning and marker-less augmented reality functionalities presents difficulties. Existing relevant systems are analyzed, and an architecture based on pedestrian dead reckoning is proposed. The corresponding implementation is presented, which consists of a positioning solution using the sensors available in the smartphones, a step detection algorithm, a distance traveled estimator, an orientation estimator and a position estimator. For the validation of this solution, functionalities were implemented in the EduPARK application for testing purposes and usability tests performed. The results obtained show that the proposed solution can be an alternative to provide accurate positioning within the Infante D. Pedro park, thus enabling the implementation of functionalities of geocaching and marker-less augmented reality.Cada vez mais, as aplicações móveis requerem soluções de localização precisa nos mais variados ambientes. Apesar de o GPS ser amplamente usado como solução para localização, pode apresentar alguns problemas de precisão em condições especiais, como mau tempo, ou espaços com várias obstruções, como parques públicos. Para estes casos, soluções alternativas ao GPS são de extrema relevância e veem sendo desenvolvidas. A presente dissertação estuda o caso do projeto EduPARK, que é uma aplicação móvel de realidade aumentada para o parque Infante D. Pedro em Aveiro. Devido à fraca precisão do GPS nesse parque, a implementação de funcionalidades baseadas no posionamento e de realidade aumentada sem marcadores apresenta dificuldades. São analisados sistemas relevantes existentes e é proposta uma arquitetura baseada em localização de pedestres. Em seguida é apresentada a correspondente implementação, que consiste numa solução de posicionamento usando os sensores disponiveis nos smartphones, um algoritmo de deteção de passos, um estimador de distância percorrida, um estimador de orientação e um estimador de posicionamento. Para a validação desta solução, foram implementadas funcionalidades na aplicação EduPARK para fins de teste, e realizados testes com utilizadores e testes de usabilidade. Os resultados obtidos demostram que a solução proposta pode ser uma alternativa para a localização no interior do parque Infante D. Pedro, viabilizando desta forma a implementação de funcionalidades baseadas no posicionamento e de realidade aumenta sem marcadores.EduPARK é um projeto financiado por Fundos FEDER através do Programa Operacional Competitividade e Internacionalização - COMPETE 2020 e por Fundos Nacionais através da FCT - Fundação para a Ciência e a Tecnologia no âmbito do projeto POCI-01-0145-FEDER-016542.Mestrado em Engenharia Informátic

    Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

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    The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future
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