2,021 research outputs found

    User Experience Enhancement on Smartphones using Wireless Communication Technologies

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    í•™ìœ„ë…ŒëŹž (ë°•ì‚Ź) -- 서욞대학ꔐ 대학원 : êł”êłŒëŒ€í•™ ì „êž°Â·ì •ëłŽêł”í•™ë¶€, 2020. 8. ë°•ì„žì›….Recently, various sensors as well as wireless communication technologies such as Wi-Fi and Bluetooth Low Energy (BLE) have been equipped with smartphones. In addition, in many cases, users use a smartphone while on the move, so if a wireless communication technologies and various sensors are used for a mobile user, a better user experience can be provided. For example, when a user moves while using Wi-Fi, the user experience can be improved by providing a seamless Wi-Fi service. In addition, it is possible to provide a special service such as indoor positioning or navigation by estimating the users mobility in an indoor environment, and additional services such as location-based advertising and payment systems can also be provided. Therefore, improving the user experience by using wireless communication technology and smartphones sensors is considered to be an important research field in the future. In this dissertation, we propose three systems that can improve the user experience or convenience by usingWi-Fi, BLE, and smartphones sensors: (i) BLEND: BLE beacon-aided fast Wi-Fi handoff for smartphones, (ii) PYLON: Smartphone based Indoor Path Estimation and Localization without Human Intervention, (iii) FINISH: Fully-automated Indoor Navigation using Smartphones with Zero Human Assistance. First, we propose fast handoff scheme called BLEND exploiting BLE as secondary radio. We conduct detailed analysis of the sticky client problem on commercial smartphones with experiment and close examination of Android source code. We propose BLEND, which exploits BLE modules to provide smartphones with prior knowledge of the presence and information of APs operating at 2.4 and 5 GHz Wi-Fi channels. BLEND operating with only application requires no hardware and Android source code modification of smartphones.We prototype BLEND with commercial smartphones and evaluate the performance in real environment. Our measurement results demonstrate that BLEND significantly improves throughput and video bitrate by up to 61% and 111%, compared to a commercial Android application, respectively, with negligible energy overhead. Second, we design a path estimation and localization system, termed PYLON, which is plug-and-play on Android smartphones. PYLON includes a novel landmark correction scheme that leverages real doors of indoor environments consisting of floor plan mapping, door passing time detection and correction. It operates without any user intervention. PYLON relaxes some requirements for localization systems. It does not require any modifications to hardware or software of smartphones, and the initial location of WiFi APs, BLE beacons, and users. We implement PYLON on five Android smartphones and evaluate it on two office buildings with the help of three participants to prove applicability and scalability. PYLON achieves very high floor plan mapping accuracy with a low localization error. Finally, We design a fully-automated navigation system, termed FINISH, which addresses the problems of existing previous indoor navigation systems. FINISH generates the radio map of an indoor building based on the localization system to determine the initial location of the user. FINISH relaxes some requirements for current indoor navigation systems. It does not require any human assistance to provide navigation instructions. In addition, it is plug-and-play on Android smartphones. We implement FINISH on five Android smartphones and evaluate it on five floors of an office building with the help of multiple users to prove applicability and scalability. FINISH determines the location of the user with extremely high accuracy with in one step. In summary, we propose systems that enhance the users convenience and experience by utilizing wireless infrastructures such as Wi-Fi and BLE and various smartphones sensors such as accelerometer, gyroscope, and barometer equipped in smartphones. Systems are implemented on commercial smartphones to verify the performance through experiments. As a result, systems show the excellent performance that can enhance the users experience.1 Introduction 1 1.1 Motivation 1 1.2 Overview of Existing Approaches 3 1.2.1 Wi-Fi handoff for smartphones 3 1.2.2 Indoor path estimation and localization 4 1.2.3 Indoor navigation 5 1.3 Main Contributions 7 1.3.1 BLEND: BLE Beacon-aided Fast Handoff for Smartphones 7 1.3.2 PYLON: Smartphone Based Indoor Path Estimation and Localization with Human Intervention 8 1.3.3 FINISH: Fully-automated Indoor Navigation using Smartphones with Zero Human Assistance 9 1.4 Organization of Dissertation 10 2 BLEND: BLE Beacon-Aided FastWi-Fi Handoff for Smartphones 11 2.1 Introduction 11 2.2 Related Work 14 2.2.1 Wi-Fi-based Handoff 14 2.2.2 WPAN-aided AP Discovery 15 2.3 Background 16 2.3.1 Handoff Procedure in IEEE 802.11 16 2.3.2 BSS Load Element in IEEE 802.11 16 2.3.3 Bluetooth Low Energy 17 2.4 Sticky Client Problem 17 2.4.1 Sticky Client Problem of Commercial Smartphone 17 2.4.2 Cause of Sticky Client Problem 20 2.5 BLEND: Proposed Scheme 21 2.5.1 Advantages and Necessities of BLE as Secondary Low-Power Radio 21 2.5.2 Overall Architecture 22 2.5.3 AP Operation 23 2.5.4 Smartphone Operation 24 2.5.5 Verification of aTH estimation 28 2.6 Performance Evaluation 30 2.6.1 Implementation and Measurement Setup 30 2.6.2 Saturated Traffic Scenario 31 2.6.3 Video Streaming Scenario 35 2.7 Summary 38 3 PYLON: Smartphone based Indoor Path Estimation and Localization without Human Intervention 41 3.1 Introduction 41 3.2 Background and Related Work 44 3.2.1 Infrastructure-Based Localization 44 3.2.2 Fingerprint-Based Localization 45 3.2.3 Model-Based Localization 45 3.2.4 Dead Reckoning 46 3.2.5 Landmark-Based Localization 47 3.2.6 Simultaneous Localization and Mapping (SLAM) 47 3.3 System Overview 48 3.3.1 Notable RSSI Signature 49 3.3.2 Smartphone Operation 50 3.3.3 Server Operation 51 3.4 Path Estimation 52 3.4.1 Step Detection 52 3.4.2 Step Length Estimation 54 3.4.3 Walking Direction 54 3.4.4 Location Update 55 3.5 Landmark Correction Part 1: Virtual Room Generation 56 3.5.1 RSSI Stacking Difference 56 3.5.2 Virtual Room Generation 57 3.5.3 Virtual Graph Generation 59 3.5.4 Physical Graph Generation 60 3.6 Landmark Correction Part 2: From Floor Plan Mapping to Path Correction 60 3.6.1 Candidate Graph Generation 60 3.6.2 Backbone Node Mapping 62 3.6.3 Dead-end Node Mapping 65 3.6.4 Final Candidate Graph Selection 66 3.6.5 Door Passing Time Detection 68 3.6.6 Path Correction 70 3.7 Particle Filter 71 3.8 Performance Evaluation 73 3.8.1 Implementation and Measurement Setup 73 3.8.2 Step Detection Accuracy 77 3.8.3 Floor Plan Mapping Accuracy 77 3.8.4 Door Passing Time 78 3.8.5 Walking Direction and Localization Performance 81 3.8.6 Impact of WiFi AP and BLE Beacon Number 84 3.8.7 Impact of Walking Distance and Speed 84 3.8.8 Performance on Different Areas 87 3.9 Summary 87 4 FINISH: Fully-automated Indoor Navigation using Smartphones with Zero Human Assistance 91 4.1 Introduction 91 4.2 Related Work 92 4.2.1 Localization-based Navigation System 92 4.2.2 Peer-to-peer Navigation System 93 4.3 System Overview 93 4.3.1 System Architecture 93 4.3.2 An Example for Navigation 95 4.4 Level Change Detection and Floor Decision 96 4.4.1 Level Change Detection 96 4.5 Real-time navigation 97 4.5.1 Initial Floor and Location Decision 97 4.5.2 Orientation Adjustment 98 4.5.3 Shortest Path Estimation 99 4.6 Performance Evaluation 99 4.6.1 Initial Location Accuracy 99 4.6.2 Real-Time Navigation Accuracy 100 4.7 Summary 101 5 Conclusion 102 5.1 Research Contributions 102 5.2 Future Work 103 Abstract (In Korean) 118 ê°ì‚Źì˜ ꞀDocto

    A Survey on Handover Management in Mobility Architectures

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    This work presents a comprehensive and structured taxonomy of available techniques for managing the handover process in mobility architectures. Representative works from the existing literature have been divided into appropriate categories, based on their ability to support horizontal handovers, vertical handovers and multihoming. We describe approaches designed to work on the current Internet (i.e. IPv4-based networks), as well as those that have been devised for the "future" Internet (e.g. IPv6-based networks and extensions). Quantitative measures and qualitative indicators are also presented and used to evaluate and compare the examined approaches. This critical review provides some valuable guidelines and suggestions for designing and developing mobility architectures, including some practical expedients (e.g. those required in the current Internet environment), aimed to cope with the presence of NAT/firewalls and to provide support to legacy systems and several communication protocols working at the application layer

    An Indoor and Outdoor Navigation System for Visually Impaired People

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    In this paper, we present a system that allows visually impaired people to autonomously navigate in an unknown indoor and outdoor environment. The system, explicitly designed for low vision people, can be generalized to other users in an easy way. We assume that special landmarks are posed for helping the users in the localization of pre-defined paths. Our novel approach exploits the use of both the inertial sensors and the camera integrated into the smartphone as sensors. Such a navigation system can also provide direction estimates to the tracking system to the users. The success of out approach is proved both through experimental tests performed in controlled indoor environments and in real outdoor installations. A comparison with deep learning methods has been presented

    Context-awareness for mobile sensing: a survey and future directions

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    The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

    An IoT based Virtual Coaching System (VSC) for Assisting Activities of Daily Life

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    Nowadays aging of the population is becoming one of the main concerns of theworld. It is estimated that the number of people aged over 65 will increase from 461million to 2 billion in 2050. This substantial increment in the elderly population willhave significant consequences in the social and health care system. Therefore, in thecontext of Ambient Intelligence (AmI), the Ambient Assisted Living (AAL) has beenemerging as a new research area to address problems related to the aging of the population. AAL technologies based on embedded devices have demonstrated to be effectivein alleviating the social- and health-care issues related to the continuous growing of theaverage age of the population. Many smart applications, devices and systems have beendeveloped to monitor the health status of elderly, substitute them in the accomplishment of activities of the daily life (especially in presence of some impairment or disability),alert their caregivers in case of necessity and help them in recognizing risky situations.Such assistive technologies basically rely on the communication and interaction be-tween body sensors, smart environments and smart devices. However, in such contextless effort has been spent in designing smart solutions for empowering and supportingthe self-efficacy of people with neurodegenerative diseases and elderly in general. Thisthesis fills in the gap by presenting a low-cost, non intrusive, and ubiquitous VirtualCoaching System (VCS) to support people in the acquisition of new behaviors (e.g.,taking pills, drinking water, finding the right key, avoiding motor blocks) necessary tocope with needs derived from a change in their health status and a degradation of theircognitive capabilities as they age. VCS is based on the concept of extended mind intro-duced by Clark and Chalmers in 1998. They proposed the idea that objects within theenvironment function as a part of the mind. In my revisiting of the concept of extendedmind, the VCS is composed of a set of smart objects that exploit the Internet of Things(IoT) technology and machine learning-based algorithms, in order to identify the needsof the users and react accordingly. In particular, the system exploits smart tags to trans-form objects commonly used by people (e.g., pillbox, bottle of water, keys) into smartobjects, it monitors their usage according to their needs, and it incrementally guidesthem in the acquisition of new behaviors related to their needs. To implement VCS, thisthesis explores different research directions and challenges. First of all, it addresses thedefinition of a ubiquitous, non-invasive and low-cost indoor monitoring architecture byexploiting the IoT paradigm. Secondly, it deals with the necessity of developing solu-tions for implementing coaching actions and consequently monitoring human activitiesby analyzing the interaction between people and smart objects. Finally, it focuses on the design of low-cost localization systems for indoor environment, since knowing theposition of a person provides VCS with essential information to acquire information onperformed activities and to prevent risky situations. In the end, the outcomes of theseresearch directions have been integrated into a healthcare application scenario to imple-ment a wearable system that prevents freezing of gait in people affected by Parkinson\u2019sDisease

    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

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

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    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 mĂ©dia de vida nas Ășltimas dĂ©cadas, criou a necessidade de desenvolvimento de tecnologias que permitam melhorar a qualidade de vida dos idosos. No Ăąmbito da AssistĂȘncia Ă  Autonomia no DomicĂ­lio, sistemas de localização indoor tĂȘm 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 nĂŁo. Sistemas dependentes de infrastrutura necessitam de implementação e manutenção que sĂŁo muitas vezes dispendiosas. Por outro lado, a maioria das soluçÔes que nĂŁo requerem infrastrutura, dependem de apenas uma fonte de informação, sendo crucial a sua disponibilidade. Um sistema que nĂŁo consegue lidar com a falta de informação de um sensor dificilmente serĂĄ implementado em cenĂĄrios reais. Uma solução eficiente deverĂĄ assim garantir o acompanhamento contĂ­nuo 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 trajetĂłria do utilizador; som, explorando as caracterĂ­sticas dis tintas de cada divisĂŁo da casa; e Wi-Fi, responsĂĄvel pela estimativa da distĂąncia entre o ponto de acesso e o smartphone. Cada fonte sensorial, extraĂ­da dos sensores incorpora dos no dispositivo, foi, numa primeira abordagem, individualmente otimizada atravĂ©s de algoritmos de Machine Learning (incluindo Deep Learning). Como os dados das diversas fontes contĂȘm informação diferente acerca das mesmas caracterĂ­sticas do sistema, a sua fusĂŁo torna a classificação mais informada e robusta. Com este objetivo, foram implementadas trĂȘs abordagens de fusĂŁo de dados (input data, early and late fusion), fornecendo um resultado final derivado de contribuiçÔes complementares de todas as fontes de dados. Os resultados experimentais mostram que o desempenho do algoritmo desenvolvido melhorou com a inclusĂŁo de informação multi-sensor, alcançando um valor para F1- score de 81.8% na distinção entre sete divisĂ”es domĂ©sticas. Concluindo, o algoritmo de localização indoor, combinando informaçÔes de trĂȘs fontes diferentes atravĂ©s de mĂ©todos de fusĂŁo de dados, alcançou uma localização room-level e estĂĄ apto para ser aplicado num cenĂĄrio de AssistĂȘncia Ă  Autonomia no DomicĂ­lio

    Foreword by guest editors for the Special Issue on the 2013 ICUFN Conferencs

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    Jeong, S.; Rodrigues, JJPC.; Cano EscribĂĄ, JC. (2014). Foreword by guest editors for the Special Issue on the 2013 ICUFN Conferencs. Wireless Personal Communications. 78(4):1827-1831. doi:10.1007/s11277-014-2046-yS1827183178
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