900 research outputs found
User Experience Enhancement on Smartphones using Wireless Communication Technologies
학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2020. 8. 박세웅.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
감사의 글Docto
BLE Localization using RSSI Measurements and iRingLA
International audienceOver the last few years, indoor localization has been a very dynamic research area that has drawn great attention. Many methods have been proposed for indoor positioning as well as navigation services. A big number of them were based on Radio frequency (RF) technology and Radio Signal Strength Indicator (RSSI) for their simplicity of use. The main issues of the studies conducted in this field are related to the improvement of localization factors like accuracy, computational complexity, easiness of deployment and cost. In our study, we used Bluetooth Low Energy (BLE) technology for indoor localization in the context a smart home where an elderly person can be located using an hybrid system that combines the radio, light and sound information. In this paper, we propose a model that averages the received signal strength indication (RSSI) at any the distance domain which offered accuracy down to 1 meter, depending on the deployment configuration
A new method for improving Wi-Fi based indoor positioning accuracy
Wi-Fi and smartphone based positioning technologies are play-ing a more and more important role in Location Based Service (LBS) indus-tries due to the rapid development of the smartphone market. However, the low positioning accuracy of these technologies is still an issue for indoor positioning. To address this problem, a new method for improving the in-door positioning accuracy was developed. The new method initially used the Nearest Neighbor (NN) algorithm of the fingerprinting method to iden-tify the initial position estimate of the smartphone user. Then two distance correction values in two roughly perpendicular directions were calculated by the pass loss model based on the two signal strength indicator (RSSI) values observed. The errors from the path loss model were eliminated through differencing two model-derived distances from the same access point. The new method was tested and the results were compared and as-sessed against that of the commercial Ekahau RTLS system and the NN algorithm. The preliminary results showed that the positioning accuracy has been improved consistently after the new method was applied and the root mean square accuracy was improved to 3.4 m from 3.8 m of the NN algorithm
Real-Time Localization Using Software Defined Radio
Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system
Radio Frequency-Based Indoor Localization in Ad-Hoc Networks
The increasing importance of location‐aware computing and context‐dependent information has led to a growing interest in low‐cost indoor positioning with submeter accuracy. Localization algorithms can be classified into range‐based and range‐free techniques. Additionally, localization algorithms are heavily influenced by the technology and network architecture utilized. Availability, cost, reliability and accuracy of localization are the most important parameters when selecting a localization method. In this chapter, we introduce basic localization techniques, discuss how they are implemented with radio frequency devices and then characterize the localization techniques based on the network architecture, utilized technologies and application of localization. We then investigate and address localization in indoor environments where the absence of global positioning system (GPS) and the presence of unique radio propagation properties make this problem one of the most challenging topics of localization in wireless networks. In particular, we study and review the previous work for indoor localization based on radio frequency (RF) signaling (like Bluetooth‐based localization) to illustrate localization challenges and how some of them can be overcome
Alzheimer Patient Tracking System in Indoor Wireless Environment
Alzheimer's disease is a disease of the nerves that are irreversible, resulting in memory impairment. This condition resulted in Alzheimer's patients easily lost because they forget the existence. In this research, we designed a tracking system for Alzheimer's patients in a hospital environment, incorporating Kalman method to estimate the position of the patient. As known Received Signal Strength Indicator value is strongly influenced by environmental conditions that lead to the acquisition of position estimation is inaccurate. From the test results showed that the optimal Kalman estimated value obtained when the value of R = 0:01 and Q = 0.1 with the average percentage of error only 7.01 % of the actual patient position. The test results with various data variations also indicate the reliability of the Kalman method, because of the average estimated position approach the actual patient position
Fingerprint indoor positioning based on user orientations and minimum computation time
Indoor Positioning System (IPS) has an important role in the field of Internet of Thing. IPS works based on many existing radio frequency technologies. One of the most popular methods is WLAN Fingerprint because this technology has been installed widely inside buildings and it provides a high level of accuracy. The performance is affected by people who hold mobile devices (user) and also people around the users. This research aimed to minimize the computation time of kNN searching process. The results showed that when the value of k in kNN was greater, the computation time increased, especially when using Cityblock and Minkowski distance function. The smallest average computation time was 2.14 ms, when using Cityblock. Then the computational time for Euclidean and Chebychev were relatively stable, i.e. 2.2 ms and 2.23 ms, respectively
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