A mixed approach to similarity metric selection in affinity propagation-based WiFi fingerprinting indoor positioning

The weighted k-nearest neighbors (WkNN) algorithm is by far the most popular choice in the design of fingerprinting indoor positioning systems based on WiFi received signal strength (RSS). WkNN estimates the position of a target device by selecting k reference points (RPs) based on the similarity of their fingerprints with the measured RSS values. The position of the target device is then obtained as a weighted sum of the positions of the k RPs. Two-step WkNN positioning algorithms were recently proposed, in which RPs are divided into clusters using the affinity propagation clustering algorithm, and one representative for each cluster is selected. Only cluster representatives are then considered during the position estimation, leading to a significant computational complexity reduction compared to traditional, flat WkNN. Flat and two-step WkNN share the issue of properly selecting the similarity metric so as to guarantee good positioning accuracy: in two-step WkNN, in particular, the metric impacts three different steps in the position estimation, that is cluster formation, cluster selection and RP selection and weighting. So far, however, the only similarity metric considered in the literature was the one proposed in the original formulation of the affinity propagation algorithm. This paper fills this gap by comparing different metrics and, based on this comparison, proposes a novel mixed approach in which different metrics are adopted in the different steps of the position estimation procedure. The analysis is supported by an extensive experimental campaign carried out in a multi-floor 3D indoor positioning testbed. The impact of similarity metrics and their combinations on the structure and size of the resulting clusters, 3D positioning accuracy and computational complexity are investigated. Results show that the adoption of metrics different from the one proposed in the original affinity propagation algorithm and, in particular, the combination of different metrics can significantly improve the positioning accuracy while preserving the efficiency in computational complexity typical of two-step algorithms

AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information

With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches

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

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

ViFi: virtual fingerprinting WiFi-based indoor positioning via multi-wall multi-floor propagation model

Widespread adoption of indoor positioning systems based on WiFi fingerprinting is at present hindered by the large efforts required for measurements collection during the offline phase. Two approaches were recently proposed to address such issue: crowdsourcing and RSS radiomap prediction, based on either interpolation or propagation channel model fitting from a small set of measurements. RSS prediction promises better positioning accuracy when compared to crowdsourcing, but no systematic analysis of the impact of system parameters on positioning accuracy is available. This paper fills this gap by introducing ViFi, an indoor positioning system that relies on RSS prediction based on Multi-Wall Multi-Floor (MWMF) propagation model to generate a discrete RSS radiomap (virtual fingerprints). Extensive experimental results, obtained in multiple independent testbeds, show that ViFi outperforms virtual fingerprinting systems adopting simpler propagation models in terms of accuracy, and allows a sevenfold reduction in the number of measurements to be collected, while achieving the same accuracy of a traditional fingerprinting system deployed in the same environment. Finally, a set of guidelines for the implementation of ViFi in a generic environment, that saves the effort of collecting additional measurements for system testing and fine tuning, is proposed

Sensors and Systems for Indoor Positioning

This reprint is a reprint of the articles that appeared in Sensors' (MDPI) Special Issue on “Sensors and Systems for Indoor Positioning". The published original contributions focused on systems and technologies to enable indoor applications

Pushing the Limits of Indoor Localization in Today’s Wi-Fi Networks

Wireless networks are ubiquitous nowadays and play an increasingly important role in our everyday lives. Many emerging applications including augmented reality, indoor navigation and human tracking, rely heavily on Wi-Fi, thus requiring an even more sophisticated network. One key component for the success of these applications is accurate localization. While we have GPS in the outdoor environment, indoor localization at a sub-meter granularity remains challenging due to a number of factors, including the presence of strong wireless multipath reflections indoors and the burden of deploying and maintaining any additional location service infrastructure. On the other hand, Wi-Fi technology has developed significantly in the last 15 years evolving from 802.11b/a/g to the latest 802.11n and 802.11ac standards. Single user multiple-input, multiple-output (SU-MIMO) technology has been adopted in 802.11n while multi-user MIMO is introduced in 802.11ac to increase throughput. In Wi-Fi’s development, one interesting trend is the increasing number of antennas attached to a single access point (AP). Another trend is the presence of frequency-agile radios and larger bandwidths in the latest 802.11n/ac standards. These opportunities can be leveraged to increase the accuracy of indoor wireless localization significantly in the two systems proposed in this thesis: ArrayTrack employs multi-antenna APs for angle-of-arrival (AoA) information to localize clients accurately indoors. It is the first indoor Wi-Fi localization system able to achieve below half meter median accuracy. Innovative multipath identification scheme is proposed to handle the challenging multipath issue in indoor environment. ArrayTrack is robust in term of signal to noise ratio, collision and device orientation. ArrayTrack does not require any offline training and the computational load is small, making it a great candidate for real-time location services. With six 8-antenna APs, ArrayTrack is able to achieve a median error of 23 cm indoors in the presence of strong multipath reflections in a typical office environment. ToneTrack is a fine-grained indoor localization system employing time difference of arrival scheme (TDoA). ToneTrack uses a novel channel combination algorithm to increase effective bandwidth without increasing the radio’s sampling rate, for higher resolution time of arrival (ToA) information. A new spectrum identification scheme is proposed to retrieve useful information from a ToA profile even when the overall profile is mostly inaccurate. The triangle inequality property is then applied to detect and discard the APs whose direct path is 100% blocked. With a combination of only three 20 MHz channels in the 2.4 GHz band, ToneTrack is able to achieve below one meter median error, outperforming the traditional super-resolution ToA schemes significantly

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

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