Yungeun Kim

Smartphone-Based Collaborative and Autonomous Radio Fingerprinting

Although active research has recently been conducted on received signal strength (RSS) fingerprint-based indoor localization, most of the current systems hardly overcome the costly and time-consuming offline training phase. In this paper, we propose an autonomous and collaborative RSS fingerprint collection and localization system. Mobile users track their position with inertial sensors and measure RSS from the surrounding access points. In this scenario, anonymous mobile users automatically collect data in daily life without purposefully surveying an entire building. The server progressively builds up a precise radio map as more users interact with their fingerprint data. The time drift error of inertial sensors is also compromised at run-time with the fingerprint-based localization, which runs with the collective fingerprints being currently built by the server. The proposed system has been implemented on a recent Android smartphone. The experiment results show that reasonable location accuracy is obtained with automatic fingerprinting in indoor environments.

Smartphone-based Wi-Fi pedestrian-tracking system tolerating the RSS variance problem

The Wi-Fi fingerprinting (WF) technique normally suffers from the RSS (Received Signal Strength) variance problem caused by environmental changes that are inherent in both the training and localization phases. Several calibration algorithms have been proposed but they only focus on the hardware variance problem. Moreover, smartphones were not evaluated and these are now widely used in WF systems. In this paper, we analyze various aspect of the RSS variance problem when using smartphones for WF: device type, device placement, user direction, and environmental changes over time. To overcome the RSS variance problem, we also propose a smartphone-based, indoor pedestrian-tracking system. The scheme uses the location where the maximum RSS is observed, which is preserved even though RSS varies significantly. We experimentally validate that the proposed system is robust to the RSS variance problem.

Smartphone-based Wi-Fi tracking system exploiting the RSS peak to overcome the RSS variance problem

The Wi-Fi fingerprinting (WF) technique normally suffers from the Received Signal Strength (RSS) variance problem caused by environmental changes that are inherent in both the training and localization phases. Several calibration algorithms have been proposed but they only focus on the hardware variance problem. Moreover, smartphones were not evaluated and these are now widely used in WF systems. In this paper, we analyzed various aspects of the RSS variance problem when using smartphones for WF: device type, device placement, user direction, and environmental changes over time. To overcome the RSS variance problem, we also propose a smartphone-based, indoor pedestrian-tracking system. The scheme uses the location where the maximum RSS is observed, which is preserved even though RSS varies significantly. We experimentally validate that the proposed system is tolerant to the RSS variance problem.

Crowdsensing-based Wi-Fi radio map management using a lightweight site survey

Localization based on Wi-Fi fingerprinting (WF) necessitates training the radio signals of target areas. Manual training enables good accuracy but requires service providers to conduct thorough site surveys to collect the radio signals of target areas periodically. Several systems are capable of eliminating the training phase by collecting radio signals from users, but these schemes are unable to provide location-based services until enough data are collected from the participatory users. Moreover, the accuracy of such systems is generally worse than that of systems that conduct manual training. In this paper, we propose a radio map management scheme in which the two methods are combined to achieve high accuracy with reduced management costs. The proposed scheme entails only a lightweight site survey for the construction of the initial radio map and does not necessarily require coverage of the entire area of interest. The quality of the radio map is enhanced in terms of both coverage and accuracy through user collaboration. In our system, mobile users conduct automatic war-walking with smartphone-based pedestrian dead reckoning (PDR), and to match the war-walking path to the radio map accurately, we employ a particle filter using both WF and PDR. We also consider the received signal strength variance problem caused by the device type and environmental changes. The proposed scheme is elastic since the service provider can adjust the costs required for the initial site survey depending on the quality of the crowdsensing-based radio map, which would compensate for the lack of coverage and accuracy of the initial radio map. The experiments result validates that our scheme achieves competitive accuracy and coverage in comparison with systems that conduct full site surveys.

Sensing WiFi packets in the air: practicality and implications in urban mobility monitoring

Mobile sensing systems employ various sensors in smartphones to extract human-related information. As the demand for sensing systems increases, a more effective mechanism is required to sense information about human life. In this paper, we present a systematic study on the feasibility and gaining properties of a crowdsensing system that primarily concerns sensing WiFi packets in the air. We propose that this method is effective for estimating urban mobility by using only a small number of participants. During a seven-week deployment, we collected smartphone sensor data, including approximately four million WiFi packets from more than 130,000 unique devices in a city. Our analysis of this dataset examines core issues in urban mobility monitoring, including feasibility, spatio-temporal coverage, scalability, and threats to privacy. Collectively, our findings provide valuable insights to guide the development of new mobile sensing systems for urban life monitoring.

Acoustic Sensor Network-Based Parking Lot Surveillance System

Camera-based surveillance systems are commonly installed in many parking lots as a countermeasure for parking lot accidents. Unfortunately these systems cannot effectively prevent accidents in advance but provide evidence of the accidents or crimes. This paper describes the design, implementation, and evaluation of an acoustic-sensor-network-based parking lot surveillance system. The system uses sensor nodes equipped with low-cost microphones to localize acoustic events such as car alarms or car crash sounds. Once the acoustic event is localized, the cameras are adjusted to the estimated location to monitor and record the current situation. We conducted extensive experiments in a parking lot to validate the performance of the system. The experimental results show that the system achieves reasonable accuracy and performance to localize the events in parking lots. The main contribution of our work is to have applied low-cost acoustic sensor network technologies to a real-life situation and solved many of the practical issues found in the design, development, and evaluation of the system.

A Participatory Service Platform for Indoor Location-Based Services

Providing indoor location-based services is challenging due to the vast coverage required and the scalability of positioning systems. This article proposes a participatory service platform for indoor location-based services to solve such problems. In the proposed platform, a few site trainers constructed an initial database of indoor positioning locations through minimal intrusive actions. Thereafter, crowd users of indoor positioning services opportunistically contributed sensing data to improve service quality. The proposed service platform provides an autonomous site-training tool for site trainers and a sample application for continuous contributions by individuals. This architecture takes advantage of crowdsourcing-based service construction and offloads the service costs.

Adaptive duty cycling for place-centric mobility monitoring using zero-cost information in smartphone

Smartphones enable the collection of mobility data using various sensors. The key challenge in the collection of continuous data is to overcome the limited battery capacity of the device. While extensive research has been conducted to solve energy issues in continuous mobility learning, we argue that previous works have not reached optimal performance. In this paper, we propose an energy-efficient mobility monitoring system, FreeTrack, to collect place-centric mobility data with minimum energy consumption in everyday life. We first analyzed the regularity of life patterns, cellular connection patterns, and battery charging behaviors of 94 smartphone users to examine important features related to human mobility. Based on our findings, we design an adaptive duty cycling scheme that uses zero-cost information (i.e., regular mobility, cell connection, and battery state) as low-level sensing to infer location change without the need to activate sensors. We model the location inference on the Hidden Markov Model and optimize the sensing schedule of individual smartphones for real-time operation. Our extensive experiment with 48 smartphone users shows that the proposed system achieves an energy saving of about 68% over previous works, yet still correctly traces 97% of mobility with 0.2±0.5 places misses in a day.

Root canal length measurement in teeth with electrolyte compensation

Electronic root canal length measurement devices have made it easier and faster to measure the root canal length of a tooth compared with the conventional radiographic method. Of these electronic apex locators, the frequency-dependent type features greater accuracy and convenience in operation. However, its accuracy is still influenced by the presence of blood and/or the various electrolytes used in root canal therapy. This study describes the development of a new frequency-dependent electronic apex locator featuring electrolyte compensation, utilising an impedance ratio and voltage difference technique to minimise the influence of electrolytes on the accuracy of root canal length measurement. The errors for distances from file tips to apical constrictions were determined in vivo with the device operating with electrolyte compensation. The measured lengths were compared with the true lengths of the extracted teeth determined using a microscope. The mean error was +0.14±0.27 mm, and 95.2% of the measurements were within the clinical tolerance of ±0.5 mm. It was also found that the degree of accuracy was not dependent on the size of the apical foramen (p=0.74).

MRI: Model-Based Radio Interpolation for Indoor War-Walking

Location estimation methods using radio fingerprint have been studied extensively. The approach constructs a database that associates ambient radio signals with physical locations in training phase, and then estimates the location by finding the most similar signal pattern within the database. To achieve robust and accurate location estimation, the training phase should be conducted across the entire target space. In practice, however, a user may only access limited or authorized places in a building, that causes degradation in accuracy. In this paper, we present a smartphone-based autonomous indoor war-walking scheme, which automatically constructs the location fingerprint database, even covering unvisited locations. While a smartphone user explores the target area, the proposed system tracks the users trajectory and simultaneously trains the location fingerprint database. Furthermore, our scheme interpolates radio signals in the database with an appropriate radio propagation model, and supplements fingerprints for unvisited places. As a result, although a user may sparsely explore the target site, the scheme returns the complete database. We implemented our solution and demonstrated the feasibility of the solution.