• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.1250-1264
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• 2017

This paper presents the architecture design, implement, experimental validation of a bistatic backscatter wireless communication system in Wi-Fi network. The operating principle is to communicate a tag's data by detecting the power level of the power modulated Wi-Fi packets to be reflected or absorbed by backscatter tag, in interconnecting with Wi-Fi device and Wi-Fi AP. This system is able to provide the identification and sensor data of tag on the internet connectivity without requiring extra device for reading data, because this uses an existing Wi-Fi AP infrastructure. The backscatter tag consists of Wi-Fi energy harvesting part and a backscatter transmitter/a power-detecting receiver part. This tag can operate by harvesting and generating energy from Wi-Fi signal power. Wi-Fi device decodes information of the tag data by recognizing the power level of the backscattered Wi-Fi packets. Wi-Fi device receives the backscattered Wi-Fi packets and generates the tag's data pattern in the time-series of channel state information (CSI) values. We believe that this system can be achieved wireless connectivity for ultra- low-power IoT and wearable device.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.70-75
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• 2022

Accurately measuring location is necessary to provide a variety of services. The data for indoor positioning measures the RSSI values from the WiFi device through an application of a smartphone. The measured data becomes the raw data of machine learning. The feature data is the measured RSSI value, and the label is the name of the space for the measured position. For this purpose, the machine learning technique is to study a technique that predicts the exact location only with the WiFi signal by applying an efficient technique to classification. Ensemble is a technique for obtaining more accurate predictions through various models than one model, including backing and boosting. Among them, Boosting is a technique for adjusting the weight of a model through a modeling result based on sampled data, and there are various algorithms. This study uses Xgboost among the above techniques and evaluates performance with other ensemble techniques.

• Journal of Satellite, Information and Communications
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• v.7 no.2
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• pp.104-109
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• 2012

Currently, GPS(Global Positioning System) is used to find user location information. However, in some cases, especially in urban environments, we receive unreliable location information deu to multipath fading. In order to resolve this problem, we propose a closely coupled positioning technique where GPS signal is combined with QZSS signal. Also we proposed and analyze a combining algorithm of GNSS and Wi-Fi signals to get closely coupled location information by referring AP information. Finally, this paper proposes a combined GPS/QZSS/Wi-Fi navigation algorithm to improve navigation performance, and it is verified by testing of car deriving according to availability and accuracy standard.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.1951-1972
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• 2018

Wi-Fi Access Point (AP) optimization approaches are used in indoor positioning systems for signal coverage enhancement, as well as positioning precision improvement. Although the huge power consumption of the AP optimization forms a serious problem due to the signal coverage requirement for large-scale indoor environment, the conventional approaches treat the problem of power consumption independent from the design of indoor positioning systems. This paper proposes a new Fast Water-filling algorithm Group Power Constraint (FWA-GPC) based Wi-Fi AP optimization approach for indoor positioning in which the power consumed by the AP optimization is significantly considered. This paper has three contributions. First, it is not restricted to conventional concept of one AP for one candidate AP location, but considered spare APs once the active APs break off. Second, it utilizes the concept of water-filling model from adaptive channel power allocation to calculate the number of APs for each candidate AP location by maximizing the location fingerprint discrimination. Third, it uses a fast version, namely Fast Water-filling algorithm, to search for the optimal solution efficiently. The experimental results conducted in two typical indoor Wi-Fi environments prove that the proposed FWA-GPC performs better than the conventional AP optimization approaches.

• The KIPS Transactions:PartB
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• v.19B no.3
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• pp.177-182
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• 2012

We propose effective motion and observation models for the position of a WiFi-equipped smartphone user in large indoor environments. Three component motion models provide better proposal distribution of the pedestrian's motion. Our Gaussian interpolation-based observation model can generate likelihoods at locations for which no calibration data is available. These models being incorporated into the particle filter framework, our WiFi fingerprint-based localization algorithm can track the position of a smartphone user accurately in large indoor environments. Experiments carried with an Android smartphone in a multi-story building illustrate the performance of our WiFi localization algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.507-510
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• 2011

As the amount of the wireless mobile products like a 'Smart phone' used increases, the studies about the Location Based Service (LBS) is highly increasing. Outdoor location determination can use the GPS which is built-in in the wireless mobile products. However, it is not possible to use GPS inside the huge cruise bigger than a normal building, it is regarded to consider Indoor location determination which is appropriate at the inside environment. Wi-Fi (Wireless Fidelity) does not need an extra installation process because it is already installed here and there inside the building. In this respect, Wi-Fi has low price competitiveness compared to other wireless sensor products. In this paper, I will introduce 'Bayesian Algorithm' which can recognize useful space with Wi-Fi signal.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.3
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• pp.271-280
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• 2023

Pedestrian Dead Reckoning (PDR) methods using initial sensors are being studied to provide the location information of smart device users in indoor environments where satellite signals are not available. PDR can continuously estimate the location of a pedestrian regardless of the walking environment, but has the disadvantage of accumulating errors over time. Unlike this, WiFi signal-based wireless positioning technology does not accumulate errors over time, but can provide positioning information only where infrastructure is installed. It also shows different positioning performance depending on the environment. In this paper, an integrated positioning technology integrating two positioning techniques with different error characteristics is proposed. A technique for correcting the error of PDR was designed by using the location information obtained through WiFi Measurement-based fingerprinting as the measurement of Extended Kalman Filte (EKF). Here, a technique is used to variably calculate the error covariance of the filter measurements using the WiFi Fingerprinting DB and apply it to the filter. The performance of the proposed positioning technology is verified through an experiment. The error characteristics of the PDR and WiFi Fingerprinting techniques are analyzed through the experimental results. In addition, it is confirmed that the PDR error is effectively compensated by adaptively utilizing the WiFi signal to the environment through the EKF to which the adaptive error covariance proposed in this paper is applied.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.37-42
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• 2023

Wi-Fi Received Signal Strength Indicator (RSSI) is considered one of the most important sensor data types for indoor localization. However, collecting a RSSI fingerprint, which consists of pairs of a RSSI measurement set and a corresponding location, is costly and time-consuming. In this paper, we propose a Wi-Fi RSSI learning technique without true location data to overcome the limitations of static database construction. Instead of the true reference positions, inertial measurement unit (IMU) data are used to generate pseudo locations, which enable a trainer to move during data collection. This improves the efficiency of data collection dramatically. From an experiment it is seen that the proposed algorithm successfully learns the unsupervised Wi-Fi RSSI positioning model, resulting in 2 m accuracy when the cumulative distribution function (CDF) is 0.8.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.6
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• pp.531-539
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• 2013

Fingerprinting technique uses the radio signal strength measured reference locations is typically used, although there are many Wi-Fi based location tracking techniques. However, it needs numerous reference locations for precision and accuracy. This paper the analyzes problems of previous techniques and proposes a fingerprinting system using reliability based on a signal strength map. The system collects the signal strength data from a number of reference locations designated by the developer. And then it generates path-loss models to one of the access points for each reference location. These models calculate the predicted signal strength and reliability for a lattice. To evaluate proposed method and system performance, We perform experiments in a $20m{\times}22m$ real indoor environment installed access points. According to the result, the proposed system reduced distance error than RADAR. Comparing the existing system, it reduced about 1.74m.