• KIPS Transactions on Computer and Communication Systems
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• v.6 no.3
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• pp.113-120
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• 2017

In recent years, indoor localization has been researched for the improvement of its localization accuracy capability in Wi-Fi environment. The fingerprint and RF propagation models has been the main approach in determining indoor positioning. With the use of fingerprint, a low-cost, versatile localization system can be achieved without the use of external hardware. However, only a few research have been made on virtual access points (VAPs) among indoor localization models. In this paper, the idea of indoor localization system using fingerprint with the addition of VAP in Wi-Fi environment is discussed. The idea is to virtually add APs in the existing indoor Wi-Fi system, this would mean additional virtually APs in the network. The experiments of the proposed algorithm shows the positive results when 2VAPs are used compared with only APs. A combination of 3APs and 2VAPs in the 3rd case had the lowest average error of 3.99 among its 4 scenarios.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.319-322
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• 2019

There are many techniques for locating users in an indoor spot. Among them, WiFi fingerprinting technique which is widely used is phased into a data collection step and a positioning step. In the data collection step, all surrounding Wi-Fi signals are collected and managed as a list. The more data collected, the better the accuracy of the indoor position based on Wi-Fi fingerprint. Existing high-quality data collection and management methods are time consuming and costly, and many operations are required to extract and generate data necessary for machine learning. Therefore, we research how to collect and manage large amount of data in limited resources. This paper presents efficient data collection methods and data generation for learning.

• Journal of Internet of Things and Convergence
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• v.2 no.4
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• pp.35-39
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• 2016

This paper is about IoT(Internet of Things)-based indoor localization scheme. GPS and WiFi are widely used to estimate the location of things. However, GPS has drawback of poor reception and radio disturbance in doors. To estimate the location in WiFi-based method, the user collects the information by scanning nearby WiFi(s) and transferring the information to WiFi database server. This is a fingerprint method with disadvantage of having an additional DB server. IoT is the internetworking of things, and this is on rapid rise. I propose the IoT-based indoor localization scheme. Under the proposed method, a device internetworking with another device with its own location information like GPS coordinate can estimate its own location through RSSI. With more devices localizing its own, the localization accuracy goes high. The proposed method allows the user to estimate the location without GPS and WiFi DB server.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.113-118
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• 2023

The WiFi fingerprint method for location estimation within the home has the advantage of using the existing infrastructure and estimating absolute coordinates, so many studies are being conducted. Existing studies have mainly focused on the study of localization algorithms, but the improvement of accuracy has reached its limits. However, since a wireless LAN receiver such as a smartphone cannot measure signals smaller than the reception sensitivity of radio signals, the position estimation error varies depending on the method of processing these values. In this paper, we proposed a method to increase the location estimation accuracy by pre-processing the received signal data of the measured wireless LAN router in various ways and applying it to the existing algorithm, and greatly improved accuracy was obtained. In addition, the preprocessed data was applied to the KNN method and the CNN method and the performance was compared.

• Journal of IKEEE
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• v.25 no.2
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• pp.252-260
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• 2021

In this paper, a method of using Software Defined Radio (SDR) is proposed for improving the accuracy of identifying two kinds of signals as Wireless Fidelity (Wi-Fi) signal and Bluetooth signal at the same frequency band of 2.4 GHz based on the time-domain signal characteristic. An SDR device was set up for collecting transmitting signals from Wi-Fi access points (Wi-Fi) and mobile phones (Bluetooth). Different characteristics between Wi-Fi and Bluetooth signals were extracted from the measured result. The SDR device is programmed with a Wi-Fi and Bluetooth detection algorithm and a collision detection algorithm to detect and verify the Wi-Fi and Bluetooth signals based on collected IQ data. These methods are necessary for some applications like wireless communication optimization, Wi-Fi fingerprint localization, which helps to avoid interference and collision between two kinds of signals.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.11
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• pp.1018-1026
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• 2012

This paper presents a developing method of smart fingerprint recognition system. First, we design a hardware configuration circuit using a 32bit Risc CPU, a fingerprint sensor, a LCD, and a WiFi communication chip to realize the smart fingerprint recognition systems. It is necessary to develop a JNI (Java Native Interface) library and a device drive program of fingerprint sense to develop application program of fingerprint recognition system with Android platform. Thus second, we develop a device drive and a JNI program. And we also develop an application program of fingerprint recognition systems using developed JNI library. Finally test results are presented to illustrate the performance of the developed smart fingerprint recognition system.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.73-75
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• 2018

LoRaWan(Long Range Wide Area Network)은 저전력, 장거리 특성을 가진 무선 통신기술로 그 특성상 스마트 시티(Smart City), IoT(Internet of Things) 등에 각광받고 있다. 또한 LoRaWan은 Chirp 신호 특성에 의해 실외 삼각측량에 따른 사용자 위치 추정 기술을 제공한다. 본 논문에서는 이러한 LoRaWan의 특성에 더해 Wi-Fi 지문 정보를 활용하여 위치 추정 정확도를 개선하고 또한 이웃 Wi-Fi 단말들, 가령 스마트폰 등의 위치 정보를 LoraWan 게이트웨이와 통신하여 최종적으로 서버에서 측위 할 수 있는 시스템을 제안한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.129-134
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• 2018

Research on the technique for estimating the indoor position has been actively carried out. In particular, the WiFi fingerprint method, which does not require any additional infrastructure, is being partially used because of its high economic efficiency. The KNN method which estimates similar points to the corresponding points by comparing intensity information of the WLAN reception signal measured at various points in advance with intensity information measured at a specific point in the future is simple but has a good performance. However, in the conventional KNN scheme, since the number K of average candidate positions is constant, there is a problem that the position estimation error is not optimized according to a specific point. In this paper, we proposed an algorithm that adaptively changes the K value for each point and applied it to experimental data and evaluated its performance.

• 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 Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1856-1864
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• 2015

In this paper, the localization algorithm for improving the accuracy of the positioning using the Wi-Fi fingerprint using the normal distribution probability and the built-in typed accelerometer sensor, the gyroscope sensor of smartphone in the indoor environment is proposed. The experiments for analyzing the performance of the proposed algorithm were carried out at the region of the horizontal and vertical 20m * 10m in the engineering school building of our university, and the performance of the proposed algorithm is compared with the fingerprint and the DR (dead reckoning) while user is moving according to the assigned region. As a result, the maximum error distance in the proposed algorithm was decreased to 2cm and 36cm compared with two algorithms, respectively. In addition to this, the maximum error distance was also less than compared with two algorithms as 16.64cm and 36.25cm, respectively. It can be seen that the fingerprint map searching time of the proposed algorithm was also reduced to 0.15 seconds compared with two algorithms.