• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.4
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• pp.999-1004
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• 2010

Most of campus vehicle management systems, so far, simply manages coming in or go out of vehicles, issuing a parking tickets. Recently some of them use RFID tags to count total numbers of cars in the campus, excluding exact parking position management. In this paper we propose a new campus vehicle management system using wireless sensor network location management scheme. This system adopts RSSI based location management method with some performance improvement technique. According to the experimental result, this proposed scheme can be used to implement an effective campus vehicle management system.

• ETRI Journal
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• v.40 no.1
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• pp.122-132
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• 2018

This paper proposes a hierarchical dual filtering (HDF) algorithm to estimate the spatial region between a Cloud of Things (CoT) gateway and an Internet of Things (IoT) device. The accuracy of the spatial region estimation is important for autonomous CoT clustering. We conduct spatial region estimation using a hidden Markov model (HMM) with a raw Bluetooth received signal strength indicator (RSSI). However, the accuracy of the region estimation using the validation data is only 53.8%. To increase the accuracy of the spatial region estimation, the HDF algorithm removes the high-frequency signals hierarchically, and alters the parameters according to whether the IoT device moves. The accuracy of spatial region estimation using a raw RSSI, Kalman filter, and HDF are compared to evaluate the effectiveness of the HDF algorithm. The success rate and root mean square error (RMSE) of all regions are 0.538, 0.622, and 0.75, and 0.997, 0.812, and 0.5 when raw RSSI, a Kalman filter, and HDF are used, respectively. The HDF algorithm attains the best results in terms of the success rate and RMSE of spatial region estimation using HMM.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.10A
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• pp.1473-1479
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• 2000

This paper presents the performance evaluation of an adaptive QAM scheme under flat and frequency selective fading channels for indoor wireless communication systems. The QAM modulation is combined with differential encoding and the demodulation process is carried out noncoherently. The adaptation is performed by varying the modulation level of QAM, depending upon received signal strength. The adaptation mechanism allows a 2- or 3-bit increase or decrease at a time, if the channel condition is considered to be significantly good or bad. Simulation results show that the average number of bits per symbol (ABPS) for each symbol block transmitted over a flat fading channel is higher than 5.0 and the BER performance is better than 10^-4 for a SNR value higher than 30 dB. For frequency selective fading channels, an oversampling technique in the receiver was employed. The BER performance obtained for frequency selective fading channels is better than 10^-4 with a SNR value of 40 dB and ABPS is found to be approximately 5.5. Therefore, this scheme is very useful in that it provides both very high bandwidth efficiency and acceptable performance with moderate SNR values over flat and frequency selective fading channels. In addition, this scheme provides reduced receiver complexity by way of noncoherent detection.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.4B
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• pp.386-390
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• 2011

Modem warfares have changed from PCW (platform Centric Warfare) to NCW (Network Centric Warfare). Therefore, it is more important to operate and manage the network. This paper proposed a node management scheme in military wireless networks. In military wireless networks, nodes can join and leave the networks easily. It causes a degradation of network capacity. This paper figured out a problem caused by node which is leaving the network. This paper proposed a RSSI based method of estimating and detecting the leaving nodes in the networks. Finally, an experimental result was demonstrated to show the efficiency of the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5A
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• pp.339-347
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• 2012

Femtocells as home base station for indoor coverage extension and wideband data service, have been studied with significant interests. When femtocell is deployed, the existing cell structural of changes causes various technical problems. In this paper, we investigate the femto-macro cell interference mitigation in OFDMA system. We propose dynamic downlink resource management scheme which adjust the transmitted power of femtocell according to the strength of received macrocell signal and allocates subcarrier to femtocells in a dynamic manner. In this way, the interference between the macrocell users and femtocells is reduced. The simulation results show that proposed scheme enhances both macrocell and femtocell throughputs.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.947-949
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• 2006

In the microcell or picocell-based system the frequent movements of mobiles bring about excessive traffics into the networks. In this paper we study multi-criteria decision making which can increase estimation accuracy by considering other multiple decision parameters than the received signal strength.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.541-556
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• 2020

Wireless networks have become integral to society as they provide mobility and scalability advantages. However, their disadvantage is that they cannot control the media, which makes them vulnerable to various types of attacks. One example of such attacks is the evil twin access point (AP) attack, in which an authorized AP is impersonated by mimicking its service set identifier (SSID) and media access control (MAC) address. Evil twin APs are a major source of deception in wireless networks, facilitating message forgery and eavesdropping. Hence, it is necessary to detect them rapidly. To this end, numerous methods using clock skew have been proposed for evil twin AP detection. However, clock skew is difficult to calculate precisely because wireless networks are vulnerable to noise. This paper proposes an evil twin AP detection method that uses a multiple-feature-based machine learning classification algorithm. The features used in the proposed method are clock skew, channel, received signal strength, and duration. The results of experiments conducted indicate that the proposed method has an evil twin AP detection accuracy of 100% using the random forest algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3889-3903
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• 2017

Fast and accurate localization of randomly deployed nodes is required by many applications in wireless sensor networks (WSNs). However, mobile nodes localization in WSNs is more difficult than static nodes localization since the nodes mobility brings more data. In this paper, we propose a Range-based Monte Carlo Box (RMCB) algorithm, which builds upon the Monte Carlo Localization Boxed (MCB) algorithm to improve the localization accuracy. This algorithm utilizes Received Signal Strength Indication (RSSI) ranging technique to build a sample box and adds a preset error coefficient in sampling and filtering phase to increase the success rate of sampling and accuracy of valid samples. Moreover, simplified Particle Swarm Optimization (sPSO) algorithm is introduced to generate new samples and avoid constantly repeated sampling and filtering process. Simulation results denote that our proposed RMCB algorithm can reduce the location error by 24%, 14% and 14% on average compared to MCB, Range-based Monte Carlo Localization (RMCL) and RSSI Motion Prediction MCB (RMMCB) algorithm respectively and are suitable for high precision required positioning scenes.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10b
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• pp.285-290
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• 2007

본 논문에서 구현하고자 하는 유비쿼터스 컴퓨팅 응용에 필요한 위치 인식 시스템의 주 요소는 블루투스 프로토콜 스택(BlueZ)에서 제공하는 RSSI(Received Signal Strength Indicator) 값을 측정하는 블루투스 AP, 측정된 RSSI 값을 위치 인식 서버에 전달하기 위한 무선 AP 공유기 그리고, 받은 데이터로 위치 값을 측정하는 위치 인식 서버 및 Context Broker(고 수준의 상황 정보를 추론하는 서버 역할)로 이루어져있다. 전체적인 동작 시스템은 위치 값을 측정하고자 하는 이동 매제(마스터)를 중심으로 최대 여덟 개까지 네트워크가 가능한 블루투스 AP(슬레이브)장치로 구성된 피코넷(Piconet) 영역에서 삼각측량 필요에 적절한 세 개의 블루투스 AP를 RSSI값을 이용하여 분류 한 후 이동 매체의 위치를 측정한다. 그 결과로 나온 데이터는 피코넷 영역에서 가장 가까운 무선 AP 공유기를 거쳐서 위치 값을 측정하는 위치 인식 서버에 전달한 후, 그 결과 값으로 Context Broker에서 상황 정보를 추론해서 Community Manager에서 유비쿼터스 컴퓨팅 응용에 맞게 서비스를 구현한다. 또한, 위와 같은 시스템 내부 구조 된 데이터처리는 리눅스 운영체제 내에서 디바이스 드라이버와 사용자 프로그램으로 구현된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.242-243
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• 2018

무선 통신기술이 발전함에 따라 위치기반 서비스에 대한 관심 또한 증가하고 있다. 그 중 저전력 블루투스 기술을 사용한 비콘(Beacon)은 실내 위치인식이 불가능한 GPS와 달리 실내에서도 측위가 가능하여 사용성이 주목 받고 있다. 그러나 비콘으로부터 수신되는 RSSI(Received Signal Strength Indication) 값은 여러 환경요소로부터 영향을 받기 때문에 RSSI값을 기반으로 한 거리측정이 실제거리와의 오차가 크게 나타난다. 이에 따른 문제를 해결하기 위한 기존의 연구들이 존재하지만 평균적으로 10m이하의 거리에서 2m의 오차를 나타내고 있다. 본 연구에서는 RSSI의 오차를 줄이기 위해 확장 칼만 필터와 신호 안정화 필터를 사용하여 Raw Data를 전처리 한 후 산출된 Cleaned Data를 기반으로 각 거리단위에 최적화된 ANN(Artificial Neural Network)모델을 생성하여 거리를 측정하는 기법을 제안한다.