• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.1
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• pp.28-33
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• 2007

In spite of the large interest in the 802.15.4 standard, no realistic experimental research of its behaviour exists to date except a few works by simulations and analytical modeling. In this paper, we have established realistic environment of IEEE 802.15.4 network and analyze its behaviour under various conditions as like 1) maximum throughput of 802.15.4 MAC, 2) MAC fairness. and 3) throughput and m rate under co-existence of 802.11 Wi-Fi wireless networks.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.1
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• pp.45-49
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• 2022

In this paper, we propose a method for estimating the error of the Ultra-Wideband (UWB) distance measurement using the channel impulse response (CIR) of the UWB signal based on machine learning. Due to the recent demand for indoor location-based services, wireless signal-based localization technologies are being studied, such as UWB, Wi-Fi, and Bluetooth. The constructive obstacles constituting the indoor environment make the distance measurement of UWB inaccurate, which lowers the indoor localization accuracy. Therefore, we apply machine learning to learn the characteristics of UWB signals and estimate the error of UWB distance measurements. In addition, the performance of the proposed algorithm is analyzed through experiments in an indoor environment composed of various walls.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.127-128
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• 2020

본 논문에서는 무선 통신(Wi-Fi)과 FireBase를 활용해 화재 발생을 알려 다수의 사람들이 대피 할 수 있는 시스템이다. 이 시스템은 아두이노를 기반으로 가스·불꽃·온도센서로 화재를 감지하고, 화재를 감지하면 무선 통신을 이용해 서버와 파이어 베이스로 전송한다. 서버로 전송된 데이터는 사용자들에게 어플리케이션의 푸시 기능으로 화재 발생 알림을 전송하고, 파이어 베이스로 전송된 데이터는 어플리케이션에서 대피경로를 알려주어 신속한 대피를 할 수 있게 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3029-3045
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• 2017

The main processes of a cognitive radio system include spectrum sensing, spectrum decision, spectrum sharing, and spectrum conversion. Experimental results show that these stages introduce a time delay that affects the spectrum sensing accuracy, reducing its efficiency. To reduce the time delay, the frequency spectrum prediction was proposed to alleviate the burden on the spectrum sensing. In this paper, the deep recurrent neural network (DRNN) was proposed to predict the spectrum of multiple time slots, since the existing methods only predict the spectrum of one time slot. The continuous state of a channel is divided into a many time slots, forming a time series of the channel state. Since there are more hidden layers in the DRNN than in the RNN, the DRNN has fading memory in its bottom layer as well as in the past input. In addition, the extended Kalman filter was used to train the DRNN, which overcomes the problem of slow convergence and the vanishing gradient of the gradient descent method. The spectrum prediction based on the DRNN was verified with a WiFi signal, and the error of the prediction was analyzed. The simulation results proved that the multiple slot spectrum prediction improved the spectrum efficiency and reduced the energy consumption of spectrum sensing.

• Journal of Korea Multimedia Society
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• v.19 no.5
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• pp.901-909
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• 2016

Unprecedented attention is being given to Smart Grid, Micro Grid and Internet of Things (IoT) in the Republic of Korea recently but such systems' effect is not well experienced by the market since they require additional and costly reforms for the existing household electrical system where adaptive communication platforms are needed. As such platforms, both wireless and wire communication technologies are being considered at the moment. Usually, they include WiFi, Zigbee technologies and the latter, LAN technology. However, communication speed decline due to signal attenuation and interference during wireless communications are considered to be the major problem and the extra works involving time and costs for the LAN system construction can be another demerit. Therefore, in this paper, we have introduced a Power Line Communication-based Smart Outlet System Expandable at Home to complement these disadvantages. Proposed IoT system involves Power Line Communication (PLC) technology which is essential to constructing a Smart Grid.

• Journal of Korea Multimedia Society
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• v.19 no.12
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• pp.2014-2023
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• 2016

Web presence is one of the key issues for extensive deployment of Internet-of-Things (IoT). An obstacle to overcome for Web presence is relatively low computing power of IoT devices. In this paper, we present implementation of an IoT platform based on Constrained Application Protocol (CoAP) which is a web transfer protocol proposed by Internet Engineering Task Force (IETF) for the low performance IoT devices such as Wireless Sensor Network (WSN) nodes and micro-controllers. To qualify the performance of CoAP-based IoT system for such an application as smart grid, we designed a test platform consisting of Raspberry Pi2, Kmote WSN node and a desktop PC. Using open source softwares, CoAP was implemented on top of the platform. Leveraging the GET command defined at CoAP specification, performance of the system was measured in terms of round-trip time (RTT) from web application to the Kmote sensor node. To investigate abnormal cases among the test results, hop-by-hop delays were measured to analyze resulting data. The average response time of CoAP-based communication except the abnormal data was reduced by 23% smaller than the previous research result.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2377-2397
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• 2020

Human activity recognition is widely used in smart homes, health care and indoor monitor. Traditional approaches all need hardware installation or wearable sensors, which incurs additional costs and imposes many restrictions on usage. Therefore, this paper presents a novel device-free activities recognition system based on the advanced wireless technologies. The fine-grained information channel state information (CSI) in the wireless channel is employed as the indicator of human activities. To improve accuracy, both amplitude and phase information of CSI are extracted and shaped into feature vectors for activities recognition. In addition, we discuss the classification accuracy of different features and select the most stable features for feature matrix. Our experimental evaluation in two laboratories of different size demonstrates that the proposed scheme can achieve an average accuracy over 95% and 90% in different scenarios.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.3
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• pp.11-20
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• 2018

Smart devices refer to various devices and control equipment such as health care devices, imaging devices, motor devices and wearable devices that use wireless network communication (e.g., Wi-fi, Bluetooth, LTE). Commercial services using such devices are found in a wide range of fields, including home networks, health care and medical services, entertainment and toys. Studies on smart devices have also been actively undertaken by academia and industry alike, as the penetration rate of smartphones grew and the technological progress made with the fourth industrial revolution bring about great convenience for users. While services offered through smart devices come with convenience, there is also various security threats that can lead to financial loss or even a loss of life in the case of terrorist attacks. As attacks that are committed through smart devices tend to pick up where attacks based on wireless internet left off, more research is needed on related security topics. As such, this paper seeks to design an access control method for reinforced security for smart devices. After registering and authenticating the smart device from the user's smart phone and service provider, a safe communication protocol is designed. Then to secure the integrity and confidentiality of the communication data, a management process such as for device renewal or cancellation is designed. Safety and security of the existing systems against attacks are also evaluated. In doing so, an improved efficiency by approximately 44% compared to the encryption processing speed of the existing system was verified.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.334-349
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• 2022

The effectiveness of Wi-Fi networks is greatly influenced by the optimization of contention window (CW) parameters. Unfortunately, the conventional approach employed by IEEE 802.11 wireless networks is not scalable enough to sustain consistent performance for the increasing number of stations. Yet, it is still the default when accessing channels for single-users of 802.11 transmissions. Recently, there has been a spike in attempts to enhance network performance using a machine learning (ML) technique known as reinforcement learning (RL). Its advantage is interacting with the surrounding environment and making decisions based on its own experience. Deep RL (DRL) uses deep neural networks (DNN) to deal with more complex environments (such as continuous state spaces or actions spaces) and to get optimum rewards. As a result, we present a new approach of CW control mechanism, which is termed as contention window threshold (CW_Threshold). It uses the DRL principle to define the threshold value and learn optimal settings under various network scenarios. We demonstrate our proposed method, known as a smart exponential-threshold-linear backoff algorithm with a deep Q-learning network (SETL-DQN). The simulation results show that our proposed SETL-DQN algorithm can effectively improve the throughput and reduce the collision rates.

• Journal of IKEEE
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• v.26 no.1
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• pp.62-66
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• 2022

Recently, there have been many research efforts based on data-based deep learning technologies to deal with the interference problem between heterogeneous wireless communication devices in unlicensed frequency bands. However, existing approaches are commonly based on the use of complex neural network models, which require high computational power, limiting their efficiency in resource-constrained network interfaces and Internet of Things (IoT) devices. In this study, we address the problem of classifying heterogeneous wireless technologies including Wi-Fi and ZigBee in unlicensed spectrum bands. We focus on a data-driven approach that employs a supervised-learning method that uses received signal strength indicator (RSSI) data to train Deep Convolutional Neural Networks (CNNs). We propose a simple measurement methodology for collecting RSSI training data which preserves temporal and spectral properties of the target signal. Real experimental results using an open-source 2.4 GHz wireless development platform Ubertooth show that the proposed sampling method maintains the same accuracy with only a 10% level of sampling data for the same neural network architecture.