• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.71-76
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• 2020

Recently, the market of private study room continues to grow due to increased usage of private study room. However, old study room operators often have problems with cleanliness and theft, and cannot handle them in real time. Also, there is a problem of wasting power since users usually leave their desk for a long time with the lights on. Thus, in this paper, we intended to design a service which can prevent theft, clean up the user's desk, and adjust lighting system according to the user's condition based on Arduino system. Also, we tried to design a system which sends notifications from information of android-based IoT(Internet of Things), to the terminal when other people approach to the user's desk or try to steal user's item.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.67-76
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• 2024

The Internet of Things (IoT) has revolutionized communication and device operation, but it has also brought significant security challenges. IoT networks are structured into four levels: devices, networks, applications, and services, each with specific security considerations. Personal Area Networks (PANs), Local Area Networks (LANs), and Wide Area Networks (WANs) are the three types of IoT networks, each with unique security requirements. Communication protocols such as Wi-Fi and Bluetooth, commonly used in IoT networks, are susceptible to vulnerabilities and require additional security measures. Apart from physical security, authentication, encryption, software vulnerabilities, DoS attacks, data privacy, and supply chain security pose significant challenges. Ensuring the security of IoT devices and the data they exchange is crucial. This paper utilizes the Random Forest Algorithm from machine learning to detect anomalous data in IoT devices. The dataset consists of environmental data (temperature and humidity) collected from IoT sensors in Oman. The Random Forest Algorithm is implemented and trained using Python, and the accuracy and results of the model are discussed, demonstrating the effectiveness of Random Forest for detecting IoT device data anomalies.

• Journal of the Korean GEO-environmental Society
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• v.13 no.6
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• pp.91-100
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• 2012

Wireless monitoring system for the structural health evaluation has a limit to the reliability of measured response. The objective of this study is to evaluate an effective measurement range of the wireless monitoring system on the analyzed data. For the wireless monitoring system, Bluetooth and Wi-Fi are applied to datalogger-receiver and receiver-personal computer, respectively. For the model of the monopile structure response, a laboratory-scale monopile is manufactured with Mono Cast Nylon and a lateral loading is applied by hammer impacting. Strain gauges attached on the model monopile are connected with the datalogger. The distances of datalogger-receiver and receiver-personal computer are changed for the evaluation of the measurement range. Experimental results show that the receiving rates of the response remain almost constant within limited distance, while the receiving rates dramatically decrease out of effective range. In addition, the receiving rates affect on the measured natural frequencies of the model monopile. This study suggests that the effective range evaluation of the wireless monitoring system may be used for the determination of a monitoring distance to the monopile installed in the offshore wind farm.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.57-65
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• 2018

Conventional RF signal-based indoor localization techniques such as BLE or Wi-Fi based fingerprinting method show considerable localization errors even in small-scale indoor environments due to unstable received signal strength(RSS) of RF signals. Therefore, it is difficult to apply the existing RF-based fingerprinting techniques to large-scale indoor environments such as airports and department stores. In this paper, instead of RF signal we use the geomagnetic sensor signal for indoor localization, whose signal strength is more stable than RF RSS. Although similar geomagnetic field values exist in indoor space, an object movement would experience a unique sequence of the geomagnetic field signals as the movement continues. We use a deep neural network model called the recurrent neural network (RNN), which is effective in recognizing time-varying sequences of sensor data, to track the user's location and movement path. To evaluate the performance of the proposed geomagnetic field based indoor positioning system (IPS), we constructed a magnetic field map for a campus testbed of about $94m{\times}26$ dimension and trained RNN using various potential movement paths and their location data extracted from the magnetic field map. By adjusting various hyperparameters, we could achieve an average localization error of 1.20 meters in the testbed.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.2811-2830
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• 2017

In this paper, we study the problem of mobile data offloading for a network that contains multiple mobile network operators (MNOs), multiple WiFi or femtocell access points (APs) and multiple mobile users (MUs). MNOs offload their subscribed MUs' data traffic by leasing the unused Internet connection bandwidth of third party APs. We propose a combinatorial auction-based two-stage matching mechanism comprised of MU-AP matching and AP-MNO matching. The MU-AP matching is designed to match the MUs to APs in order to maximize the total offloading data traffic and achieve better MU satisfaction. Conversely, for AP-MNO matching, MNOs compete for APs' service using the Nash bargaining solution (NBS) and the Vickrey auction theories and, in turn, APs will receive monetary compensation. We demonstrated that the proposed mechanism converges to a distributed stable matching result. Numerical results demonstrate that the proposed algorithm well capture the tradeoff among the total data traffic, social welfare and the QoS of MUs compared to other schemes. Moreover, the proposed mechanism can considerably offload the total data traffic and improve the network social welfare with less computation complexity and communication overhead.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1019-1024
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• 2019

In this paper, we plan to design and manufacture a new sleeping child check system using image and PIR sensors. Therefore, it plans to implement a system that can prevent in-vehicle accidents that can occur due to presence of human and certain temperature inside the vehicle by collecting data from PIR sensors and temperature sensors with the main control device based on Arduino. In addition, the values of sensors can be linked with Bluetooth communication or Wi-Fi communication to send information to the mobile phone, and the sensor values can be printed by texting on the screen. It also plans to use raspberry pie and camera modules to build a display and smartphone streaming service inside the vehicle. Through this, the objective of this study is to prevent safety accidents in school vehicles by making experiments and research in this paper recognize 100% inside the vehicle with the quantitative goal of PIR sensors.

• International journal of advanced smart convergence
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• v.6 no.1
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• pp.82-88
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• 2017

Emergency lights and exit signs are an indispensable part of safety precautions for effective evacuation in case of emergency in public buildings. These emergency sign indicates safe escape routes and emergency doors, using an internationally recognizable sign. However visibility of those signs drops drastically in case of emergency situations like fire smoke, etc. and loss of visibility causes serious problems for safety evacuation. This paper propose a novel emergency light and exit sign built-in with Bluetooth Low Energy (BLE) Beacon to assist the emergency self-guiding evacuation using devices for crisis and emergency management to avoid panic condition inside the buildings. In this approach, the emergency light and exit sign with the BLE beacons deployed in the indoor environments and the smart devices detect their indoor positions, direction to move, and next exit sign position from beacon messages and interact with map server in the Internet / Intranet over the available LTE and/or Wi-Fi network connectivity. The map server generate an optimal emergency exit path according to the nearest emergency exit based on a novel graph generation method for less route computation for each smart device. All emergency exit path data interfaces among three system components, the emergency exit signs, map server, and smart devices, have been defined for modular implementation of our emergency evacuation system. The proposed exit sign experimental system has been deployed and evaluated in real-time building environment thoroughly and gives a good evidence that the modular design of the proposed exit sign system and a novel approach to compute emergency exit path route based on the BLE beacon message, map server, and smart devices is competitive and viable.

• Journal of Advanced Navigation Technology
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• v.18 no.6
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• pp.594-599
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• 2014

The routinized wireless devices such as smart phone have promoted to expand the use of IEEE 802.11 groups. The challenge environments of the wireless network utilizes effectively and user-oriented seamless services that handover is the most desirable issues under the wireless circumstance. In data center software defined network (SDN) has provided the flow routing to reduce costs and complexities. Flow routing has directly offered control for network administrator and has given to reduce delay for users. Under the circumstance of being short of network facilities, SDNs give the virtualization of network environments and to support out of the isolation traffic conditions. It shows that the mechanism of handover makes sure seamless services for higher density of the network infrastructure which is SDN to support network service re-configurable.