• Electronics and Telecommunications Trends
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• v.29 no.6
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• pp.113-125
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• 2014

최근 모바일 단말 기술의 발전과 무선망의 성능 향상에 따른 다양한 서비스가 제공되고 있는 추세이며, 위치정보인식시스템과 결합된 서비스에 많은 관심이 높아졌다. 본고에서는 GPS(Global Positioning System)의 신호가 미치지 못하는 건물의 실내환경에 적합한 경로 안내서비스 및 지하시설물 안내 등 초정밀 실내 측위 서비스를 제공하기 위한 융합 측위 방안을 제안한다. 융합 측위 방안은 실내외 연속 측위를 위해 실외에서는 GPS를 이용하고 실내환경에서는 WLAN 기반의 측위 전용 AP(Access Point)를 이용, 전파신호의 LoS(Line of Sight)를 확보하여 측위하고 전파음영지역에서는 스마트폰의 가속도, 자이로센서 등 여러 가지 관성센서를 활용하여 PDR(Pedestrian Dead Reckoning) 알고리즘 등을 적용하여 측위한다. 또한 측위 정확도 향상 및 오차를 줄이기 위한 방법으로 LSE(Least Squire Estimation) 및 EKF(Extended Kalman Filter), KNN(K-Neighbor Node)/MSSM(Maximum Signal Strength Model) Algorithm 등 다양한 융합 측위 알고리즘을 적용하여 실내환경에 적합한 최적의 초정밀 실내 측위 서비스를 제공한다.

• Journal of Korea Multimedia Society
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• v.11 no.10
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• pp.1376-1384
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• 2008

Numerous researches on network self-configuration have been conducted on Wireless Sensor Network (WSN) and Ad Hoc network but the results have not been applied yet for factory automation. This paper presents development of intelligent process management systems conducting process monitoring and control irrelevant to physical position of a plant operator. The systems are indispensable for diagnosis of rotating machines which cannot exploit wired network. The system was implemented in a Personal Digital Assistant (PDA) using Bluetooth communication protocol. When a mobile terminal reaches to communication range of the process management server, the server detects the mobile terminal to reconfigure plant network automatically. The server authenticates a user of the terminal to download and installs monitoring and control program considering authorized level of the user. If the terminal leaves communication range of the server, it deletes the node from the network and removes the program automatically to save resources and prevent security problems such as missing terminal. Operation of developed functions was verified in a testbed emulating steel-making plant.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1469-1476
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• 2022

Recently, many researches have been conducted to estimate blood pressure using ECG(Electrocardiogram) and PPG(Photoplentysmography) signals. In this paper, we designed and implemented a mobile system to monitor blood pressure in real time by using 1-D convolutional neural networks. The proposed model consists of deep 11 layers which can learn to extract various features of ECG and PPG signals. The simulation results show that the more the number of convolutional kernels the learned neural network has, the more detailed characteristics of ECG and PPG signals resulted in better performance with reduced mean square error compared to linear regression model. With receiving measurement signals from wearable ECG and PPG sensor devices attached to the body, the developed system receives measurement data transmitted through Bluetooth communication from the devices, estimates systolic and diastolic blood pressure values using a learned model and displays its graph in real time.

• Journal of Korea Multimedia Society
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• v.13 no.8
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• pp.1221-1234
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• 2010

MANET(Mobile Ad-hoc Network)s have been researched primary at routing protocols and at the guarantees of QoS(Quality of Service) for mobile environments. Otherwise the Ubiquitous Sensor Networks (USNs) have some limitations in power energies and in processing of sensing data, as well as their network topologies are frequently changed by fading off and node failures. Thus we should redesign network protocols with concerning to energy efficiency for the USNs above all. In this paper, we focus on the protocol interface for managing for USNs based on the surveys. And then we figure the topology of USNs out and design the network protocol interface to make power saved, with data gathering and processing more efficient using our designed packet structures.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.12
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• pp.301-312
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• 2018

In industrial wireless sensor networks, the continuous object detection such as fire or toxic gas detection is one of major applications. A continuous object occurs at a specific point and then diffuses over a wide area. Therefore, many studies have focused on accurately detecting a continuous object and delivering data to a static sink with an energy-efficient way. Recently, some applications such as fire suppression require mobile sinks to provide real-time response. However, the sink mobility support in continuous object detection brings challenging issues. The existing approaches supporting sink mobility are designed for individual object detection, so they establish one-to-one communication between a source and a mobile sink for location update. But these approaches are not appropriate for a continuous object detection since a mobile sink should establish one-to-many communication with all sources. The one-to-many communication increases energy consumption and thus shortens the network lifetime. In this paper, we propose the origin-centric communication scheme to support sink mobility in a continuous object detection. Simulation results verify that the proposed scheme surpasses all the other work in terms of energy consumption.

• Journal of IKEEE
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• v.24 no.2
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• pp.523-528
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• 2020

With a widespread of sensor-rich mobile devices, the analysis of human activities becomes more general and simpler than ever before. In this paper, we propose two deep neural networks that efficiently and accurately perform human activity recognition (HAR) using tri-axial accelerometers. In combination with powerful modern deep learning techniques like batch normalization and LSTM networks, our model outperforms baseline approaches and establishes state-of-the-art results on WISDM dataset.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.93-102
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• 2023

Data processing through the cloud causes many problems, such as latency and increased communication costs in the communication process. Therefore, many researchers study edge computing in the IoT, and autonomous driving is a representative application. In indoor self-driving, unlike outdoor, GPS and traffic information cannot be used, so the surrounding environment must be recognized using sensors. An efficient autonomous driving system is required because it is a mobile environment with resource constraints. This paper proposes a machine-learning method using neural networks for autonomous driving in an indoor environment. The neural network model predicts the most appropriate driving command for the current location based on the distance data measured by the LiDAR sensor. We designed six learning models to evaluate according to the number of input data of the proposed neural networks. In addition, we made an autonomous vehicle based on Raspberry Pi for driving and learning and an indoor driving track produced for collecting data and evaluation. Finally, we compared six neural network models in terms of accuracy, response time, and battery consumption, and the effect of the number of input data on performance was confirmed.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.189-196
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• 2011

The main issues of the researches are monitoring environment such as weather or temperature variation and natural accident, and sensor gateways which have mobile device, applications for mobile health care. In this paper, we propose the SFMS(Smart Factory Management System) that can effectively monitor and manage a green smart factory area based on M2M service and smart phone with android OS platform. The proposed system is performed based on the TinyOS-based IEEE 802.15.4 protocol stack. To validate system functionality, we built sensor network environments where were equipped with four application sensors such as Temp/Hum, PIR, door, and camera sensor. We also built and tested the SFMS system to provide a novel model for event detection systems with smart phone.

• Convergence Security Journal
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• v.12 no.1
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• pp.55-61
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• 2012

Owing to the development of wireless infrastructure and mobile communication technology, There is growing interest in smart phone using it. The resulting popularity of smart phone has increased the Mobile Malicious AP-related security threat and the access to the wireless AP(Access Point) using Wi-Fi. mobile AP mechanism is the use of a mobile device with Internet access such as 3G cellular service to serve as an Internet gateway or access point for other devices. Within the enterprise, the use of mobile AP mechanism made corporate information management difficult owing to use wireless system that is impossible to wire packet monitoring. In this thesis, we propose mobile AP mechanism-based mobile malicious AP detection and prevention mechanism in radius authentication server network. Detection approach detects mobile AP mechanism-based mobile malicious AP by sniffing the beacon frame and analyzing the difference between an authorized AP and a mobile AP mechanism-based mobile malicious AP detection.

• Annual Conference of KIPS
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• 2016.04a
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• pp.116-119
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• 2016

무선 센서 망에서 모바일 싱크 그룹에게 데이터를 전송하기 위한 Mobile Geocasting에 관한 연구가 진행되었다. Mobile Geocasting에서, 싱크 그룹의 지역에 싱크들에게 데이터를 전송하기 위해 유니캐스팅과 플러딩의 두 가지 접근 방식이 있다. 에너지 효율성의 관점에서, 유니캐스팅은 분산된 적은 수의 분산된 싱크들에게 효율적인 전송 방식이고, 반면에 플러딩은 다수의 밀집된 싱크들에게 효율적인 전송 방식이다. 하지만, 이 두 가지 방식은 모두 너무 극단적으로 보수적인 방법이기 때문에, 그룹 지역 내에 싱크들이 전체적으로는 분산적이지만 부분적으로는 밀집하게 분포된 일반적인 상황들을 지원하기는 어렵다. 그러므로, 본 논문은 일반적인 분포를 가지는 이동 싱크 그룹을 효율적으로 지원하기 위한 유니캐스팅과 플러딩을 통합하는 하이브리드 방안을 제안한다. 제안 방안은 그룹 지역내에 분산된 싱크들을 위해서는 개별적 유니캐스팅과 밀집된 싱크들에게는 부분적 플러딩을 이용한다. 부분적 플러딩 지역을 도출하기 위해, 제안 방안은 하나의 수학적 모델을 제시한다. 시뮬레이션 결과는 제안 방안이 기존의 방안들보다 에너지 소비 관점에서 더 효율적임을 증명한다.