• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5476-5496
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• 2016

Energy efficiency of resource-constrained wireless sensor networks is critical in applications such as real-time monitoring/surveillance. To improve the energy efficiency and reduce the energy consumption, the time series data can be compressed before transmission. However, most of the compression algorithms for time series data were developed only for single variate scenarios, while in practice there are often multiple sensor nodes in one application and the collected data is actually multivariate time series. In this paper, we propose to compress the time series data by the Lasso (least absolute shrinkage and selection operator) approximation. We show that, our approach can be naturally extended for compressing the multivariate time series data. Our extension is novel since it constructs an optimal projection of the original multivariates where the best energy efficiency can be realized. The two algorithms are named by ULasso (Univariate Lasso) and MLasso (Multivariate Lasso), for which we also provide practical guidance for parameter selection. Finally, empirically evaluation is implemented with several publicly available real-world data sets from different application domains. We quantify the algorithm performance by measuring the approximation error, compression ratio, and computation complexity. The results show that ULasso and MLasso are superior to or at least equivalent to compression performance of LTC and PLAMlis. Particularly, MLasso can significantly reduce the smooth multivariate time series data, without breaking the major trends and important changes of the sensor network system.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.19-29
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• 2022

Natural user interface (NUI) is used for the natural motion interface without using a specific device or tool like a mouse, keyboards, and pens. Recently, as non-contact sensor-based interaction technologies for recognizing human motion, gestures, voice, and gaze have been actively studied, an environment has been prepared that can provide more diverse contents based on various interaction methods compared to existing methods. However, as the number of sensors device is rapidly increasing, the system using a lot of sensors can suffer from a lack of computational resources. To address this problem, we proposed a real-time multi-device control system for natural interactive platform. In the proposed system, we classified two types of devices as the HC devices such as high-end commercial sensor and the LC devices such astraditional monitoring sensor with low-cost. we adopt each device manager to control efficiently. we demonstrate a proposed system works properly with user behavior such as gestures, motions, gazes, and voices.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.5
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• pp.281-290
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• 2014

The real-time data dissemination schemes exploit the spatiotemporal commuication approach which forwards data at the delivery speed calculated with the desired time deadline and the end-to-end distance in wireless sensor networks (WSNs). In practical environments, however, the performance of the real-time data dissemination might be degraded by additional and inevitable delay due to some holes. Namely, the holes lengthen the data delivery path and the spatiotemporal approach could not estimate a distance of the data delivery path. To deal with this, we propose A Hole Self-Organization Real-time Routing Protocol for Irregular Wireless Sensor Networks. In proposed protocol, nodes around holes could detect them at deploying phase. A hole is represented as a circle with center point and radius. This hole information is processed and provided as a form of location service. When a source queries a destination location, location provider replies certain points for avoiding holes as well as destination location. Thus, the source could set desired speed toward the destination via the points. Performance evaluation shows that provides better real-time service in practical environments.

• The Journal of Korean Institute of Information Technology
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• v.17 no.4
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• pp.87-97
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• 2019

SRS was proposed for immediate processing of the meaning of sensor data on mobile devices independent from specific sensor networks and sensor type. However, each time new sensor data is received, sensor data inspection operations are performed repeatedly, and it cause resulting in low performance. App profile-based SRS has been proposed to resolve the problem. The app profile-based SRS has improved the SRS problem through the profile, but has been tested in a virtual simulation environment. After that the test was experimented in a real-time environment, but has not been tested with a variety of dynamic factors. Therefore, this paper experiment considering such as user mobility and sensor density in real-time environment. And this paper also evaluate performance of the App profile-based through analysis of the results of the experiment. As a result, app profile-based SRS is high influence by density and sensor type, and the number of sensor node is not influence.

• Journal of IKEEE
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• v.20 no.4
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• pp.420-423
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• 2016

In this paper, we proposed an implementation of panoramic image stitching that operates in real time at the embedded environment by applying ROI based PROSAC algorithm using the directionality of the image. The conventional panoramic image stitching applies SURF or SIFT algorithm which contains unnecessary computation and a lots of data to detect feature points. In this paper, we use the direction of the input image and we proposed the method of reducing the unnecessary computation by using ROI. We use a gyro sensor and an acceleration sensor. Output data from gyro and acceleration sensors can be calibrated by complementary filter. The calibration does not affect the operating time of the proposed image stitching algorithm in embedded environment. Therefore, it is possible to operate in real-time.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.193-202
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• 2012

RFID tag awareness information and sensor data continuously change, and are categorized with the position. They are able to similar data in the side, called massive data to change in time. If two data are managed together, a convergence process of object awareness along change of environment is possible. If RFID middleware and EPCIS repository realized the integrated system, it is usable with the functions of middleware and repository at the same time. The real-time awareness information retrieval is possible without process, getting information from another middleware. In this paper, it is able to continuously read information from RFID reader and sensor equipment and store to database in order to make general object awareness and an object retrieval dependent on an environmental information change possible by real time. ALE-compliant middleware and EPCIS repository proposing for standards at EPCglobal is designed and implemented to be able to deal with RFID and sensor data to bases on the collected data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.10
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• pp.1307-1313
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• 2018

In recent years, there have been frequent occurrences of collapsing buildings and tilting accidents due to frequent earthquakes and aging of buildings. Various methods have been proposed to prevent disasters on these buildings. In this paper, we propose a system that provides an indication of anomalous phenomena such as collapse and tilting of buildings by real-time monitoring of IoT(Internet of Things) based architectural anomalies. The MEMS sensor is based on the inclinometer sensor and the accelerometer sensor, transmits the detected data to the server in real time, accumulates the data, and provides the service to cope when the set threshold value is different. It is possible to evacuate and repair the collapse and tilting of the building by warning the occurrence of the upper threshold event such as the collapse and tilting of the building.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.5
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• pp.424-434
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• 2017

In industrial wireless sensor networks, many applications require integrated QoS supporting. This paper proposes a reliable protocol for real-time monitoring in industrial wireless sensor networks. Retransmission is well-known to recover the transmission failure, however, this might cause the time delay to violate the real-time requirement. The proposed protocol exploits broadcasting feature of wireless networks and the temporal opportunity concept. The opportunities to relay the data packets are shared by the broadcasting feature and the temporal opportunity concept maximize the number of candidates in communication. Simulation results show that the proposed protocol is superior to the existing real-time protocols in term of real-time service and reliability.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.310-318
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• 2018

Recently, the importance of big data analysis is increasing as a large amount of data is generated by various devices connected to the Internet with the advent of Internet of Things (IoT). Especially, it is necessary to analyze various large-scale IoT streaming sensor data generated in real time and provide various services through new meaningful prediction. This paper proposes a real-time indoor PM10 concentration prediction LSTM model based on streaming data generated from IoT sensor using AWS. We also construct a real-time indoor PM10 concentration prediction service based on the proposed model. Data used in the paper is streaming data collected from the PM10 IoT sensor for 24 hours. This time series data is converted into sequence data consisting of 30 consecutive values from time series data for use as input data of LSTM. The LSTM model is learned through a sliding window process of moving to the immediately adjacent dataset. In order to improve the performance of the model, incremental learning method is applied to the streaming data collected every 24 hours. The linear regression and recurrent neural networks (RNN) models are compared to evaluate the performance of LSTM model. Experimental results show that the proposed LSTM prediction model has 700% improvement over linear regression and 140% improvement over RNN model for its performance level.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.94.3-94
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• 2001

A sensor system was developed to measure displacement of civil structure at a long distance. A He-Ne Laser tube and photodiodes ware used for non-contact measurement. This system allows real time vibration displacement measurement of bridges. The measured displacement data is displayed on computer monitor graphically and also in digit. The accuracy of the displacement measurement shows 2mm in vertical vibration. It shows remote inspection of the vibration of long bridges and buildings.