• Journal of Sensor Science and Technology
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• v.26 no.1
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• pp.1-6
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• 2017

The advanced driver assistant system (ADAS) requires the collection of a large amount of information including road conditions, environment, vehicle status, condition of the driver, and other useful data. In this regard, large-scale sensor networks can be an appropriate solution since they have been designed for this purpose. Recent advances in sensor network technology have enabled the management and monitoring of large-scale tasks such as the monitoring of road surface temperature on a highway. In this paper, we consider the estimation and fusion problems of the large-scale sensor networks used in the ADAS. Hierarchical fusion architecture is proposed for an arbitrary topology of the large-scale sensor network. A robust cluster estimator is proposed to achieve robustness of the network against outliers or failure of sensors. Lastly, a robust hierarchical data fusion scheme is proposed for the communication channel between the clusters and fusion center, considering the non-Gaussian channel noise, which is typical in communication systems.

• Journal of Internet Computing and Services
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• v.9 no.1
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• pp.79-90
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• 2008

In the ubiquitous environment sensor network technologies have advanced for collecting information of the environment. With the rapid growth of sensor network technology, it is necessary and important to share the collected sensor data with a large base of diverse users. In order to provide dissemination of sensor data, we design an information dissemination system using an independent disseminator between provider and consumer. This paper describes how we designed the information dissemination system using one of the possible dissemination patterns for sensor networks, and an efficient matching algorithm called CGIM (Classed Grouping Index Matching) which employs a dynamic re-grouping scheme.

• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.105-112
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• 2015

The estimation of missing sensor values is an important problem in sensor network applications, but the existing approaches have some limitations, such as the limitations of application scope and estimation accuracy. Therefore, in this paper, we propose a new estimation model based on a spatial-temporal correlation analysis (STCAM). STCAM can make full use of spatial and temporal correlations and can recognize whether the sensor parameters have a spatial correlation or a temporal correlation, and whether the missing sensor data are continuous. According to the recognition results, STCAM can choose one of the most suitable algorithms from among linear interpolation algorithm of temporal correlation analysis (TCA-LI), multiple regression algorithm of temporal correlation analysis (TCA-MR), spatial correlation analysis (SCA), spatial-temporal correlation analysis (STCA) to estimate the missing sensor data. STCAM was evaluated over Intel lab dataset and a traffic dataset, and the simulation experiment results show that STCAM has good estimation accuracy.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.6
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• pp.369-376
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• 2011

Since wireless sensor networks generally have the capability of network recovery, malfunction of a few sensor nodes in a sensor network does not cause a crucial problem paralyzing the sensor network. The malfunction of the sink node, however, is critical. If the sink node of a sensor network stops working, the data collected by sensor nodes cannot be delivered to the gateway because no other sensor nodes can take the place of the sink node. This paper proposes a TDMA-based wireless sensor network equipped with dual sink nodes, with a view to preventing data loss in the case of malfunction of a sink node. A secondary sink node, which synchronizes with a primary sink node and receives data from other sensor nodes in normal situations, takes the role of the primary sink node in the case of malfunction of the primary sink, thereby eliminating the possibility of data loss. The effectiveness of the proposed scheme is demonstrated through experiments.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.8
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• pp.73-85
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• 1995

The sensor system to measure the distance precisely from the center of the sensor system to the obstacle is needed to recognize the surrounding environments, and the sensor system is to be calibrated thoroughly to get the range information exactly. This study covers the calibration of the active range sensor which consists of camera and laser slit emitting device, and provides the equations to get the 3D range data. This can be possible by obtaining the extrinsic parameters of laser slit emitting device through image processing the slits measured during the constant distance intervals and the intrinsic parameters from the calibration of camera. The 3D range data equation derived from the simple geometric assumptions is proved to be applicable to the general cases using the calibration parameters. Also the exact 3D range data were obtained to the object from the real experiment.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.423-423
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• 2000

This paper proposes an effective algorithm for following a wall by an autonomous mobile robot with sonar sensors and infrared sensors in an indoor environment. The proposed method uses deadreckoning to estimate the current position and orientation of a mobile robot. Sonar sensor data are used to estimate shape and position of wall using proposed algorithm. Infrared sensor data are used as assistant when sonar sensor data is uncertain. Simulation results using mobile robot show that the proposed algorithm is proper for the following wall.

• Journal of Sensor Science and Technology
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• v.15 no.6
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• pp.433-441
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• 2006

Because of the low power and low rate of a sensor network, outlier is frequently occurred in the time series data of sensor network. In this paper, we suggest periodic pattern analysis that is applied to the time series data of sensor network and predict outlier that exist in the time series data of sensor network. A periodic pattern is minimum period of time in which trend of values in data is appeared continuous and repeated. In this paper, a quantization and smoothing is applied to the time series data in order to analyze the periodic pattern and the fluctuation of each adjacent value in the smoothed data is measured to be modified to a simple data. Then, the periodic pattern is abstracted from the modified simple data, and the time series data is restructured according to the periods to produce periodic pattern data. In the experiment, the machine learning is applied to the periodic pattern data to predict outlier to see the results. The characteristics of analysis of the periodic pattern in this paper is not analyzing the periods according to the size of value of data but to analyze time periods according to the fluctuation of the value of data. Therefore analysis of periodic pattern is robust to outlier. Also it is possible to express values of time attribute as values in time period by restructuring the time series data into periodic pattern. Thus, it is possible to use time attribute even in the general machine learning algorithm in which the time series data is not possible to be learned.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.9
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• pp.425-430
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• 2015

The Sensor Registry System(SRS) provides sensor metadata to a user for instant use and seamless interpretation of sensor data in a heterogeneous sensor network environment. The existing sensor registry system cannot provide sensor metadata in case that the network connection is not available or is unstable. To resolve the problem, this paper proposes an extension of sensor registry system using network coverage information. The extended system sends a set of sensor metadata to the user by using network coverage open data (mobile vendors, signal strength, communication type). The extended SRS proposed in this paper supports a safer sensor metadata provision than the existing SRS, and it thus improves the quality of application services.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.874-877
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• 2008

Nowadays the existing data processing systems can only support some simple query for sensor network. It is increasingly important to process the vast data streams in sensor network, and achieve effective acknowledges for users. In this paper, we propose a holistic distributed k-means algorithm for sensor network. In order to verify the effectiveness of this method, we compare it with central k-means algorithm to process the data streams in sensor network. From the evaluation experiments, we can verify that the proposed algorithm is highly capable of processing vast data stream with less computation time. This algorithm prefers to cluster the data streams at the distributed nodes, and therefore it largely reduces redundant data communications compared to the central processing algorithm.

• Journal of Sensor Science and Technology
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• v.27 no.4
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• pp.254-258
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• 2018

A Wi-Fi signal network (WSN) system is introduced in this paper. This system consists of several data-transmitting sensor modules and a data-receiving server. Each sensor module and the server contain a unique intranet IP address. A piezoelectric accelerometer with a bandwidth of 12 kHz, a 24-bit analog-digital converter with a sampling rate of 15.625 kS/s, a 32-bit microprocessor unit, and a 1-Mbps Wi-Fi module are used in the data-transmitting sensor module. A 300-Mbps router and a PC are used in the server. The system is verified using an accelerometer calibrator. The voltage output from the sensor is converted into 24-bit digital data and transmitted via the Wi-Fi module. These data are received by a Wi-Fi router connected to a PC. The input frequencies of the accelerometer calibrator (320 Hz, 640 Hz, and 1280 Hz) are used in the data transfer verification. The received data are compared to the data retrieved directly from the analog-to-digital converter used in the sensor module. The comparison shows that the developed system represents the original data considerably well. Theoretically, the system can acquire vibration signals from 600 sensor modules at an accelerometer bandwidth of 15.625 kHz. However, delay exists owing to software processes, multiplexing between sensor modules, and the use of non-real time operating system. Hence, it is recommended that this system may be used to acquire vibration signals with up to 10 kHz, which is approximately 70% of the theoretical maximum speed of the system. The system can be upgraded using parts with higher performance