• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.11
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• pp.2901-2919
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• 2012

Recent research in MEMS (Micro-Electro-Mechanical Systems) and wireless communication has enabled tracking of continuous objects, including fires, nuclear explosions and bio-chemical material diffusions. This paper proposes an energy-efficient scheme that detects and tracks different dynamic shapes of a continuous object (i.e., the inner and outer boundaries of a continuous object). EBCO (Efficient Boundary detection and tracking of Continuous Objects in WSNs) exploits the sensing capabilities of sensor nodes by automatically adjusting the sensing range to be either a boundary sensor node or not, instead of communicating to its neighboring sensor nodes because radio communication consumes more energy than adjusting the sensing range. The proposed scheme not only increases the tracking accuracy by choosing the bordering boundary sensor nodes on the phenomenon edge, but it also minimizes the power consumption by having little communication among sensor nodes. The simulation result shows that our proposed scheme minimizes the energy consumption and achieves more precise tracking results than existing approaches.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.287-291
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• 2010

In this paper, we designed and implemented mobile object automatic system based on senor networks for telematics. For developing this system, we gather the various sensing data through wireless communication method using zigbee sensor networks and analyze them in monitoring equipment. And we enable the driver to recognize the car state information on the whole by interfacing analyzed data to telematics unit. And, we implemented automatic controller that can control temperature and humidity in car automatically by actuating air conditioner based on the data that was monitored throughout temperature sensor, humidity sensor and brightness sensor based on sensor networks.

• Journal of KIISE
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• v.41 no.10
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• pp.745-761
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• 2014

To address the problems of existing traffic information acquisition systems such as high cost and low scalability, wireless sensor networks (WSN)-based traffic information acquisition systems have been studied. WSN-based systems have many benefits including high scalability and low maintenance cost. Recently, various sensors are studied for traffic surveillance based on WSN, such as magnetic, acoustic, and accelerometer sensors. However, ultrasonic sensor based systems have not been studied. There are many issues for WSN-based systems, such as battery driven operation and low computing power. Thus, power saving methods and specific algorithms with low complexity are necessary. In this paper, we introduce optimal methodologies for power saving of ultrasonic sensors based on the modeling and analysis in detail. Moreover, a new vehicle detection algorithm for low complexity using ultrasonic data is presented. The proposed methodologies are implemented in a tiny microprocessor. The evaluation results show that our algorithm has high detection accuracy.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.6
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• pp.121-130
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• 2009

In this paper, Gaussian probability distribution model based multi-sensor data fusion algorithm is proposed for a vehicular location awareness system. Conventional vehicular location awareness systems are operated by GPS (Global Positioning System). However, the conventional system is not working in the indoor of building or urban area where the receiver is difficult to receive the signal from satellites. A method which is combined GPS and UWB (Ultra Wide-Band) has developed to improve this problem. However, vehicular is difficult to receive seamless location information since the measurement systems by both GPS and UWB convert the vehicle's movement information separately at each sensor. In this paper, normalized probability distribution model based Hybrid UWB/GPS is proposed by utilizing GPS location data and UWB sensor data. Therefore the proposed system provides information with seamless and location flexible properties. The proposed system tested by Ubisense and Asen GPS in the $12m{\times}8m$ outdoor environments. As a result, the proposed system has improved performance for accurateness and connection ability between devices to support various CNS (Car Navigation System).

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.9 s.351
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• pp.80-87
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• 2006

In this paper, we analyze the performance of IEEE 802.11b WLAN communication between access point(AP) and mobile equipment(ME) in 2.4 GHz band with noise and interference factors. WLAN communication at in-vehicle environment is assumed as the communication between main vehicle controller and electronic device such as sensor, ECU (Electrical Control Unit) in vehicle on telematics field for implementing wireless vehicle control system. Received interference level from other system's mobile equipment in the same band and automobile noise from each part of vehicle can be the main factors that can cause increasing error rate of control signal. With these (actors, we focus on the Eb/No the BER performance of WLAN for analyzing the characteristic of interference factors by the measured bit error rate.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.316-319
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• 2008

Most of campus vehicle management systems, so far, have simple functions such as managing vehicle in/out or issuing parking tickets. Recently some of them use RFID tags to count total numbers of cars in the campus, excluding exact parking position management. In this paper we propose a new campus vehicle management system using wireless sensor network location management scheme. This system adopts RSSI based location management method with some performance improvement technique. According to the experimental result, this proposed scheme can be used to implement an effective campus vehicle management system.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.5
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• pp.1003-1010
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• 2018

The geomagnetic sensor nodes are usually designed with wireless communication and battery for easy installation on the road without cable connection and wiring. To minimize operation cost of a system, it is important to prolong the battery lifetime of the geomagnetic sensors installed on the road. In this paper, we propose a low-power TDMA MAC protocol that reduces energy consumption while guaranteeing the real-time vehicle detection information in the vehicle detection system. The performance evaluations are conducted through a real testbed of a vehicle detection system, and it proves that the proposed low-power protocols provides 5 year lifetime by improving energy efficiency up to about 62%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.9
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• pp.2114-2120
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• 2015

Vehicle cloud technology is a fusion technology of vehicle communication technology and cloud computing used in wired and wireless Internet, and has attracted attention as a new IT paradigm. It is expected that it would contribute to resolve the road traffic problem with effective communication by providing computer, sensor, communication, device, and resource. but security is necessary to apply vehicle cloud environment and it have to resolve security threats and various attacks occurred in wired and wireless vehicle environment. Therefore, in this paper, we designed security framework to provide secure communication between vehicle and vehicle, and vehicle and the Road side in the vehicle cloud environment. Safety and security of the vehicle environment was satisfied with the security requirements of the vehicle and cloud-based environment, and increased efficiency than the conventional vehicle network communication protocols.

• Journal of Sensor Science and Technology
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• v.26 no.3
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• pp.199-203
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• 2017

In this paper, we developed an underwater localization system for underwater robot docking using the electromagnetic wave attenuation model. Electromagnetic waves are generally known to be impossible to use in water environment. However, according to the conclusions of the previous studies on the attenuation characteristics in underwater, the attenuation pattern is uniform and its model was accurately proposed and verified in 3-dimensional space via the omnidirectional antenna. In this paper, a docking structure and localization sensor system are developed for a widely used cone type docking mechanism. First, we fabricated electromagnetic wave range sensor transmit modules. And a mobile sensor node is equipped with unmanned underwater vehicle(UUV)s. The mobile node senses the four different signal strength (RSS: Received Signal Strength) from fixed nodes, and the obtained RSS data are transformed to each distance information using the 3-Dimensional EM wave attenuation model. Then, the relative localization between the docking area and underwater robot can be achieved according to optimization algorithm. Finally, experimental results show the feasibility of the proposed localization system for the docking induction by comparing the errors in the actual position of the mobile node and the theoretical position through the model.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.2
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• pp.128-133
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• 2021

Currently, autonomous vehicle markets are commercializing a third-level autonomous vehicle, but there is a possibility that an accident may occur even during fully autonomous driving due to stability issues. In fact, autonomous vehicles have recorded 81 accidents. This is because, unlike level 3, autonomous vehicles after level 4 have to judge and respond to emergency situations by themselves. Therefore, this paper proposes a vehicle crisis detection system(VCDS) that collects and stores information outside the vehicle through CNN, and uses the stored information and vehicle sensor data to output the crisis situation of the vehicle as a number between 0 and 1. The VCDS consists of two modules. The vehicle external situation collection module collects surrounding vehicle and pedestrian data using a CNN-based neural network model. The vehicle crisis situation determination module detects a crisis situation in the vehicle by using the output of the vehicle external situation collection module and the vehicle internal sensor data. As a result of the experiment, the average operation time of VESCM was 55ms, R-CNN was 74ms, and CNN was 101ms. In particular, R-CNN shows similar computation time to VESCM when the number of pedestrians is small, but it takes more computation time than VESCM as the number of pedestrians increases. On average, VESCM had 25.68% faster computation time than R-CNN and 45.54% faster than CNN, and the accuracy of all three models did not decrease below 80% and showed high accuracy.