http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
사물인터넷의 에너지 효율을 위한 클러스터 속성 기반 데이터 교환
이충산(Chungsan Lee),전수빈(Soobin Jeon),정인범(Inbum Jung) Korean Institute of Information Scientists and Eng 2017 정보과학회논문지 Vol.44 No.9
In Internet of Things (IoT), the aim of the nodes (called ‘Things’) is to exchange information with each other, whereby they gather and share information with each other through self decision-making. Therefore, we cannot apply existing aggregation algorithms of Wireless sensor networks that aim to transmit information to only a sink node or a central server, directly to the IoT environment. In addition, since existing algorithms aggregate information from all sensor nodes, problems can arise including an increasing number of transmissions and increasing transmission delay and energy consumption. In this paper, we propose the clustering and property based data exchange method for energy efficient information sharing. First, the proposed method assigns the properties of each node, including the sensing data and unique resource. The property determines whether the node can respond to the query requested from the other node. Second, a cluster network is constructed considering the location and energy consumption. Finally, the nodes communicate with each other efficiently using the properties. For the performance evaluation, TOSSIM was used to measure the network lifetime and average energy consumption.
사물인터넷에서 초음파 센서와 블루투스 통신을 이용한 스마트 주차 시스템
이충산(Chungsan Lee),한영탁(Youngtak Han),전수빈(Soobin Jeon),서동만(Dongmahn Seo),정인범(Inbum Jung) 한국정보과학회 2016 정보과학회 컴퓨팅의 실제 논문지 Vol.22 No.6
사물인터넷이 대중화되면서 주차장에도 사물인터넷 환경을 구축해 이용객이 주차한 차량의 위치 식별 및 주차 위치 안내 서비스를 제공하려는 연구가 진행되고 있다. 기존 시스템들은 차량을 식별하기 위해 식별 장치를 차량에 부착하거나 이용객이 소지하는 방법 또는 고화질 카메라를 이용하여 주차된 차량의 번호판을 인식하는 방법 등을 이용하고 있다. 하지만 이 방법들은 이용 방법과 비용 측면에서 아쉬운 면을 보였다. 본 논문에서는 초음파 센서와 블루투스 통신을 이용한 스마트 실내 주차장 관리 시스템을 제안한다. 제안하는 시스템은 각 주차공간에 설치된 주차장 센서 모트의 초음파센서를 이용하여 차량의 점령을 판단하고 블루투스의 RSSI를 이용하여 주차된 차량의 위치 식별이 가능하다. 또한 블루투스 RSSI를 이용한 실시간 실내 위치 인식을 통해 주차된 차량까지의 길 안내 서비스가 가능하다. 시스템의 성능평가를 위해 주차된 차량의 위치 인식률과 RSSI를 변환해 얻은 거리의 정확도를 측정하였다. IoT (Internet of Things) technologies have largely contributed to our smart living environment. The smart parking system is one of the prominent services that IoT supports. To identify the parked vehicles, the previous parking system use special identifying devices, the RFID tags carried by the users, and the high quality camera to recognize the vehicle license numbers. However, the previous methods cause cost inefficiency and unfriendly usages. To address these problems, we propose a smart parking system based on ultrasonic sensors and Bluetooth communication. The proposed system decides the available slots by using the sensor motes located in the parking spaces. Also it recognizes the location of the parked vehicle using Bluetooth RSSI between a Smartphone and the sensor motes. In addition, based on these converging technologies, it can support the parked routes of vehicles for users. To evaluate the implemented smart parking system, we applied the RSSI transform equations and the recognition rate for parked vehicles. As a result, the accurate rate of transformed distances could be measured.
박총명(Chongmyung Park),이충산(Chungsan Lee),조영태(Youngtae Jo),정인범(Inbum Jung) Korean Institute of Information Scientists and Eng 2014 정보과학회논문지 Vol.41 No.11
Wireless sensor networks offer a distributed processing environment. Many sensor nodes are deployed in fields that have limited resources such as computing power, network bandwidth, and electric power. The sensor nodes construct their own networks automatically, and the collected data are sent to the sink node. In these traditional wireless sensor networks, network congestion due to packet flooding through the networks shortens the network life time. Clustering or in-network technologies help reduce packet flooding in the networks. Many studies have been focused on saving energy in the sensor nodes because the limited available power leads to an important problem of extending the operation of sensor networks as long as possible. However, we focus on the execution time because clustering and local distributed processing already contribute to saving energy by local decision-making. In this paper, we present a cooperative processing model based on the processing timeline. Our processing model includes validation of the processing, prediction of the total execution time, and determination of the optimal number of processing nodes for distributed processing in wireless sensor networks. The experiments demonstrate the accuracy of the proposed model, and a case study shows that our model can be used for the distributed application.