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이동통신망의 최적화를 위한 M2M 가상시뮬레이터 설계 및 구현 원문보기

  • 저자

    이순식

  • 학위수여기관

    경상대학교 대학원

  • 학위구분

    국내박사

  • 학과

    정보통신공학과 통신공학

  • 지도교수

  • 발행년도

    2014

  • 총페이지

    73 p.

  • 키워드

    m2m 이동통신;

  • 언어

    kor

  • 원문 URL

    http://www.riss.kr/link?id=T13534337&outLink=K  

  • 초록

    Abstract Mobile communication has rapidly grown over recent years and evolved from analog to digital and from voice-based service to data-based service. Transmission speed improvement over time has rapidly phased into ubiquitous environments thanks to the development of various wireless communication technologies. M2M combines with communication of intelligence and machine which could acknowledge and act and by promoting interchange between machines. M2M is an abbreviation for machine-to-machine. The main purpose of basic communication is communication between people, which is described as person-to-person. M2M is a communication for which a machine, a thing or an intelligent device replaces a person and things are positioned at one or both ends of communication, without controlling directly by human due to the advancement in technology. M2M(Machine-to-Machine) communication refers to communication for an intelligent device to deliver, collect and provide information about position, time, weather and finance that are owned by the things acquired through several paths such as a sensor. On a broader perspective, it is a solution that can identify and control things in a remote area, systems, vehicles, person's physical conditions and position information, in combination with communication and IT technology. M2M is based on a concept of the networking between equipment and machines spread widely in machines which daily lives with us. For domestic technique development and standardization status, mobile communication carriers, research institutes and industries are developing standards for domestic forums based on the M2M/IoT forum and also exploiting a group standard by TTA PG 708. Standards for M2M communication technology have been in progress based on the service model between the current device and server. The development of standards for the compatibility of the data collected from low-power M2M communication control technology and the data between the M2M server and application services has already been made. For overseas technology development and standardization status, ETSI TC M2M is exploiting End-to-End M2M standards, along with 3GPP developing standards related to mobile communication networks for providing M2M services based on mobile communication networks. International standards for IoT based on ITU-T are also in progress. According to one case study around, there are two control systems - one is the delivery control system that grasps the moving status of freight by loading modules on delivery and logistics vehicles in combination with the delivery control system, and the other is the stock control system that saves expenses by figuring out stock and failure information by loading modules on a vending machine in combination with a calibration system. There is also an operation management system in charge of vehicle operation status and vehicle safety in real time by loading modules on taxi and bus in connection with operation management. Also there is vehicle theft prevention, building invasion monitoring and location tracking service that can monitor out the patient conditions in real time. In addition with small GPS modules, there are smart grid services designed for reduction in food waste in the apartment complex, smart auto services designed for managing main auto parts inside the vehicle and status management between electronic devices, smart home services that enable the control of appliances, such as household TV, refrigerators, and air conditioners, from a mobile device. M2M has evolved from simple functions such as inspection, information collection and monitoring to a customer-oriented custom data solution, bringing a lot of more data traffic than before. Therefore, as its uses and utilization extend into more fields and the number of terminals used are steadily growing compared to the existing communication, the number of transmitted information in each services are also on the rise. This each data makes use of mobile communication networks, and when the traffic reaches its limit, it may keep M2M communication service from being smoothly processed. This study implements a virtual simulator using the Knapsack Problem algorithm for processing M2M services smoothly when mobile communication networks reach its limit. A virtual simulator, designed to process the data entering each system primarily in mobile communication networks, implements how to prioritize data values that needs primary processing and data values to be processed later, with the use of the Knapsack Problem algorithm. With M2M technology far more upgraded and compact devices growing in number, it may be necessary to implement optimized communication networks by prioritizing data when there is an explosive traffic increase. The study shows when explosive increase of traffic in M2M service happens, mobile communication networks began in order to provide optimized mobile communication networks by providing the order of priority after selecting important data. The Knapsack problem is about how to pick out baggage for the sum of values being maximized, when loads with certain values and weight into the bag, despite the maximum weight of a traveler's suitcase is fixed. The Knapsack problem may be divided into fractional data and non-fractional data, and the former is called a Fractional knapsack problem and the latter called a 0-1 Knapsack problem. This study dealt with algorithms such as Depth-first search, Branch and bound search in the Knapsack problem, as each terminal device transmitting urgent information or information that requires rapid despatch needs to take priority and arrival times of the data for primary processing when traffic grows explosively in M2M communication. The branch and bound search algorithm, however, must not be cut in part and insert. Only two options are available - Inserting (0), Not inserting (1) - therefore, it does not meet algorithm methods required in this study. The Depth-First Search algorithm, however, allows an item to be split as much as the remaining weight which then can be fully filled in the baggage. This study, therefore, conducts an implementation using the Depth-First Search backtracking algorithm. When the Knapsack Problem algorithm begins, the condition value of data is being compared with other data. When the condition value of data to be newly compared is lower, the existing data will be stored, whereas when the condition value of data to be newly compared is higher, the new data is stored. If the amount of data processed in this manner is optimized, the data is processed, whereas if the amount of data is not optimized, other data is added and data is additionally processed in the ordered named. This is when the new data inserted into the existing arranged data that was processed before. The comparison shows the number of data processing times has changed on a regular basis since the value narrowed down priorities to two, regardless of any data being searched for, as the graph of the number of processes according to reduction in arrival time. Above results indicate the number of data processing times has no more effect beyond a certain level no matter how many reductions are made to priorities. Many reductions in priorities do not guarantee satisfactory results and reducing a value based on the arrival time cuts the number of data processing times strikingly. The study shows good results come out when the arrival time of data being reduced and the value for priority reduced by 2. A virtual M2M simulator is a program that helps finding out optimum conditions for quick processing if possible, by judging with the ranking of a terminal device which sends data and the data to process in a several times . Better return values did not come out no matter how many priorities and arrival times were reduced, whereas reasonable reduction in priorities and arrival times brought good results. Scheduling methods based on the Knapsack Problem may have different outcomes depending on what terminal device should be on priority and what data to transmit. Situations used by each suitable application can prevent damages on an explosive traffic growth of mobile communication networks in the case of emergency.


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