Q-learning 모델을 이용한 IoT 기반 주차유도 시스템의 설계 및 구현
Design and Implementation of Parking Guidance System Based on Internet of Things(IoT) Using Q-learning Model
0
조회
0
다운로드
2
인용
0
참고문헌
키워드
초록
This paper proposes an optimal dynamic resource allocation method in IoT (Internet of Things) parking guidance system using Q-learning resource allocation model. In the proposed method, a resource allocation using a forecasting model based on Q-learning is employed for optimal utilization of parking guidance system. To demonstrate efficiency and availability of the proposed method, it is verified by computer simulation and practical testbed. Through simulation results, this paper proves that the proposed method can enhance total throughput, decrease penalty fee issued by SLA (Service Level Agreement) and reduce response time with the dynamic number of users.
Abstract
This paper proposes an optimal dynamic resource allocation method in IoT (Internet of Things) parking guidance system using Q-learning resource allocation model. In the proposed method, a resource allocation using a forecasting model based on Q-learning is employed for optimal utilization of parking guidance system. To demonstrate efficiency and availability of the proposed method, it is verified by computer simulation and practical testbed. Through simulation results, this paper proves that the proposed method can enhance total throughput, decrease penalty fee issued by SLA (Service Level Agreement) and reduce response time with the dynamic number of users.
참고문헌 (22)
- 송규석, "U-City 주차 유도 및 정보 시스템 설계 및 구현", 한국통신학회논문지B, 35(6), pp.940~947 (2010), 한국통신학회
- Getting Clever about Smart Cities: New Opportunities Require New Business Models, "J. Belissent", Forrester Research Incorporation, ~ (2010)
- S.A. Mahmud, "A Servey of Intelligent Car Parking System", Journal of Applied Research and Technology, 11(5), pp.714~726 (2013)
- T. Rajabioun, "On-street and Off-street Parking Availability Prediction Using Multivariate Spatiotemporal Models", IEEE Transactions on Intelligent Transportation Systems, 16(5), pp.2913~2924 (2015)
- G. Yan, "Smartparking : A Secure and Intelligent Parking System", IEEE Intelligent Transportation Systems Magazine, 3(1), pp.18~30 (2011)
- 최영호, "클라우드 컴퓨팅 환경에서 강화학습기반 자원할당 기법", 한국통신학회논문지, 40(4), pp.653~658 (2015), 한국통신학회
- Y. Geng, "New"Smart Parking"System Based on Resource Allocation and Reservations", IEEE Transactions on Intelligent Transportation Systems, 14(3), pp.1129~1139 (2013)
- A. Kianpisheh, "Smart Parking System(SPS)Architecture Using Ultrasonic Detector", Journal of Software Engineering and Its Applications, 6(3) (2012)
- J. Yick, "Wireless Sensor Network Survey", Computer Networks, 52, pp.2292~2330 (2008)
- A. Dunkels, "An Adaptive Communication Architecture for Wireless Sensor Networks", Proceeding of 5th ACM Conference Networked Embedded Sensor System, pp.335~349 (2007)
- "Guidelines for Construction of Cloud Data Centers", Telecommunications Technology Association, ~ (2013)
- J. Hui, "IP is Dead, Long Live IP for Wireless Sensor Networks", Proceeding of 6th ACM Conference Networked Embedded Sensor System, pp.15~28 (2008)
- 김환태, "가상화 클라우드 데이터센터에서 가상 머신 간의 균등한 성능 보장을 위한 제어 알고리즘", 한국통신학회논문지C, 38(6), pp.512~520 (2013), 한국통신학회
- R.E. Parr, "Hierarchical Control and Learning for Markov Decision Processes", Ph.D., pp.1998
- C.J.C.H. Watkins, "Q-Learning", Machine Learning, 8, pp.279~292 (1992)
- Y. Chen, "Q-learning-based Multirate Transmission Control Scheme for RRM in Multimedia WCDMA Systems", IEEE Transactions on Vehicular Technology, 53(1), pp.38~48 (2004)
- L. Hu, "Online System for Grid Resource Monitoring and Machine Learning-based Prediction", IEEE Transactions Parallel Distributed Systems, 23(1, 518), pp.134~145 (2012)
- 조호식, "혼재된 중첩 이동 네트워크에서의 라우팅 최적화 기법", 한국통신학회논문지B, 33(2), pp.82~89 (2008), 한국통신학회
- J. Rao, "A Distributed Self-learning Approach for Elastic Provisioning of Virtualized Cloud Resources", IEEE 19th Annual International Symposium on Modelling, Analysis, and Simulation of Computer and Telecommunication Systems, pp.45~54 (2011)
- Y. Jiang, "Cloud Analytics for Capacity Planning and Instant VM Provisioning", IEEE Transactions Network and Service Management, 10(3), pp.312~325 (2013)
- 김혁호, "A Pattern-Based Prediction Model for Dynamic Resource Provisioning in Cloud Environment", KSII Transactions on Internet and Information Systems, 5(10), pp.1712~1732 (2011), 한국인터넷정보학회
- M. Riedl, "Distributed automation system supports process monitoring and control", Industrial Electronics, 2011 IEEE International Symposium on Industrial Electronics, pp.711~1715 (2011)