산업용 IoT 네트워크에서의 강화학습을 통한 에너지 절약을 위한 효율적 자원할당 방법
Efficient Resource Allocation for Energy Saving with Reinforcement Learning in Industrial IoT Network
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
IWSN(Industrial Wireless Sensor Network)는 센서, 로봇, 기계 등의 산업 장비들을 무선으로 연결하여 공정 모니터링 및 제어를 통한 산업 자동화 실현을 가능하게 하는 산업 IoT의 핵심 기술로써 실시간, 신뢰성, 에너지 효율 등 현대 산업환경의 엄격한 요구사항을 지원해야 한다. 이를 위해 IWSN 에서는 다중 라우팅 경로 설정, 고정적 중복자원 할당 및 비경쟁 기반 스케줄링 등 신뢰적 통신 방식이 사용된다. 그러나 활용되지 않는 무선 자원의 낭비는 한정된 무선 자원의 효율뿐만 아니라 에너지 효율을 저하한다. 본 논문에서는 통신 스케줄링 시 강화학습을 활용하여 사용되지 않는 할당된 무선 자원을 파악하고, 이를 반영한 자원 재할당을 통해 전체 산업 네트워크의 에너지 소모를절약하는 방안을 제안한다. 실험을 통한 성능평가 결과, 제안 방안은 높은 전송 신뢰성을 유지하면서도 기존 방법에 비해 약 30% 향상된 스케줄링 자원 효율을 보였다. 또한, 불필요한 통신을 줄임으로써 에너지 효율 및 전송지연이 각각 21%, 38% 이상 개선됨을 확인하였다.
Abstract
Industrial Wireless Sensor Network (IWSN) is a key feature of Industrial IoT that enables industrial automation through process monitoring and control by connecting industrial equipment such as sensors, robots, and machines wirelessly, and must support the strict requirements of modern industrial environments such as real-time, reliability, and energy efficiency. To achieve these goals, IWSN uses reliable communication methods such as multipath routing, fixed redundant resource allocation, and non-contention-based scheduling. However, the issue of wasting redundant resources that are not utilized for communication degrades not only the efficiency of limited radio resources but also the energy efficiency. In this paper, we propose a scheme that utilizes reinforcement learning in communication scheduling to periodically identify unused wireless resources and reallocate them to save energy consumption of the entire industrial network. The experimental performance evaluation shows that the proposed approach achieves about 30% improvement of resource efficiency in scheduling compared to the existing method while supporting high reliability. In addition, the energy efficiency and latency are improbed by more than 21% and 38%, respectively, by reducing unnecessary communication.
참고문헌 (23)
- W. Z. Khan, "Industrial internet of things : Recent advances, enabling technologies and open challenges", Computer&electrical engineerging, 81, pp.106522~ (2020)[DOI]
- M. C. Zizic, "From industry 4. 0 towards industry 5. 0 : A review and analysis of paradigm shift for the people, organization and technology", Energies, 15(14) (2022)[DOI]
- I. Silva, "Embedded Computing Systems: Applications, Optimization, and Advanced Design", pp.343~359 (2013), IGI Global
- A. Willig, "Wireless technology in industrial networks", Proceedings of the IEEE, 93(6), pp.1130~1151 (2005)
- K. J. Singh, "Create your own Internet of things : A survey of IoT platforms", IEEE Consumer Electronics Magazine, 6(2), pp.57~68 (2017)
- "IEEE 802. 15 Working Group for WPAN"[URL]
- "Wi-Fi Alliance"[URL]
- K. Sharma, "A study of wireless networks : WLANs, WPANs, WMANs, and WWANs with comparison", International Journal of Computer Science and Information Technologies, 5(6), pp.7810~7813 (2014)
- M. S. Mahmoud, "A study of efficient power consumption wireless communication techniques", modules for internet of things (IoT) applications, ait.2016.62002, pp.2016, ~
- G. Chen, "Joint scheduling and channel allocation for end-to-end delay minimization in industrial WirelessHART networks", IEEE Internet of Things Journal, 6(2), pp.2829~2842 (2018)[DOI]
- D. Chen, "Real-Time Mesh Network for Industrial Automation", pp.1~6 (2010), Springer
- A. Slivkins, "Introduction to multi-armed bandits", Foundations and Trends in Machine Learning, 12(1-2), pp.1~286 (2019)[DOI]
- A. Saifullah, "Real-time scheduling for WirelessHART networks", 2010 31st IEEE Real-Time Systems Symposium, pp.150~159 (2010)
- A. Bachir, "MAC essentials for wireless sensor networks", IEEE communications surveys & tutorials, 12(2), pp.222~248 (2010)[DOI]
- R. S. Sutton, "Reinforcement learning : An introduction" (2018), MIT press
- S. Agrawal, "Analysis of thompson sampling for the multi-armed bandit problem", JMLR Workshop and Conference Proceedings, In Conference on learning theory, pp.39.1~39.26 (2012)
- S. Han, "Reliable and real-time communication in industrial wireless mesh networks", IEEE Real-Time and Embedded Technology and Applications Symposium, pp.3~12 (2011)
- F. Li, "Data aggregation framework for energy-efficient wirelesshart networks", Journal of System Architecture, 63, pp.70~79 (2016)[DOI]
- S. Duquennoy, "Orchestra : Robust mesh networks through autonomously scheduled TSCH", Proceedings of the 13th ACM conference on embedded networked sensor systems, pp.337~350 (2015)
- J. Shi, "Distributed graph routing and scheduling for industrial wireless sensor-actuator networks", IEEE, 2019, pp.27(4), 1669~1682
- V. P. Modekurthy, "Distributedhart : A distributed real-time scheduling system for wirelesshart networks", 2019 IEEE Real-Time and Embedded Technology and Applications Symposium, pp.216~227 (2019)
- S. Kim, "Routing Graph Construction Scheme to Prevent Path Failure due to Mobile Devices in Industrial Wireless Sensor Networks", Journal of Korean Institute of Communications and Information Sciences, 44(3), pp.528~539 (2019)[DOI]
- A. Elsts, "TSCH-Sim : Scaling up simulations of TSCH and 6TiSCH networks", Sensors, 20(19), pp.5663~ (2020)[DOI]