Journal of the Korea Society for Simulation (한국시뮬레이션학회논문지)

The Korea Society for Simulation (한국시뮬레이션학회)
권호 표지
DOI QR코드

DOI QR Code

Cluster-based Delay-adaptive Sensor Scheduling for Energy-saving in Wireless Sensor Networks

센서네트워크에서 클러스터기반의 에너지 효율형 센서 스케쥴링 연구

  • 최욱 (한국외국어대학교 컴퓨터공학과) ;
  • 이용 (충주대학교 전자통신공학과) ;
  • 정유진 (한국외국어대학교 컴퓨터공학과)
  • Received : 2009.05.13
  • Accepted : 2009.08.05
  • Published : 2009.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Due to the application-specific nature of wireless sensor networks, the sensitivity to such a requirement as data reporting latency may vary depending on the type of applications, thus requiring application-specific algorithm and protocol design paradigms which help us to maximize energy conservation and thus the network lifetime. In this paper, we propose a novel delay-adaptive sensor scheduling scheme for energy-saving data gathering which is based on a two phase clustering (TPC). The ultimate goal is to extend the network lifetime by providing sensors with high adaptability to the application-dependent and time-varying delay requirements. The TPC requests sensors to construct two types of links: direct and relay links. The direct links are used for control and forwarding time critical sensed data. On the other hand, the relay links are used only for data forwarding based on the user delay constraints, thus allowing the sensors to opportunistically use the most energy-saving links and forming a multi-hop path. Simulation results demonstrate that cluster-based delay-adaptive data gathering strategy (CD-DGS) saves a significant amount of energy for dense sensor networks by adapting to the user delay constraints.

다양한 응용에 적용될 수 있는 특성을 가진 무선 센서 네트워크는 적용되는 응용에 따라 데이터 리포팅 지연시간의 제한과 같이 요구사항이 다양하므로 각 응용별로 구분되는 알고리즘이나 프로토콜 설계 패러다임을 적용하여 에너지 효율을 최대화하고 네트워크의 생존기간을 최대화할 수 있어야 한다. 이 논문에서는 2단계 클러스터링(Two Phase Clustering : TPC) 방식을 이용하여 에너지 효율 데이터 수집을 제공하기 위한 새로운 알고리즘으로 지연시간 적응형 센서 스케쥴링 방안을 제안한다. 이 논문의 궁극적인 목표는 센서들에게 응용 환경의 특성과 시간에 따라 변하는 특성을 갖는 지연시간에 대한 요구사항에 대하여 높은 적응성을 제공하여 네트워크의 생존기간을 늘리는 것이다. TPC 방식은 센서들이 직접 링크와 릴레이 링크의 두 가지 링크를 구성하도록 한다. 직접 링크는 제어 메시지나 시간에 민감한 센서 데이터들을 포워딩하는 데 사용된다. 릴레이 링크는 사용자의 지연시간 제한에 따라 데이터를 포워딩하는데 사용되며 이를 이용하여 센서들이 에너지-절약효과를 갖는 릴레이를 사용할 기회가 증가하도록 멀티홉 경로를 구성할 수 있도록 한다. 이 논문에서는 제안하는 CD-DGS 방식이 사용자의 지연시간 제한 요구사항에 잘 적응하여 센서 네트워크의 분포 밀도가 높은 경우에 상당한 비율의 에너지 효율을 보이는 것을 시뮬레이션 결과로 증명한다.

Keywords

References

  1. I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci (2002), Wireless Sensor Networks: a Survey. Computer Networks, Vol. 38, No. 4, pp. 393-422. https://doi.org/10.1016/S1389-1286(01)00302-4
  2. A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler, and J. Anderson (2002), Wireless Sensor Networks for Habitat Monitoring. In Proceedings of ACM Workshop on Wireless Sensor Networks and Applications, pp. 88-97.
  3. G. J. Pottie and W. J. Kaiser (2000), Wireless Integrated Network Sensors. Communications of the ACM, Vol. 43, No. 5, pp. 51-58. https://doi.org/10.1145/332833.332838
  4. S. Ghiasi, A. Srivastava, X. Yang, and M. Sarrafzadeh (2002), Optimal Energy Aware Clustering in Sensor Networks. http://www.mdpi.net/sensors, Vol. 2, No. pp. 258- 269. https://doi.org/10.3390/s20700258
  5. C. Intanagonwiwat, R. Govindan, and D. Estrin (2000), Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks. In Proceedings of ACM MOBICOM, pp. 56-67.
  6. W. Choi, S. K. Das, and K. Basu, (2004), Angle-based Dynamic Path Construction for Route Load Balancing in Wireless Sensor Networks. In Proceedings of IEEE Wireless Communications and Networking Conference (WC NC), pp. 2474-2479.
  7. X. Hong, M. Gerla, and H. Wang, (2002), Load Balanced, Energy-Aware Communications for Mars Sensor Networks. In IEEE Aerospace, Vol. 3, pp. 1109-1115.
  8. A. Scaglione and S. D. Servetto, (2002), On the Interdependence of Routing and Data Compression in Multi- Hop Sensor Network. In Proceedings of ACM Mobile Computing and Networking, pp. 140-147.
  9. M. Gerla, T. J. Kwon, and G. Pei, (2000), On Demand Routing in Large Ad Hoc Wireless Networks with Passive Clustering. In Proceedings of IEEE WCNC, Vol. 1, pp. 100-105.
  10. J. Gao, L. L. Guibas, J. Hershberger, L. Zhang, and A. Zhu (2001), Discrete Mobile Centers. In Proceedings of ACM Computational Geometry, pp. 188-196.
  11. S. Lindsey, C. Raghavendra, and K. M. Sivalingam, (2002), Data Gathering Algorithm in Sensor Networks Using Energy Metrics. IEEE Transactions on Parallel and Distributed Systems, Vol. 13, No. 9, pp. 924-932. https://doi.org/10.1109/TPDS.2002.1036066
  12. N. Bulusu, J. Heidemann, and D. Estrin, (2000), GPSless Low Cost Outdoor Localization for Very Small Devices. IEEE Personal Communications, Vol. 7, No. 5, pp. 28-34. https://doi.org/10.1109/98.878533
  13. I. Stojmenovic, (2002), Position-Based Routing in Ad Hoc Networks. IEEE Communications, Vol.40, No. 7, pp. 128-134. https://doi.org/10.1109/MCOM.2002.1018018
  14. W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, (2002) An Application-specific Protocol Architecture for Wireless Microsensor Networks. IEEE Transactions on Wireless Communications, Vol. 1, No. 4, pp. 660-670. https://doi.org/10.1109/TWC.2002.804190
  15. F. Zhao and L. Guibas, (2004), Wireless Sensor Networks: An Information Processing Approach. Morgan Kaufmann.
  16. S. Banerjee and A. Misra, (2002), Minimum Energy Paths for Reliable Communication in Multi-hop Wireless Networks. In Proceedings of ACM Mobile Adhoc Network Symposium (MOBIHOC), pp. 145-156.
  17. A. Nasipuri and K. Li (2002), A Directionality based Location Discovery Scheme for Wireless Sensor Networks. In Proceedings of ACM Workshop on WSNA, pp. 105-111.
  18. Younis, O and Fahmy, S. (2004) Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks. IEEE Transactions on Mobile Computing, Vol. 3, No. 4, pp. 366-379. https://doi.org/10.1109/TMC.2004.41
  19. R. Virrankoski and A. Savvides (2005) TACS: Topology Adaptive Spatial Clustering for Sensor Networks. In Proceedings of IEEE Int'l Conference on MASS, pp. 1-10.
  20. M. Lotfinezhad, B. Liang, and E. S. Sousa (2007) Adaptive Cluster-Based Data Collection in Sensor Networks with Direct Sink Access. IEEE Transactions on Mobile Computing, Vol. 7, No. 7, pp. 884-897.