DOI QR코드

DOI QR Code

Implementation of ACS-based Wireless Sensor Network Routing Algorithm using Location Information

위치 정보를 이용한 개미 집단 시스템 기반의 무선 센서 네트워크 라우팅 알고리즘 구현

  • 전혜경 (인하대학교 컴퓨터정보공학과) ;
  • 한승진 (경인여자대학교 정보미디어학부) ;
  • 정경용 (상지대학교 컴퓨터정보공학부) ;
  • 임기욱 (선문대학교 컴퓨터정보공학부) ;
  • 이정현 (인하대학교 컴퓨터정보공학과)
  • Received : 2011.03.29
  • Accepted : 2011.04.07
  • Published : 2011.06.28

Abstract

One of the objectives of research on routing methods in wireless sensor networks is maximizing the energy life of sensor nodes that have limited energy. In this study, we tried to even energy use in a wireless sensor network by giving a weight to the transition probability of ACS(Ant Colony System), which is commonly used to find the optimal path, based on the amount of energy in a sensor and the distance of the sensor from the sink. The proposed method showed improvement by 46.80% on the average in energy utility in comparison with representative routing method GPSR (Greedy Perimeter Stateless Routing), and its residual energy after operation for a specific length of time was 6.7% more on the average than that in ACS.

무선 센서 네트워크의 라우팅 기술은 제한된 에너지를 갖고 있는 센서 노드들의 에너지 수명을 최대한으로 연장할 수 있는 방법으로 많이 연구되고 있다. 기본 라우팅 방법 중 위치 정보를 이용한 라우팅 방법은 라우팅 설정을 위한 계산시에 필요한 정보의 양이 평면, 계층적 라우팅 방법보다 적기 때문에 효율적이다. 하지만 주로 거리를 활용하기 때문에 센서 노드의 에너지 활용도가 떨어질 수도 있다. 본 논문에서는 최적의 경로 탐색에 많이 이용되고 있는 개미 집단 시스템(ACS : Ant Colony System)의 전이 확률에 센서의 에너지양과 싱크와의 거리를 이용한 가중치를 부여하여 무선 센서 네트워크의 에너지 사용량을 고르게 사용할 수 있게 하였다. 제안된 방법은 대표적인 GPSR(Greedy Perimeter Stateless Routing)과 비교하여 에너지 사용도에 있어 평균적으로 46.80%의 향상을 보였으며, 기존의 ACS보다 동일한 시간의 수행 종료 후 잔여 에너지가 평균 6.7% 더 남아 있음을 확인하였다.

Keywords

References

  1. I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "A survey on sensor networks," IEEE Communications Magazine, Vol.40, pp.102-114, 2002. https://doi.org/10.1109/MCOM.2002.1024422
  2. G. Anastasi, M. Conti, M. Francesco, and A. Passarella, "Energy conservation in wireless sensor networks: A survey," Ad Hoc Networks, Vol.7, No.3, pp.537-568, 2009. https://doi.org/10.1016/j.adhoc.2008.06.003
  3. R. Szewczyk, E. Osterwil, J. Polastre, and M. Hamilton, "A Mainwaring Habitat Monitoring With Sensor Networks," Communications of the ACM Vol.47, No.6, pp.34-40, 2004. https://doi.org/10.1145/990680.990704
  4. B. Chen, K. Jamieson, H. Balakrishnan, and R. Morris, "Span: an energy-efficient coordination algorithm for topology maintenance in ad hoc wireless networks," Proceedings of the ACM/IEEE International Conference on Mobile Computing and Networking, 2001(7).
  5. B. Karp and H. T. Kung, "Greedy perimeter stateless forwarding for wireless networks," Proceedings of the 6th Annual ACM/IEEE International Conference on Mobile Computing and Networking(MobiCom '00), pp.243-254, Boston, MA., 2000(8).
  6. http://www.inf.ethz.ch/-kasten/research/bathtub/energy_consumption.html, 2001.
  7. M. Stemm and R. H. Katz, "Measuring and reducing energy consumption of network interfaces in hand-held devices," IEICE Transactions on Communications, E80-B, No.8, pp.1125-1131, 1997(8).
  8. K. Sohrabi, Protocols for self-organization of a wireless sensor network, IEEE Personal Communications Vol.7, No.5, pp.16-27, 2000. https://doi.org/10.1109/98.878532
  9. M. Younis, M. Youssef, and K. Arisha, Energy-aware routing in cluster-based sensor networks, in: Proceedings of the 10th IEEE/ACM International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS2002), Fort Worth, TX, 2002(10). https://doi.org/10.1109/MASCOT.2002.1167069
  10. C. Schurgers and M. B. Srivastava, Energy efficient routing in wireless sensor networks, in: The MILCOM Proceedings on Communications for Network-Centric Operations:Creating the Information Force, McLean, VA, 2001.
  11. Y. Xu, J. Heidemann and D. Estrin, "Geography-informed energy conservation for ad-hoc routing," Proceedings of 7th Annual ACM/IEEE International Conference on Mobile Computing and Networking(MobiCom '01), pp.70-84, Rome, Italy, 2001(7). https://doi.org/10.1145/381677.381685
  12. Xin Liu, Q. Huang, and Ying Zhang, "Comb, needles, haystacks:balancing push and pull for discovery in large-scale sensor network," Sensys '04, 2004.
  13. L. M. Gambardella and M. Dorigo, "Ant Colony System: A Cooperative Learning approach to the Traveling Salesman Problem," IEEE Transactions on Evoutionery Computation, Vol.1, No.1, 1997. https://doi.org/10.1109/4235.585892
  14. T. Rappaort, Wireless Communications: Principle & Practice, Englewood Cliffs, NJ: Prentice-Hall, 1996.
  15. S. Funke, "Topological Hole Detection in Wireless Sensor Networks and its Applications," Workshop on Discrete Algorithms and Methods for MOBILE Computing and Communications, pp.44-53, 2005. https://doi.org/10.1145/1080810.1080819
  16. M. Dorigo and C. Blum, "Ant colony optimization theory:A survey"
  17. M. Dorigo, V. Maniezzo, and A. Colorni, "Positive FeedBack as a search strategy," Report No. 91-106, Laboratorio di Calcolatori, Dipartimento di Elettronica, Milano, Italy, 1991.
  18. B. Bullnheimer, R. F. Hartel, and C. Straub, "A New Rank Based Version of the Ant System - A Computational Study," Working Paper No.1, Department of Management of Science, University of Vienna, 1997(4).