DOI QR코드

DOI QR Code

해양 센서 네트워크에서 네트워크 클러스터링을 위한 유전 알고리즘

A Genetic Algorithm for Network Clustering in Underwater Acoustic Sensor Networks

  • 투고 : 2011.06.20
  • 심사 : 2011.07.18
  • 발행 : 2011.12.31

초록

해양 센서 네트워크에서 클러스터링 문제는 네트워크 수명 및 확장성을 향상시키기 위한 문제 중 하나이다. 본 논문은 모든 노드가 클러스터 헤더를 통하여 싱크 노드로 데이터를 전송하는데 사용되는 전송 에너지를 최소화할 수 있는 클러스터링 문제의 최적 결과를 찾는 알고리즘을 제안한다. 일반적으로 네트워크에서 노드의 수가 증가하면 적절한 클러스터 구성을 위한 계산량은 급격히 늘어나게 된다. 본 논문에서는 적정한 시간 내에 최적의 클러스터 구성을 위한 유전 알고리즘을 제안하며, 효율적인 검색을 위해 유전 알고리즘의 효과적인 이웃해 생성 동작을 제안한다. 제안된 알고리즘은 전송 에너지와 실행시간 관점에서 성능을 평가하며, 평가 결과에서 제안된 알고리즘이 해양 센서 네트워크에서 클러스터를 효과적으로 구성함을 보인다.

A Clustering problem is one of the organizational problems to improve network lifetime and scalability in underwater acoustic sensor networks. This paper propose an algorithm to obtain an optimal clustering solution to be able to minimize a total transmission power for all deployed nodes to transmit data to the sink node through its clusterhead. In general, as the number of nodes increases, the amount of calculation for finding the solution would be too much increased. To obtain the optimal solution within a reasonable computation time, we propose a genetic algorithm to obtain the optimal solution of the cluster configuration. In order to make a search more efficient, we propose some efficient neighborhood generating operations of the genetic algorithm. We evaluate those performances through some experiments in terms of the total transmission power of nodes and the execution time of the proposed algorithm. The evaluation results show that the proposed algorithm is efficient for the cluster configuration in underwater acoustic sensor networks.

키워드

참고문헌

  1. K. Akkaya, M. Younis. A survey on routing protocols for wireless sensor networks, Ad hoc networks, 3 2005;325-349. https://doi.org/10.1016/j.adhoc.2003.09.010
  2. H. Karl, A. Willig, Protocols and architectures for wireless sensor networks. Wiley Press, 2005.
  3. W. Heinzelman, A. Chandrakasan, H. Balakrishnan, An Application Specific Protocol Architecture for Wireless Microsensor Networks, IEEE Transactions on Wireless Communications, 1 2002;660-670.
  4. O. Moussaoui, A. Ksentini, M. Naimi, M. Gueroui, A Novel Clustering Algorithm for Efficient Energy Saving in Wireless Sensor Networks, Proceedings of the 7th International Symposium on Computer Networks, 2006; 66-72.
  5. S. Raghuwanshi, A. Mishra, A Self-Adaptive Clustering Based Algorithm for Increased Energy-Efficiency and Scalability in Wireless Sensor Networks, Proceedings of IEEE 58th Vehicular Technology Conference, 5 2003;2921-2925.
  6. O. Younis, S. Fahmy, Distributed Clustering in Ad-hoc Sensor Networks: A Hybrid, Energy-Efficient Approach, Proceedings of IEEE INFOCOM 2004, 2004;29-640.
  7. S. Basagni, Chlamtac, A. Farago, A generalized clustering algorithm for peer-to-peer networks. Proceedings of the Workshop on Algorithmic Aspects of Communication, 1997
  8. T. Kanugo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, A. Y. Wu., A Local Search Approximation Algorithm for k-Means Clustering, Proceedings of the 18th Annual ACM Symp. on Computational Geometry, 2002;10-18.
  9. S. Ghiasi , A. Srivastava , X. Yang, M. Sarrafzadeh, Optimal Energy Aware Clustering in Sensor Networks, Sensors, 2002;258-269.
  10. J. Holland. Adaptation in Natural and Artificial Systems. Univ.of Michigan Press, 1975.
  11. D. E. Goldberg. Genetic Algorithms in Search, Optimization & Machine Learning. Addison Wesley, 1989.
  12. J. Antonisse. A new interpretation of schema notation that overturns the binary encoding constraint. Proceedings of the 3rd International Conference on Genetic Algorithms 1989;86-91.