The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Design of Distributed Node Scheduling Scheme Inspired by Gene Regulatory Networks for Wireless Sensor Networks

무선 센서 망에서 생체 유전자 조절 네트워크를 모방한 분산적 노드 스케줄링 기법 설계

  • Byun, Heejung (Suwon University Department of Information & Telecommun. Engineering)
  • Received : 2015.09.01
  • Accepted : 2015.10.21
  • Published : 2015.10.31
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Abstract

Biologically inspired modeling techniques have received considerable attention for their robustness, scalability, and adaptability with simple local interactions and limited information. Among these modeling techniques, Gene Regulatory Networks (GRNs) play a central role in understanding natural evolution and the development of biological organisms from cells. In this paper, we apply GRN principles to the WSN system and propose a new GRN model for decentralized node scheduling design to achieve energy balancing while meeting delay requirements. Through this scheme, each sensor node schedules its state autonomously in response to gene expression and protein concentration, which are controlled by the proposed GRN-inspired node scheduling model. Simulation results indicate that the proposed scheme achieves superior performance with energy balancing as well as desirable delay compared with other well-known schemes.

최근 생물학적으로 영감을 받은 모델링 기술은 단순한 현장 상호작용과 제한된 정보와 함께 이들의 강인성과 확장성, 적응성에 대해 상당한 관심을 받고 있다. 이러한 모델링 기술들 중, 유전자 조절 네트워크(Gene Regulatory Networks)(GRNs)은 세포로부터 생물학적 유기체의 발생과 자연 진화에 대한 이해에서 핵심적인 역할을 하고 있다. 본 논문은 GRN 원리를 무선 센서 네트워크 시스템에 적용하고 시간지연 요건을 충족하는 동시에 에너지 균형을 달성할 수 있는 분산화된 노드 스케쥴링 설계 기법을 제안한다. 각 센서 노드는 소비된 에너지 수준과 지연시간에 반응하여 자동으로 자신의 상태를 스케줄링하며, 이는 GRN 모델에서 영감을 받은 유전자 발현과 단백질 농도 조절 모델에 의해 제어된다. 시뮬레이션 결과는 제안된 방법이 에너지 균형뿐만 아니라 원하는 시간 지연에서 성능을 달성하고 있다는 점을 보여준다.

Keywords

References

  1. A. Bachir, M. Dohler, T. Watteyne, and K. K. Leung, "MAC essentials for wireless sensor networks," IEEE Commun. Surveys & Tutorials, vol. 12, no. 2, pp. 222-248, 2010. https://doi.org/10.1109/SURV.2010.020510.00058
  2. R. C. Luo and O, Chen, "Mobile sensor node deployment and asynchronous power management for wireless sensor networks," IEEE Trans. Ind. Electron., vol. 59, no. 5, pp. 2377-2385, 2012. https://doi.org/10.1109/TIE.2011.2167889
  3. X. Wang, G. Xing, and Y. Yao, "Dynamic duty cycle control for end-to-end delay guarantees in wireless sensor networks," IWQoS, pp. 1-9, Beijing, China, Jun. 2010.
  4. F. Dressler and O. B. Akan, "A survey on bio-inspired networking," Computer Netw., vol. 54, no. 6, pp. 881-900, 2010. https://doi.org/10.1016/j.comnet.2009.10.024
  5. T. Nakano, "Biologically inspired network systems: A review and future prospects," IEEE Trans. Syst., Man, and Cybernetics -Part C: Appl. and Rev., vol. 41, no. 5, pp. 630-643, 2011. https://doi.org/10.1109/TSMCC.2010.2090141
  6. C. Charalambous and S. Cui, "A biologically inspired networking model for wireless sensor networks," IEEE Netw., vol. 24, no. 3, pp. 6-13, 2010. https://doi.org/10.1109/MNET.2010.5464221
  7. K. Leibnitz and M. Murata, "Attractor selection and perturbation for robust networks in fluctuating environments," IEEE Netw., vol. 24, no. 3, pp. 14-18, 2010. https://doi.org/10.1109/MNET.2010.5464222
  8. C. Cheng, C. K. Tse, and F. C. M. Lau, "A bio-inspired scheduling scheme for wireless sensor networks," IEEE Veh. Technol. Conf., pp. 223-227, 2008.
  9. L. T. Macneil and A. J. Walhout, "Gene regulatory networks and the role of robustness and stochasticity in the control of gene expression," Genome Res., vol. 21, pp. 645-657, 2011. https://doi.org/10.1101/gr.097378.109
  10. R. J. Prill, P. A, Iglesias, and A. Levchenko, "Dynamic properties of network motifs contribute to biological network organization," Public Library Sci. Biology, vol. 3, no. 11, pp. 1881-1892, 2005.
  11. A. Nazi, M. Raj, M. D. Francesco, G. Preetan, and S. K. Das, "Robust deployment of wireless sensor networks using gene regulatory networks," Distrib. Comput. and Netw., LNCS, vol. 7730, pp. 192-207, 2013.
  12. S. Das, P. Koduru, X. Cai, S. Welch, and V. Sarangan, "The gene regulatory network: an application to optimal coverage in sensor networks," 10th Annu. Conf. Genetic and Evolutionary Computation, pp. 1461-1468, 2008.
  13. A. Markham and N. Trigoni, "Discrete gene regulatory networks (dGRNs): a novel approach to configuring sensor networks," in Proc. INFOCOM'10, pp. 1-9, San Diego, CA, Mar. 2010.
  14. A. Markham and N. Trigoni, "The automatic evolution of distributed controller to configure sensor networks," Oxford Computer J., vol. 54, no. 3, pp. 421-438, 2011. https://doi.org/10.1093/comjnl/bxq016
  15. T. Taylor, "A genetic regulatory networkinspired real-time controller for a group of underwater robots," in Proc. 8th Conf. Intell. Autonomous Syst., vol. 8, 2004.
  16. P. Ghosh, M. Mayo, V. Chaitankar, T. Habib, and S. Das, "Principles of genomic robustness inspire fault-tolerant WSN topologies: a network science based case study," 7th IEEE Int. Wkshps Sensor Netw. Syst. Pervasive Comput., pp. 160-165, Seattle, WA, Mar. 2011.
  17. S. Nolfi and D. Floreano, Evolutionary robotics: the biology, intelligence and technology of self-organizing machine, MIT Press, 2000.
  18. Y. Jin and B. Sendhoff, "Evolving in silico bistable and oscillatory dynamics for gene regulatory network motifs," IEEE/CEC Congress on Evolutionary Computation, pp. 386-391, Hong Kong, Jun. 2008.
  19. E. Mojlsness, D. H. Sharp, and J. Reinitz, "A connectionist model of development," J. Theor. Biol., vol. 152, no. 4, pp. 429-453, Oct. 1991. https://doi.org/10.1016/S0022-5193(05)80391-1
  20. H. Guo, Y. Meng, and Y. Jin, "A cellular mechanism for multirobot construction via evolutionary multi-objective optimization of a gene regulatory network," BioSytems, vol. 98, no. 3, pp. 193-203, 2009. https://doi.org/10.1016/j.biosystems.2009.05.003
  21. Y. Jin, H. Guo, and Y. Meng, "Robustness analysis and failure recovery of a bio-inspired self-organizing multi-robot system," The 3rd IEEE Int. Conf. Self-Adaptive and Self Organizing Syst., pp. 154-164, San Francisco, CA, Sept. 2009.