DOI QR코드

DOI QR Code

Practical Implementation and Stability Analysis of ALOHA-Q for Wireless Sensor Networks

  • Received : 2015.12.01
  • Accepted : 2016.07.04
  • Published : 2016.10.01

Abstract

This paper presents the description, practical implementation, and stability analysis of a recently proposed, energy-efficient, medium access control protocol for wireless sensor networks, ALOHA-Q, which employs a reinforcement-learning framework as an intelligent transmission strategy. The channel performance is evaluated through a simulation and experiments conducted using a real-world test-bed. The stability of the system against possible changes in the environment and changing channel conditions is studied with a discussion on the resilience level of the system. A Markov model is derived to represent the system behavior and estimate the time in which the system loses its operation. A novel scheme is also proposed to protect the lifetime of the system when the environment and channel conditions do not sufficiently maintain the system operation.

Keywords

References

  1. I.F. Akyildiz et al., "A Survey on Sensor Networks," Commun. Mag., vol. 40, no. 8, 2002, pp. 102-114.
  2. I. Demirkol, C. Ersoy, and F. Alagoz, "MAC Protocols for Wireless Sensor Networks: A Survey," Commun. Mag., vol. 44, no. 4, 2006, pp. 115-121.
  3. M.A. Yigitel, O.D. Incel, and C. Ersoy, "QoS-Aware Mac Protocols for Wireless Sensor Networks: A Survey," Comput. Netw., vol. 55, no. 8, 2011, pp. 1982-2004. https://doi.org/10.1016/j.comnet.2011.02.007
  4. Y. Chu, P.D. Mitchell, and D. Grace, "ALOHA and Q-Learning Based Medium Access Control for Wireless Sensor Networks," Int. Symp. Wireless Commun. Syst., Paris, France, Aug. 28-31, 2012, pp. 511-515.
  5. Y. Yan et al., "Distributed Frame Size Selection for Q Learning Based Slotted ALOHA Protocol," Proc. Int. Symp. Wireless Commun. Syst., Ilmenau, Germany, Aug. 2013, pp. 733-737.
  6. R.S. Sutton and A.G. Barto, Reinforcement Learning: An Introduction, Cambridge, MA, USA: MIT Press, 1998.
  7. W. Ye, J. Heidemann, and D. Estrin, "An Energy-Efficient MAC Protocol for Wireless Sensor Networks," Ann. Joint Conf. IEEE Comput. Commun. Soc., New York, USA, 2002, pp. 1567-1576.
  8. T.V. Dam and K. Langendoen, "An Adaptive Energy-Efficient MAC Protocol for Wireless Sensor Networks," Proc. Int. Conf. Embedded Netw. Sensor Syst., Los Angeles, CA, USA, Nov. 5-7, 2003, pp. 171-180.
  9. Z. Liu and I. Elhanany, "RL-MAC: A QoS-Aware Reinforcement Learning Based MAC Protocol for Wireless Sensor Networks," Proc. IEEE Int. Conf. Netw. Sens. Contr., Lauderdale, FL, USA, Apr. 23-25, 2006, pp. 768-773.
  10. W.R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, "Energy-Efficient Communication Protocol for Wireless Microsensor Networks," Proc. Ann. Hawaii Int. Conf. Syst. Sci., Hawaii, HI, USA, Jan. 7, 2000, pp. 1-10.
  11. V. Rajendran, K. Obraczka, and J.J. Garcia-Luna-Aceves, "Energy-Efficient, Collision-Free Medium Access Control for Wireless Sensor Networks," Wireless Netw., vol. 12, no. 1, Feb. 2006, pp. 63-78. https://doi.org/10.1007/s11276-006-6151-z
  12. I. Rhee et al., "Z-MAC: A Hybrid MAC for Wireless Sensor Networks," IEEE/ACM Trans. Netw., vol. 16, no. 3, June 2008, pp. 511-524. https://doi.org/10.1109/TNET.2007.900704
  13. Datasheet for MicaZ wireless measurement system, Accessed Nov. 2015. http://www.openautomation.net/uploadsproductos/micaz_datasheet.pdf
  14. Datasheet for CC2420 IEEE 802.15.4-compliant RF Transceiver, Accessed Nov. 2015. http://www.ti.com/lit/ds/symlink/cc2420.pdf
  15. P. Levis et al., "TinyOS: An Operating System for Wireless Sensor Networks," in Ambient Intelligence, Berlin, Germany: Springer, 2005, pp. 115-148.
  16. N. Abramson, "The Throughput of Packet Broadcasting Channels," IEEE Trans. Commun., vol. 25, no. 1, Jan. 1977, pp. 117-128. https://doi.org/10.1109/TCOM.1977.1093713
  17. J. Zhao and R. Govindan, "Understanding Packet Delivery Performance in Dense Wireless Sensor Networks," Proc. Int. Conf. Embedded Netw. Sensor Syst., Los Angeles, CA, USA, Nov. 5-7, 2003, pp. 1-13.
  18. S. Kosunalp et al., "Practical Implementation Issues of Reinforcement Learning Based ALOHA for Wireless Sensor Networks," Proc. Int. Symp. Wireless Commun. Syst., Ilmenau, Germany, Aug. 27-30, 2013, pp. 360-364.
  19. Y. Chu et al., "Application of Reinforcement Learning to Medium Access Control for Wireless Sensor Networks," Eng. Appl. Artif. Intell., vol. 46, Nov. 2015, pp. 23-32. https://doi.org/10.1016/j.engappai.2015.08.004

Cited by

  1. Efficient Approach for Maximizing Lifespan in Wireless Sensor Networks by Using Mobile Sinks vol.39, pp.3, 2016, https://doi.org/10.4218/etrij.17.0116.0629