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An Energy-Efficient Topology Control Mechanism for Wireless Sensor Networks Based on Transmit Power Adjustment

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Abstract

Adjusting the transmission power of the individual nodes has shown to be an effective topology control approach to improve the performance and to prolong the lifetime of the wireless sensor networks. In this approach, each sensor node dynamically adjusts its radio transmission range to keep the network connected and to reduce the power consumption during transmission as much as possible. In this paper, a learning automata-based method is proposed to adjust the transmit power of the sensor nodes aiming at controlling the network topology. In the proposed method, each node is equipped with a learning automaton, and range of transmission power of the node is defined as the action-set of a continuous automaton. At each stage, depending on the network condition, the learning automaton selects the transmit power consuming the minimum possible power and keeping the network connected. A strong theorem is presented to show the convergence of the proposed method. To show the performance of the proposed method, several simulation experiments are conducted. The obtained results show the superiority of the proposed approach over the existing ones in terms of the transmit power, normalized Signal-to-noise-ratio, control message overhead, and average residual energy level.

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References

  1. Santi, P. (2005). Topology control in wireless ad hoc and sensor networks. ACM Computing Surveys, 37(2), 164–194.

    Article  Google Scholar 

  2. Jones, C. E., Sivalingam, K. M., Agrawal, P., & Chen, J. C. (2001). A survey of energy efficient network protocols for wireless networks. Wireless Networks, 7(4), 343–358.

    Article  MATH  Google Scholar 

  3. Ramanathan, R., & Rosales-Hain, R. (2000). Topology control of multihop wireless networks using transmit power adjustment. In Proceedings of the nineteenth annual joint conference of the IEEE computer and communications societies, Vol. 2, Tel Aviv, Israel, (pp. 404–413).

  4. Wightman, P. M., & Labrador, M. A. (2011). A family of simple distributed minimum connected dominating set-based topology construction algorithms. Journal of Network and Computer Applications, 34, 1997–2010.

    Article  Google Scholar 

  5. Qureshi, H. K., Rizvi, S., Saleem, M., Khayam, S. A., Rakocevic, V. & Rajarajan, M. (2012). Evaluation and improvement of CDS-based topology control for wireless sensor networks. Wireless Networks, doi:10.1007/s11276-012-0449-9.

  6. Ya, X., Heidemann, J., & Estrin, D. (2001). Geography-informed energy conservation for ad hoc routing. In Proceedings of annual international conference on mobile computing and networking, (pp. 70–84).

  7. Schurgers, C., Tsiatsis, V., & Srivastava, M. B. (2002). STEM: Topology management for energy efficient sensor networks. In Proceedings of IEEE aerospace conference, Vol. 3., ( pp. 1099–1108).

  8. Cerpa, A., & Estrin, D. (2004). Ascent: Adaptive self-configuring sensor networks topologies. IEEE Transactions on Mobile Computing, 3(3), 272–285.

    Article  Google Scholar 

  9. Li, L., Halpern, J. Y., Bahl, P., Wang, Y. M., & Wattenhofer, R. (2005). A cone-based distributed topology-control algorithm for wireless multi-hop networks. IEEE/ACM Transactions on Networking, 13(1), 147–159.

    Article  Google Scholar 

  10. Hu, L. (1993). Topology control for multi-hop packet radio networks. Transactions on Communications, 41(10), 1474–1481.

    Article  MATH  Google Scholar 

  11. Lloyd, E. L., Liu, R., Marathe, M. V., Ramanathan, R., & Ravi, S. S. (2005). Algorithmic aspects of topology control problems for ad hoc networks. Mobile Networks and Applications, 10(1), 19–34.

    Article  Google Scholar 

  12. Rodoplu, V., & Meng, T. H. (1999). Minimum energy mobile wireless networks. IEEE Journal on Selected Areas in Communications, 17(8), 1333–1344.

    Article  Google Scholar 

  13. Li, L., & Halpern, J. Y. (2001). Minimum-energy mobile wireless networks revisited. In Proceedings of IEEE international conference on communications, (pp. 278–283).

  14. Matsui, G., Tachibana, T., Nakamura, Y., & Sugimoto, K. (2013). Distributed power adjustment based on control theory for cognitive radio networks. Computer Networks, 57, 3344–3356.

    Article  Google Scholar 

  15. Li, D., Du, H., Liu, L., & Huang, S. C. H. (2008). Joint topology control and power conservation for wireless sensor networks using transmit power adjustment. Computing and Combinatorics, Lecture Notes in Computer Science, 5092, 541–550.

    Google Scholar 

  16. Narayanaswamy, S., Kawadia, V., Sreenivas, R. S., & Kumar, P. R. (2002). Power control in ad-hoc networks: Theory, architecture, algorithm and implementation of the COMPOW protocol. European Wireless Conference, 2002, 156–162.

    Google Scholar 

  17. Yang, M., & Grace, D. (2011). Cognitive radio with reinforcement learning applied to multicast downlink transmission with power adjustment. Wireless Personal Communications, 57(1), 73–87.

    Article  Google Scholar 

  18. Cheng, S.-T., & Wu, M. (2009). Optimization of multilevel power adjustment in wireless sensor networks. Telecommunication Systems, 42(1–2), 109–121.

    Article  MathSciNet  Google Scholar 

  19. Chevillat, P., Jelitto, J., & Truong, H. L. (2005). Dynamic data rate and transmit power adjustment in IEEE 802.11 wireless LANs. International Journal of Wireless Information Networks, 12(3), 123–145.

    Article  Google Scholar 

  20. Bao, L., & Garcia-Luna-Aceves, J. J. (2003). Topology management in ad hoc networks. In Proceedings of 4th ACM international symposium on mobile ad hoc networking and computing,(pp. 129–140).

  21. Yuanyuan, Z., Jia, X., & Yanxiang, H. (2008). Energy efficient distributed connected dominating sets construction in wireless sensor networks. In Proceedings of international conference on wireless communications and mobile computing, (pp. 797–802).

  22. Chen, B., Jamieson, K., Balakrishnan, H., & Morris, R. (2002). Span: An energy-efficient coordination algorithm for topology maintenance in ad hoc wireless networks. Wireless Networks, 8(5), 481–494.

    Article  MATH  Google Scholar 

  23. Vikas, K., & Kumar, P. R. (2003). Power control and clustering in ad hoc networks. In Proceedings of the 23th annual joint conference of the IEEE computer and communications, Vol. 1,(pp. 459–469).

  24. Narendra, K. S., & Thathachar, M. A. L. (1989). Learning automata: An introduction. New York: Prentice-Hall.

    Google Scholar 

  25. Thathachar, M. A. L., & Harita, B. R. (1987). Learning automata with changing number of actions. IEEE Transactions on Systems, Man, and Cybernetics, SMG17, 1095–1100.

    Article  Google Scholar 

  26. Santharam, G., Sastry, P. S., & Thathachar, M. A. L. (1994). Continuous action set learning automata for stochastic optimization. Journal of Franklin Institute, 331B(5), 607–628.

    Article  MATH  MathSciNet  Google Scholar 

  27. Howell, M. N., Frost, G. P., Gordon, T. J., & Wu, Q. H. (1997). Continuous action reinforcement learning applied to vehicle suspension control. Mechatronics, 7(3), 263–276.

    Article  Google Scholar 

  28. Gullapalli, V. (1990). A stochastic reinforcement learning algorithm for learning real-valued functions. Neural Networks, 3, 671–692.

    Article  Google Scholar 

  29. Gullapalli, V. (Dec. 1996). Associative reinforcement learning of real-valued functions. Tech. Rep. 90–129, Departement of Computer and Information Sciences, University of Massachusetts, Amherst, MA, USA.

  30. Gullapalli, V. (Feb. 1992). Reinforcement learning and its application on control. Ph.D. thesis, Department of Computer and Information Sciences, University of Massachusetts, Amherst, MA, USA.

  31. Vasilakos, A., & Loukas, N. H. (1996). ANASA-a stochastic reinforcement algorithm for real-valued neural computation. IEEE Transactions on Neural Networks, 7, 830–842.

    Article  Google Scholar 

  32. Vasilakos, A., Loukas, N. H., & Zikidis, K. (1993). Adaptive stochastic algorithm for Fuzzy computing / function etimation. In Proceedings of 1993 IEEE international joint conference on neural networks, (pp. 1417–1420).

  33. Doob, J. L. (1953). Stochastic processes. New York: Wiley.

    MATH  Google Scholar 

  34. Heinzelman, W., Chandrakasan, A. P., Balakrishnan, H. (2000). Energy-efficient communication protocols for wireless microsensor networks. In Proceedings of the 33rd Hawaii international conference on system sciences.

  35. IEEE Computer Society LAN MAN Standards Committee (1997). Wireless LAN medium access protocol (MAC) and physical layer (PHY) specification, IEEE Standard 802.11-1997, The Institute of Electrical and Electronics Engineers, New York.

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Authors and Affiliations

  1. Department of Computer Engineering, Arak Branch, Islamic Azad University, Arak, Iran

    Javad Akbari Torkestani

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  1. Javad Akbari Torkestani
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Akbari Torkestani, J. An Energy-Efficient Topology Control Mechanism for Wireless Sensor Networks Based on Transmit Power Adjustment. Wireless Pers Commun 82, 2537–2556 (2015). https://doi.org/10.1007/s11277-015-2363-9

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  • DOI: https://doi.org/10.1007/s11277-015-2363-9

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