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Reseach Article

Joint Adaptive Modulation and Adaptive MAC Protocols for Wireless Sensor Networks

by Xiao Zhao, Elyes Bdira, Mohamed Ibnkahla
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 85 - Number 19
Year of Publication: 2014
Authors: Xiao Zhao, Elyes Bdira, Mohamed Ibnkahla
10.5120/15098-3492

Xiao Zhao, Elyes Bdira, Mohamed Ibnkahla . Joint Adaptive Modulation and Adaptive MAC Protocols for Wireless Sensor Networks. International Journal of Computer Applications. 85, 19 ( January 2014), 32-39. DOI=10.5120/15098-3492

@article{ 10.5120/15098-3492,
author = { Xiao Zhao, Elyes Bdira, Mohamed Ibnkahla },
title = { Joint Adaptive Modulation and Adaptive MAC Protocols for Wireless Sensor Networks },
journal = { International Journal of Computer Applications },
issue_date = { January 2014 },
volume = { 85 },
number = { 19 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 32-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume85/number19/15098-3492/ },
doi = { 10.5120/15098-3492 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:02:55.619639+05:30
%A Xiao Zhao
%A Elyes Bdira
%A Mohamed Ibnkahla
%T Joint Adaptive Modulation and Adaptive MAC Protocols for Wireless Sensor Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 85
%N 19
%P 32-39
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper introduces cognitive MAC-layer techniques to wireless sensor networks (WSN) to optimize Network survivability. We compare Adaptive Modulation (AM) over flat-fading channels, with data rate and transmit power being varied according to channel conditions with two variants: Adaptive Modulation with Idle mode (AMI) and a new Adaptive Sleep with Adaptive Modulation (ASAM) which dynamically adjusts the transmission and sleep modes based shared global information on channel conditions. These introduced cognitive methods assume power allocation schemes that improve energy efficiency and this node life assuming multi-hop relay networks. Simulation results indicate that a notable reduction in energy consumption can be achieved by jointly adapting the data rate and the transmit power in WSNs. The proposed ASAM algorithm can considerably improve node lifetime compared to AM and AMI. The optimal power control values and optimal power allocation factors are further considered for multi-hop relay networks, respectively, thus reducing the need for higher layer network protocols in local switching.

References
  1. S. G. Cui, et al. , "Energy-constrained modulation optimization," IEEE Transactions on Wireless Communications, vol. 4, pp. 2349-2360, Sep 2005.
  2. S. Phoha, et al. , Sensor network operations. Hoboken, N. J. and Piscataway, N. J. Wiley; IEEE Press, 2006.
  3. T. Yan, et al. , "Design and optimization of distributed sensing coverage in wireless sensor networks," ACM Trans. on Embedded Computing Systems, vol. 7, 2008.
  4. I. F. Akyildiz, et al. , "Wireless multimedia sensor networks: A survey," IEEE Wireless Communications, vol. 14, pp. 32-39, Dec 2007.
  5. A. Goldsmith, Wireless communications. Cambridge, New York: Cambridge University Press, 2005.
  6. M. S. Alouini and A. J. Goldsmith, "Adaptive modulation over Nakagami fading channels," Wireless Personal Communications, vol. 13, pp. 119-143, May 2000.
  7. E. Bdira & M. Ibnkahla, "Performance Modeling of Cognitive Wireless Sensor Networks Applied to Environmental Protection," IEEE GLOBECOM, (2009 pp. 1-6).
  8. A. J. Goldsmith and P. P. Varaiya, "Capacity of fading channels with channel side information," IEEE Transactions on Information Theory, vol. 43, pp. 1986-1992, Nov 1997.
  9. T. Ue, et al. , "Symbol rate and modulation level-controlled adaptive modulation TDMA TDD system for high-bit-rate wireless data transmission," IEEE Trans. Veh. Technology, vol. 47, pp. 1134-1147, Nov 1998.
  10. Q. W. Liu, et al. , "Cross-layer combining of adaptive modulation and coding with truncated ARQ over wireless links," IEEE Transactions on Wireless Communications, vol. 3, pp. 1746-1755, Sep 2004.
  11. B. Classon, et al. , "Channel coding for 4G systems with adaptive modulation and coding," IEEE Wireless Communications, vol. 9, pp. 8-13, Apr 2002.
  12. S. Nanda, et al. , "Adaptation techniques in wireless packet data services," IEEE Communications Magazine, vol. 38, pp. 54-64, Jan 2000.
  13. A. Y. Alemdar, "Link Adaptation For Energy Constrained Networks," Master of Science (Engineering), Department of Electrical and Computer Engineering, Queen's University, Kingston, 2008.
  14. B. M. Sadler, "Fundamentals of energy-constrained sensor network systems," IEEE Aerospace and Electronic Systems Magazine, vol. 20, pp. 17-35, 2005.
  15. Y. T. Hou, et al. , "On energy provisioning and relay node placement for wireless sensor networks," IEEE Transactions on Wireless Communications, vol. 4, pp. 2579-2590, Sep 2005.
  16. J. Van Greunen, et al. , "Adaptive Sleep Discipline for Energy Conservation and Robustness in Dense Sensor Networks," presented at the 2004 IEEE International Conference on Communications, 2004.
  17. P. Agrawal and N. Patwari, "Correlated Link Shadow Fading in Multi-Hop Wireless Networks," IEEE Transactions on Wireless Communications, vol. 8, pp. 4024-4036, Aug 2009.
  18. M. Ilyas and I. Mahgoub, Handbook of sensor networks : compact wireless and wired sensing systems. Boca Raton: CRC Press, 2005.
  19. D. P. J. Van Greunen, A. Bonivento, J. Rabaey, K. Ramchandran, A. S. Vincentelli, A. S. ; , "Adaptive Sleep Discipline for Energy Conservation and Robustness in Dense Sensor Networks," presented at the IEEE International Conference on Communications, 2004
Index Terms

Computer Science
Information Sciences

Keywords

Cognitive WSNs Adaptive Modulation Cross-layer protocols