Enhanced energy conditioned mean square error algorithm for wireless sensor networks

Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 2)

Abstract

Wireless Sensor Networks (WSNs) have found numerous applications in control and monitoring fields. Advancements in the field of electronics have made wireless sensors economical enough to be widely used. WSNs have found wide applications in defence, agriculture, seismic monitoring, health sector, urban area monitoring, etc. The battery life of nodes in such networks is a constraint. Routing algorithms chosen for WSNs should make sure that energy consumption of nodes is minimized. Geographic routing is one of the options. It can be used in large scale networks owing to its low energy consumption properties. It also gives low overhead. Geographic routing comes with an inherent defect of location errors. Location errors impair the performance of geographic routing. In this paper a protocol Enhanced Energy Conditioned Mean Square Error Algorithm (E-ECMSE) is proposed that copes with the location errors of geographic routing and hence shows a fair increase in the packet delivery ratio of the network and a decrease in the energy consumption. The number of hops in the network are controlled which directly reduce the energy consumption.

Keywords

Sensor Network Wireless Sensor Network Source Node Cluster Head Destination Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Maghsoudlou, A., St-Hilaire, M., & Kunz, T. (2011). “A survey on geographic routing protocolsGoogle Scholar
  2. 2.
    for mobile ad hoc networks”. Systems and Computer Engineering, Technical Report SCE-11-03.Carleton University.2011.49 p.Google Scholar
  3. 3.
    Ruhrup, S. (2009). “Theory and practice of geographic routing”. Ad Hoc and Sensor Wireless Networks: Architectures, Algorithms and Protocols, 69.Google Scholar
  4. 4.
    Seada, K., Helmy, A., & Govindan, R. (2004, April). “On the effect of localization errors on geographic face routing in sensor networks”. In Proceedings of the 3rd international symposium on Information processing in sensor networks (pp. 71-80). ACM.Google Scholar
  5. 5.
    Shah, R. C., Wolisz, A., & Rabaey, J. M. (2005, May). “On the performance of geographical routing in the presence of localization errors [ad hoc network applications]”. In IEEE International Conference on Communications, 2005. ICC 2005. 2005 (Vol. 5, pp. 2979-2985). IEEE.Google Scholar
  6. 6.
    Takagi, H., & Kleinrock, L. (1984). “Optimal transmission ranges for randomly distributed packet radio terminals. IEEE Transactions on communications ”, 32(3), 246-257.Google Scholar
  7. 7.
    Peng, B., & Kemp, A. H. (2011). “Energy-efficient geographic routing in the presence of localization errors”. Computer Networks, 55(3), 856-872.Google Scholar
  8. 8.
    Popescu, A. M., Salman, N., &Kemp, A. H. (2014). “Energy efficient geographic routing robust against location errors”. IEEE Sensors Journal, 14(6), 1944-1951.Google Scholar
  9. 9.
    Kim, Y., Lee, J. J., & Helmy, A. (2004). “Modeling and analyzing the impact of location inconsistencies on geographic routing in wireless networks”. ACM SIGMOBILE Mobile Computing and Communications Review, 8(1), 48-60Google Scholar
  10. 10.
    Yu, Y., Govindan, R., & Estrin, D. (2001). “Geographical and energy aware routing: A recursive data dissemination protocol for wireless sensor networks”.Google Scholar
  11. 11.
    Zeng, K., Ren, K., Lou, W., & Moran, P. J. (2009). “Energy aware efficient geographic routing in lossy wireless sensor networks with environmental energy supply”. Wireless Networks, 15(1), 39-51.Google Scholar
  12. 12.
    Sanchez, J. A., Ruiz, P. M., Liu, J., & Stojmenovic, I. (2007). “Bandwidth-efficient geographic multicast routing protocol for wireless sensor networks”. IEEE Sensors Journal, 7(5), 627-636.Google Scholar
  13. 13.
    Zhang, H., & Shen, H. (2010). “Energy-efficient beaconless geographic routing in wireless sensor networks”. IEEE transactions on parallel and distributed systems, 21(6), 881-896.Google Scholar
  14. 14.
    Akbar, M., Javaid, N., Khan, Z. A., Qasim, U., Alghamdi, T. A., Mohammad, S. N., … & Bouk, S. H. (2015). “Towards network lifetime maximization: sink mobility aware multihop scalable hybrid energy efficient protocols for Terrestrial WSNs”. International Journal of Distributed Sensor Networks, 2015, 10.Google Scholar
  15. 15.
    Latif, K., Javaid, N., Saqib, M. N., Khan, Z. A., Qasim, U., Mahmood, B., & Ilahi, M. (2015). “Energy hole minimization with field division for energy efficient routing in WSNs”. International Journal of Distributed Sensor Networks, 2015, 12.Google Scholar
  16. 16.
    Latif, K., Javaid, N., Saqib, M. N., Khan, Z. A., & Alrajeh, N. (2016). “Energy consumption model for density controlled divide-and-rule scheme for energy efficient routing in wireless sensor networks”. International Journal of Ad Hoc and Ubiquitous Computing, 21(2), 130-139Google Scholar
  17. 17.
    Popescu, A. M., Salman, N., & Kemp, A. H. (2013). “Geographic routing resilient to location errors”. IEEE Wireless Communications Letters, 2(2), 203-206.Google Scholar
  18. 18.
    Kadi, M., & Alkhayat, I. (2015). “The effect of location errors on location based routing protocols in wireless sensor networks”. Egyptian Informatics Journal, 16(1), 113-119.Google Scholar
  19. 19.
    Melodia, T., Pompili, D., & Akyildiz, I. F. (2004, March). “Optimal local topology knowledge for energy efficient geographical routing in sensor networks”. In INFOCOM 2004. Twentythird AnnualJoint Conference of the IEEE Computer and Communications Societies (Vol. 3, pp. 1705-1716). IEEE.Google Scholar
  20. 20.
    Heinzelman,W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). “An application-specific protocol architecture for wireless microsensor networks”. IEEE Transactions on wireless communications, 1(4), 660-670.Google Scholar
  21. 21.
    Salman, N., Ghogho, M., & Kemp, A. H. (2014). “Optimized low complexity sensor node positioning in wireless sensor networks”. IEEE Sensors Journal, 14(1), 39-46.Google Scholar
  22. 22.
    Radulescu, V. (2008). “Rodrigues-type formulae for Hermite and Laguerre polynomials”. An. St. Univ. Ovidius Constanta, 16, 109-116.Google Scholar
  23. 23.
    Kreh, M. (2012). “Bessel functions”. Lecture Notes, Penn State-Gttingen Summer School on Number Theory, 82.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Space TechnologyIslamabadPakistan
  2. 2.COMSATS Institute of Information TechnologyIslamabadPakistan

Personalised recommendations