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Energy-Efficient Wireless Sensor’s Routing Using Balanced Unequal Clustering Technique

  • Mallika MhatreEmail author
  • Anoop Kumar
  • C. K. Jha
Chapter

Abstract

From last few years, advance researches in wireless sensor network have been extensively taken place. One of the major challenges in WSN is to prolong sensor node’s operational lifetime. For this, many clustering algorithms have been proposed that provide an effective way to improve energy efficiency. However, they rarely consider the position of base station and hot-spot problem during multihop routing. To solve such routing layer problem, we propose an energy-efficient routing in unequal clustering (EERUC) technique. This technique starts with preparation phase in which base station plays important role in deciding prerequired parameter like optimal probability threshold by applying genetic algorithm on node’s geographical position and residual energy. We have fixed base station location in the middle of sensor network which balances energy consumption load equally among clusters. During setup phase, final CHs are selected based on internal competition between temporary CHs whose competitive radius range intercepts with each other. In this technique, distance factor and node’s residual energy are considered as important clustering parameters. These parameters are normalized to produce different competitive radii of CHs as normalization provides better selection of radius in comparison with existing approach. Our novel approach retains unequal size cluster for few rounds and cluster head selection will rotate within a cluster for every round. This method effectively reduces clustering overhead by balancing energy consumption of network. The results show that the proposed technique improves network lifetime as compared to existing techniques.

Keywords

EERUC Hot-spot problem Multihop routing Normalization Clustering overhead Network lifetime 

References

  1. 1.
    Li, C., Ye, M., & Chen, G. (2005). An energy-efficient unequal clustering mechanism for wireless sensor networks. IEEE.Google Scholar
  2. 2.
    Moschitto, A., & Igor, N. Power consumption assessment in WSN. InTech.Google Scholar
  3. 3.
    Gupta, V., & Pandey, R. (2016). An improved energy aware distributed unequal clustering protocol for heterogeneous wireless sensor networks. Engineering Science and Technology, An International Journal, 1050–1058 (Elsevier).CrossRefGoogle Scholar
  4. 4.
    Mammu, A. S. K., Sharma, A., Hernandez-Jayo, U., & Sainz, N. (2013). A novel cluster based Energy-efficient routing in wireless sensor network. In IEEE 27th International Conference on Advanced Information Networking and Communication. IEEE.Google Scholar
  5. 5.
    Chaubey, N. K., & Dharti, H. P. (2016). Energy efficient clustering algorithm for decreasing energy consumption and delay in wireless sensor network. International Journal of Innovative Research in Computer and communication Engineering, 4.Google Scholar
  6. 6.
    Rajeshwari, P., Shanthini, B., & Prince, M. (2015). Hierarchical energy efficient clustering algorithm for WSN. Middle-East Journal of Scientific Research, Sensing, Signal Processing and Security, 108–117.Google Scholar
  7. 7.
    Zhang, D., Ki, G., et al. (2014). An energy-balanced routing method based on forward aware factor for wireless sensor networks. IEEE Transactions on Industrial Informatics, 10(1) (IEEE).CrossRefGoogle Scholar
  8. 8.
    Meng, J.-T., Yuan, J.-R., et. al. (2013). An energy efficient clustering scheme for data aggregation in wireless sensor networks. Journal of Computer Science & Technology, 564–573.MathSciNetCrossRefGoogle Scholar
  9. 9.
    Liu, J.-L., & Chinya, V. R. (2011). LEACH-GA: Genetic algorithm-based energy-efficient adaptive clustering protocol for wireless sensor networks. International Journal of Machine Learning and Computing, 1(1).Google Scholar
  10. 10.
    Pal, V., Yogita, Girdhari S., & Yadav, R. P. (2015). Cluster head selection optimization based on genetic algorithm to prolong lifetime of wireless sensor networks. In Third International Conference on Recent Trends in Computing (pp. 1417–1423). Elsevier.Google Scholar
  11. 11.
    Mohammad, K., Hamid R. N., & Shahrzad, G. (2012). Optimizing cluster-head selection in wireless sensor networks using genetic algorithm and harmony search algorithm. In 20th Iranian Conference on Electrical Engineering.Google Scholar
  12. 12.
    Kaushik, A. K. (2016). A hybrid approach of fuzzy C-means clustering and neural network to make energy-efficient heterogeneous wireless sensor network. International Journal of Electrical and Computer Engineering, 6(2), 674–681.Google Scholar
  13. 13.
    Uma Maheshwari, S., Pushpalatha, S. (2014). Cluster head selection based on genetic algorithm using AHYMN approaches in WSN. International Conference on Innovations in Engineering and Technology, 3.Google Scholar
  14. 14.
    Lanzisera, S., Mehta, A., & Pister, K. S. (2009). Reducing average power in wireless sensor network through data rate adaptation. IEEE.Google Scholar
  15. 15.
    Kaur, A., & Amit G. (2015). LEACH and extended LEACH protocols in wireless sensor network-A survey. International Journal of Computer Applications, 116(10).CrossRefGoogle Scholar
  16. 16.
    Reenkamal, K. G., Priya, C., & Monika S. (2014). Study of LEACH routing protocol for wireless sensor networks. In International Conference on Communication, Computing & Systems.Google Scholar
  17. 17.
    Patil, P., Umakant K., & Ayachit, N. H. (2011). Some issues in clustering algorithms for wireless sensor networks. In 2nd National Conference-Computing, Communication and Sensor Network.Google Scholar
  18. 18.
    Lee, S., Lee, J., & Hongjong, S. (2011). An energy-efficient distributed unequal clustering protocol for heterogeneous wireless sensor network. International Journal of Distributed Sensor Network, 1050–1058 (Elsevier).Google Scholar
  19. 19.
    Heinzelman, W., Chandrakasan, A., & Balakrishanan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transaction on Wireless Communications, 1(4), 660–670 (IEEE).CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceBanasthali UniversityVanasthaliIndia

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