Prolonged Network Lifetime to Reduce Energy Consumption Using Cluster-Based Wireless Sensor Network

  • Sagargouda S. Patil
  • Anand GudnavarEmail author
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 77)


Efficient utilization of energy for each sensor node is the key to prolong the network lifetime. The balance of energy consumption among the nodes located at central and edge areas also plays another important role in the situation of lifetime extension. In earlier work, LEACH algorithm has been investigated in the existing cluster head selection and the edge sub-clustering scheme is further developed to ease the effect of non-uniform node distribution on the edge, but it has not covered entire coverage area of the network due to failure of some nodes. The proposed work uses partition around medoid algorithm to improve optimization of number and size of sub-clusters along with the communication range for better improvement in cluster head selection and overcoming failure of edge nodes. Thus, it not only improves the energy conservation but also drastically improves the balance energy consumption, which can contribute to longer network lifetime and outage probability.


Clustering Energy consumption Node connectivity Network lifetime Sensor node 


  1. 1.
    Takaishi, D., Nishiyama, H., Kato, N., Miura, R.: Towards energy efficient big data gathering in densely distributed sensor networks. IEEE Trans. Emerg. Top. Comput. 2(3), 388–397 (2014)CrossRefGoogle Scholar
  2. 2.
    Shah, S.H., Khan, F.K., Ali, W., Khan, J.: A new framework to integrate wireless sensor networks with cloud computing. In: IEEE Aerospace Conference, March 2013, pp. 1–6 (2013)Google Scholar
  3. 3.
    Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: A survey on sensor networks. IEEE Commun. Mag. 40(8), 102–114 (2002)CrossRefGoogle Scholar
  4. 4.
    Tao, Y., Zheng, Y.: The combination of the optimal number of cluster-heads and energy adaptive cluster-head selection algorithm in wireless sensor networks. In: International Conference on Wireless Communications, Networking and Mobile Computing, Wuhan, China, pp. 1–4 (2006)Google Scholar
  5. 5.
    Chan, T.J., Chan, M.U., Huang, Y.F., Lin, J.Y., Chen, T.R.: Optimal cluster number selection in ad-hoc wireless sensor networks. WTOC 7(8), 837–846 (2008)Google Scholar
  6. 6.
    Dorsey, D.J., Kam, M.: Non-uniform deployment of nodes in clustered wireless sensor networks. In: 43rd Annual Conference on Information Sciences and Systems, (CISS), Baltimore, MD, USA, pp. 823–828 (2009)Google Scholar
  7. 7.
    Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: An application specific protocol architecture for wireless microsensor networks. IEEE Trans. Wirel. Commun. 1(4), 660–670 (2002)CrossRefGoogle Scholar
  8. 8.
    Tripathi, R.K., Singh, Y.N., Verma, N.K.: Two-tiered wireless sensor networks-base station optimal positioning case study. IET Wirel. Sens. Syst. 2(4), 351–360 (2012)CrossRefGoogle Scholar
  9. 9.
    Mathapati, B.S., Patil, S.R., Mytri, V.D.: Energy efficient cluster based mobility prediction for wireless sensor networks. In: IEEE International Conference on Circuits, Power and Computing Technologies (ICCPCT), (2013)Google Scholar
  10. 10.
    Elbhiri, B., El Fkihi, S., Saadane, R.: A new spectral classification for robust clustering in wireless sensor networks. In: IFIP WMNC (2013)Google Scholar
  11. 11.
    Jawad Ali, S., Roy, P.: Energy saving methods in wireless sensor networks. In: IDE0814, (May 2008)Google Scholar
  12. 12.
    Saini, M., Saini, R.K.: Solution of energy-efficiency of sensor nodes in wireless sensor networks. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(5) (2013)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringShaikh College of Engineering and TechnologyBelagaviIndia

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