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Maximizing Network Lifetime of Wireless Sensor Networks: An Energy Harvesting Approach

  • Srikanth Jannu
  • Prasanta K. Jana
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 395)

Abstract

Energy preservation is very crucial in wireless sensor networks as they are operated in hostile and non-accessible areas. The use of renewable energy sources is an alternative technique for extending lifetime of a sensor network where the battery-driven sensor nodes run out of battery power faster. In this paper, we study and solve the problem of extending network lifetime by introduce energy-harvesting (EH) sensor nodes and propose a clustering algorithm to extend the network lifetime. In the proposed algorithm, we present an efficient scheme for cluster head selection by considering the locations of EH sensor nodes and all of these EH sensor nodes serve as relay nodes to the cluster heads. Simulation results and their theoretical analysis show that the proposed algorithm outperforms the existing algorithm.

Keywords

Wireless sensor networks Clustering EH nodes Network lifetime Residual energy 

References

  1. 1.
    Sudevalayam, S., Kulkarni, P. Energy harvesting sensor nodes: Survey and implications. Communications Surveys & Tutorials, IEEE, (13) (3) 443–461 (2011).Google Scholar
  2. 2.
    V. Raghunathan, A. Kansal, J. Hsu, J. Friedman, M. Srivastava, Design considerations for solar energy harvesting wireless embedded systems, in: Fourth International Symposium on Information Processing in Sensor Networks, 457–462 (2005).Google Scholar
  3. 3.
    D. Hasenfratz, A. Meier, C. Moser, J.J. Chen, L. Thiele, Analysis, comparison, and optimization of routing protocols for energy harvesting wireless sensor networks, in: IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC), 19–26 (2010).Google Scholar
  4. 4.
    M. Islam, M. Islam, M. Islam, A-sleach: an advanced solar aware leach protocol for energy efficient routing in wireless sensor networks, in: Sixth International Conference on Networking, (4) (2007).Google Scholar
  5. 5.
    B. Medepally, N. Mehta, Voluntary energy harvesting relays and selection in cooperative wireless networks, IEEE Tran. Wireless Communications, (9) 3543–3553 (2010).Google Scholar
  6. 6.
    C. Bergonzini, D. Brunelli, L. Benini, Algorithms for harvested energy prediction in batteryless wireless sensor networks, in: 3rd International Workshop on Advances in Sensors and Interfaces, 144–149 (2009).Google Scholar
  7. 7.
    Rault, Tifenn, Abdelmadjid Bouabdallah, and Yacine Challal. Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks (67) 104–122 (2014).Google Scholar
  8. 8.
    Afsar, M. Mehdi, and Mohammad-H. Tayarani-N. “Clustering in sensor networks: A literature survey. Journal of Network and Computer Applications, (46) 198–226 (2014).Google Scholar
  9. 9.
    P. Kuila, P.K. Jana, An energy balanced distributed clustering and routing algorithm for wireless sensor networks. 2nd IEEE Int. Conf. Parallel Distributed and Grid Computing (PDGC), 220–225 (2012).Google Scholar
  10. 10.
    Voigt, T., Dunkels, A., Alonso, J., Ritter, H., & Schiller, J. Solar-aware clustering in wireless sensor networks, in: IEEE International conference in Computers and Communications, (ISCC) (1) 238–243 (2004).Google Scholar
  11. 11.
    Bergonzini, D. Brunelli, and L. Benini, Algorithms for harvested energy prediction in battery less wireless sensor networks, International Workshop on Advances in sensors and Interfaces, (IWASI) 144–149 (2009).Google Scholar
  12. 12.
    Zhang, Pengfei, Gaoxi Xiao, Hwee-Pink Tan. Clustering algorithms for maximizing the lifetime of wireless sensor networks with energy-harvesting sensors, Computer Networks, (57) 2689–2704 (2013).Google Scholar
  13. 13.
    W.B. Heinzelman, A.P. Chandrakasan, H. Balakrishnan.: An application specific protocol architecture for wireless microsensor networks, IEEE Trans. Wirel. Commun. 1 (4) 660–670 (2002).Google Scholar
  14. 14.
    Jeong, J., & Culler, D. Predicting the long-term behavior of a micro-solar power system. ACM Transactions on Embedded Computing Systems (TECS), (2) 35 (2011).Google Scholar

Copyright information

© Springer India 2017

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian School of MinesDhanbadIndia

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