Skip to main content
Log in

Energy Efficient Energy Hole Repelling (EEEHR) Algorithm for Delay Tolerant Wireless Sensor Network

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Reducing energy consumption and increasing network lifetime are the major concerns in Wireless Sensor Network (WSN). Increase in network lifetime reduces the frequency of recharging and replacing batteries of the sensor node. The key factors influencing energy consumption are distance and number of bits transmitted inside the network. The problem of energy hole and hotspot inside the network make neighbouring nodes unusable even if the node is efficient for data transmission. Energy Efficient Energy Hole Repelling (EEEHR) routing algorithm is developed to solve the problem. Smaller clusters are formed near the sink and clusters of larger size are made with nodes far from the sink. This methodology promotes equal sharing of load repelling energy hole and hotspot issues. The opportunity of being a Cluster Head (CH) is given to a node with high residual energy, very low intra cluster distance in case of nodes far away from the sink and very low CH to sink distance for the nodes one hop from the sink. The proposed algorithm is compared with LEACH, LEACH-C and SEP routing protocol to prove its novel working. The proposed EEEHR routing algorithm provides improved lifetime, throughput and less packet drop. The proposed algorithm also reduces energy hole and hotspot problem in the network.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Abbasi, A. A., & Younis, M. (2007). A survey on clustering algorithms for wireless sensor networks. Computer Communications, 30(14), 2826–2841.

    Article  Google Scholar 

  2. 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.

    Article  Google Scholar 

  3. Guo, P., Jiang, T., Zhang, K., et al. (2009). Clustering algorithm in initialization of multi-hop wireless sensor networks. IEEE Transactions on Wireless Communications, 8(12), 5713–5717.

    Article  Google Scholar 

  4. Deng, S., Li, J., & Shen, L. (2011). Mobility-based clustering protocol for wireless sensor networks with mobile nodes. IET Wireless Sensor Systems, 1(1), 39–47.

    Article  Google Scholar 

  5. Kumar, R., Malik, A., & Kumar, B. (2016). NEECP: A novel energy efficient clustering protocol for prolonging lifetime of WSNs. IET Wireless Sensor Systems, 6, 151–157.

    Article  Google Scholar 

  6. Lu, K., Liu, G., Mao, R., et al. (2011). Relay node placement based on balancing power consumption in wireless sensor networks. IET Wireless Sensor Systems, 1(1), 1–6.

    Article  Google Scholar 

  7. Maheswar, R., & Jayaparvathy, R. (2011). Performance analysis of cluster based sensor networks using N-policy M/G/1 queueing model. European Journal of Scientific Research, 58(2), 177–188.

    Google Scholar 

  8. Maheswar, R., & Jayaparvathy, R. (2012). Performance analysis of fault tolerant node in wireless sensor network. In Third international conference on advances in communication, network, and computing—CNC 2012. Springer.

  9. Kanagachidambaresan, G. R., Dhulipalab, V. R. S., & Udhaya, M. S. (2011). Markovian model based trustworthy architecture. In Procedia engineering. Elseiver, ICCTSD.

  10. Dhulipala, V. R. S., Kanagachidambaresan, G. R., & Chandrasekaran, R. M. (2012). Lack of power avoidance: A fault classification based fault tolerant framework solution for lifetime enhancement and reliable communication in wireless sensor network. Information Technology Journal, 11(6), 719.

    Article  Google Scholar 

  11. Kanagachidambaresan, G. R., Dhulipala, V. R. S., Vanusha, D., & Udhaya, M. S. (2011). Matlab based modeling of body sensor network using ZigBee protocol. In CIIT 2011, CCIS 250 (pp. 773–776).

  12. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38, 393–422.

    Article  Google Scholar 

  13. Kanagachidambaresan, G. R., & Chitra, A. (2015). Fail safe fault tolerant mechanism for wireless body sensor network (WBSN). Wireless Personal Communications, 80(1), 247–260. https://doi.org/10.1007/s11277-014-2006-6.

    Article  Google Scholar 

  14. Kanagachidambaresan, G. R., & Chitra, A. (2016). TA-FSFT thermal aware fail safe fault tolerant algorithm for wireless body sensor network. Wireless Personal Communication, 90(4), 1935–1950.

    Article  Google Scholar 

  15. Mahima, V., & Chitra, A. (2017). Battery recovery based lifetime enhancement algorithm for wireless sensor network. Wireless Personal Communication, 97(4), 6541–6557.

    Article  Google Scholar 

  16. Vahdat, A., & Becker, D. (2002). Epidemic routing for partially-connected ad hoc networks. In Technical report. Duke University.

  17. Spyropoulos, T., Psounis, K., & Raghavendra, C. S. (2008). Efficient routing in intermittently connected mobile networks: The multiplecopy case. IEEE/ACM Transactions on Networking, 16(1), 77–90.

    Article  Google Scholar 

  18. Liu, C., & Wu, J. (2009). An optimal probabilistic forwarding protocol in delay tolerant networks. In Proceedings of ACM MobiHoc.

  19. Liu, C., & Wu, J. (2012). On multicopy opportunistic forwarding protocols in nondeterministic delay tolerant networks. IEEE Transactions on Parallel and Distributed Systems, 23, 1121–1128.

    Article  Google Scholar 

  20. Eshghi, S., Khouzani, M. H. R., Sarkar, S., Shroff, N. B., & Venkatesh, S. S. (2015). Optimal energy-aware epidemic routing in DTNs. IEEE Transactions on Automatic Control, 60(6), 1554–1569.

    Article  MathSciNet  MATH  Google Scholar 

  21. Conan, V., Leguay, J., & Friedman, T. (2008). Fixed point opportunistic routing in delay tolerant networks. IEEE Journal on Selected Areas in Communications, 26(5), 773–782.

    Article  Google Scholar 

  22. Wen, H., Ren, F., Liu, J., Lin, C., Li, P., & Fang, Y. (2011). A storage-friendly routing scheme in intermittently connected mobile network. IEEE Transactions on Vehicular Technology, 60, 1138–1149.

    Article  Google Scholar 

  23. Elwhishi, A., & Ho, P.-H. (2013). Contention aware routing for intermittently connected mobile networks. IEEE Transactions on parallel and Distributed Systems, 24, 1422–1435.

    Article  Google Scholar 

  24. Lai, X., Ji, X., Zhou, X., & Chen, L. (2018). Energy efficient link-delay aware routing in wireless sensor networks. IEEE Sensors Journal, 18, 837–848.

    Article  Google Scholar 

  25. Cengiz, K., & Dag, T. (2018). Energy aware multi-hop routing protocol for WSNs. IEEE Access Journal, 6, 2622–2633.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to K. Lakshmi Prabha.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Prabha, K.L., Selvan, S. Energy Efficient Energy Hole Repelling (EEEHR) Algorithm for Delay Tolerant Wireless Sensor Network. Wireless Pers Commun 101, 1395–1409 (2018). https://doi.org/10.1007/s11277-018-5768-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-018-5768-4

Keywords