Skip to main content

Advertisement

Springer Nature Link
Log in

EMSNDP: Energy Minimized Optimal Zone Selection of Sensor Network to Build the Data Path for IoTN

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Wireless sensor network contains a huge volume of sensors that are capable of observing environmental data and conveying that information to the base station. The zone routing is a way to group sensors into multiple clusters in sensing field. In general, each zone has a single co-ordinator that operates as a zone head and accountable to collect the data from all sensors inside the zone and then forward it to the base station. Clustering and energy efficient path construction to base station is a major issue in sensor networks. This paper concentrates on clustering the sensing field into zones and further splitting it into zone quadrants to maximize the energy efficiency of sensor nodes with minimum delay transmission time. An “Energy Minimized optimal zone selection of Sensor Networks to build the Data Path” protocol is proposed in this paper to minimize the load on zone heads by using the zone coordinators to gather the information from different quadrants of zone. Distance-based communication with the minimum hop is opted to minimize the energy cost. A critical evaluation is carried out on the proposed protocol in the context of total energy consumption of network and average energy consumed per node, lifetime of the Network, delay in reception of packet and packet delivery ratio. The results of our research work and analyzed and benchmarked against similar protocols using Network Simulator 2. The outcome of this research is a reduced delay in packet processing with low energy consumption which significantly increases lifetime of sensor network.

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

Access this article

Log in via an institution

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Code Availability

Can be provided if requested personally through corresponding author mail or through a repository created by our institution. All simulation videos are included in the supplementary information file.

References

  1. Bendjeddou, A., Laoufi, H., & Boudiit, S. (2018). LEACH-S: Low Energy Adaptive Clustering Hierarchy for Sensor Network. In: 2018 International Symposium on Networks, Computers and Communications, ISNCC 2018, Rome (pp.1–6) doi: https://doi.org/10.1109/ISNCC.2018.8531049

  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. https://doi.org/10.1109/TWC.2002.804190.

    Article  Google Scholar 

  3. Awad, F., Taqieddin, E., & Seyam, A. (2012). Energy-efficient and coverage-aware clustering in wireless sensor networks. Wireless Engineering and Technology, 3(3), 142–151. https://doi.org/10.4236/wet.2012.33021.

    Article  Google Scholar 

  4. Manjeshwar, A., & Agrawal, D. P. (2001). TEEN: A routing protocol for enhanced efficiency in wireless sensor networks. In: Proceedings - 15th International Parallel and Distributed Processing Symposium, IPDPS 2001, San Francisco, CA, USA, (pp.2009–2015) doi: https://doi.org/10.1109/IPDPS.2001.925197

  5. Kumar, D., Aseri, T. C., & Patel, R. B. (2009). EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks. Computer Communications, 32(4), 662–667. https://doi.org/10.1016/j.comcom.2008.11.025.

    Article  Google Scholar 

  6. Gautam, N., Lee, W, & Pyun, J. Y. (2009). Dynamic clustering and distance aware routing protocol for wireless sensor networks. In: PE-WASUN’09 - Proceedings of the 6th ACM International Symposium on Performance Evaluation of Wireless Ad-Hoc, Sensor and Ubiquitous Networks (pp. 9–14) doi: https://doi.org/10.1145/1641876.1641879

  7. Lai, W. K., Fan, C. S., & Lin, L. Y. (2012). Arranging cluster sizes and transmission ranges for wireless sensor networks. Information Sciences, 183(1), 117–131. https://doi.org/10.1016/j.ins.2011.08.029.

    Article  Google Scholar 

  8. Azizi, N., Karimpour, J., & Seifi, F. (2012). HCTE: Hierarchical clustering based routing algorithm with applying the two cluster heads in each cluster for energy balancing in WSN. International Journal of Computer Science Issues, 9(1), 57–61.

    Google Scholar 

  9. Tang, F., You, I., Guo, S., Guo, M., & Ma, Y. (2012). A chain-cluster based routing algorithm for wireless sensor networks. Journal of Intelligent Manufacturing, 23(4), 1305–1313. https://doi.org/10.1007/s10845-010-0413-4.

    Article  Google Scholar 

  10. Arash, G. D. (2012). SLGC: A new cluster routing algorithm in wireless sensor network for decreasing energy consumption. International Journal of Computer Science Engineering and Applications (IJCSEA), 2(3), 39–51. https://doi.org/10.1007/s10845-010-0413-4.

    Article  Google Scholar 

  11. Fan, Z., & Jin, Z. (2012). A multi-weight based clustering algorithm for wireless sensor networks. Przeglad Elektrotechniczny, 88(1B), 19–21.

    Google Scholar 

  12. Ossama Younis, S. F. (2006). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 5(10), 1471–1472. https://doi.org/10.1109/TMC.2006.141.

    Article  Google Scholar 

  13. Behera, T. M., Mohapatra, S. K., Samal, U. C., Khan, M. S., Daneshmand, M., & Gandomi, A. H. (2020). I-SEP: An improved routing protocol for heterogeneous WSN for IoT-based environmental monitoring. IEEE Internet of Things Journal, 7(1), 710–717. https://doi.org/10.1109/JIOT.2019.2940988.

    Article  Google Scholar 

  14. He, A., Long, J., & Zhang, J. (2019). An energy-efficient multi-ring-based routing scheme for WSNs. IEEE Access, 7, 181257–181272. https://doi.org/10.1109/ACCESS.2019.2947496.

    Article  Google Scholar 

  15. Hu, S., Liu, L., Fang, L., Zhou, F., & Ye, R. (2020). A novel energy-efficient and privacy-preserving data aggregation for WSNs. IEEE Access, 8(1), 802–813. https://doi.org/10.1109/ACCESS.2019.2961512.

    Article  Google Scholar 

  16. Xu, C., Xiong, Z., Zhao, G., & Yu, S. (2019). An energy-efficient region source routing protocol for lifetime maximization in WSN. IEEE Access, 7, 135277–135289. https://doi.org/10.1109/ACCESS.2019.2942321.

    Article  Google Scholar 

  17. He, W. (2019). Energy-saving algorithm and simulation of wireless sensor networks based on clustering routing protocol. IEEE Access, 7, 172505–172514. https://doi.org/10.1109/ACCESS.2019.2956068.

    Article  Google Scholar 

  18. Gharaei, N., Al-Otaibi, Y. D., Butt, S. A., Sahar, G., & Rahim, S. (2019). Energy-efficient and coverage-guaranteed unequal-sized clustering for wireless sensor networks. IEEE Access, 7, 157883–157891. https://doi.org/10.1109/ACCESS.2019.2950237.

    Article  Google Scholar 

  19. Rady, A., Shokair, M., El-Rabaie, E. L. S. M., Saad, W., & Benaya, A. (2019). Energy-efficient routing protocol based on sink mobility for wireless sensor networks. IET Wireless Sensor Systems, 9(6), 405–415. https://doi.org/10.1049/iet-wss.2019.0044.

    Article  Google Scholar 

  20. Nalluri, P. R. K., & Bala, G. J. (2019). An efficient energy saving sink selection scheme with the best base station placement strategy using tree based self organizing protocol for IoT. Wireless Personal Communications, 109, 869–895. https://doi.org/10.1007/s11277-019-06595-5.

    Article  Google Scholar 

  21. Zaman, N., & Abdullah, A. B. (2012). energy optimization through position responsive routing protocol ( PRRP) in wireless sensor network. International Journal of Information and Electronics Engineering, 2(5), 748–751. https://doi.org/10.7763/IJIEE.2012.V2.199.

    Article  Google Scholar 

Download references

Funding

No funding was received.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, 641114, India

    Prophess Raj Kumar Nalluri & Josemin Bala Gnanadhas

Authors
  1. Prophess Raj Kumar Nalluri
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Josemin Bala Gnanadhas
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Prophess Raj Kumar Nalluri.

Ethics declarations

Conflicts of interest

Authors declare no conflicts of interest.

Data Availability

The authors declare that the data supporting the findings of this study are available within the article [and its supplementary information files].

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

Nalluri, P.R.K., Gnanadhas, J.B. EMSNDP: Energy Minimized Optimal Zone Selection of Sensor Network to Build the Data Path for IoTN. Wireless Pers Commun 119, 63–95 (2021). https://doi.org/10.1007/s11277-021-08199-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08199-4

Keywords