Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
SpringerLink
Log in

A hybridization strategy using equal and unequal clustering schemes to mitigate idle listening for lifetime maximization of wireless sensor network

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

The main focus in the designing of wireless sensor networks (WSNs) applications and protocols is minimizing energy dissipation while at the same time maximizing the network lifetime. In this paper, we investigate the idle listening and hotspot problems with respect to the clustering routing technique in WSNs. Some of the studies in clustering centered around equal clustering scheme while others were based on unequal clustering scheme. Equal clustering scheme avoids inter-cluster idle listening as the intra-cluster data collection can be completed at the same time across all clusters in the network but lead to hotspot problem due to additional traffic relay experienced by cluster heads near the base station. Similarly, unequal clustering addresses hotspot problem but lead to inter-cluster idle listening due to the use of global network-wide intra-cluster collection among clusters of unequal sizes and densities. In this paper we proposed a zone-based clustering scheme referred to as hybrid equal and unequal clustering (HEUC) that jointly address inter-cluster idle listening and hotspot problems to maximize the lifetime of WSNs. To achieve this, the network area is firstly partitioned into zones. The size of clusters in the same zone is designed to be equal and cluster size increases across zones as the distance from the BS increases. Secondly, the density of nodes deployed in each cluster per zone is obtained in such a way that the distribution of nodes in zones away from BS increases according to the radius increase in clusters across the zones. Thirdly, a deterministic deployment strategy is used to deploy nodes into various clusters. Fourthly, we implement a zone-wise intra-cluster data collection strategy that allows intra-cluster data collection to be completed at once to address inter-cluster idle listening. Lastly, a multihop routing is used for data forwarding to BS. The result shows that our proposed HEUC has 38% and 51% lifetime improvement against EBULRP and AECR respectively.

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
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Shaikh, F. K., Zeadally, S., & Exposito, E. (2015). Enabling technologies for green internet of things. IEEE Systems Journal, 11(2), 983–994.

    Article  Google Scholar 

  2. Adil Mahdi, O., Abdul Wahab, A. W., Idris, M. Y. I., Abu Znaid, A., Al-Mayouf, Y. R. B., & Khan, S. (2016). WDARS: A weighted data aggregation routing strategy with minimum link cost in event-driven WSNs. Journal of Sensors. https://doi.org/10.1155/2016/3428730.

  3. Arampatzis, T., Lygeros, J., & Manesis, S. (2005). A survey of applications of wireless sensors and wireless sensor networks. In Proceedings of the 2005 IEEE international symposium on, Mediterrean conference on control and automation intelligent control. IEEE (pp. 719–724).

  4. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.

    Article  Google Scholar 

  5. Chen, G., Li, C., Ye, M., & Wu, J. (2009). An unequal cluster-based routing protocol in wireless sensor networks. Wireless Networks, 15(2), 193–207.

    Article  Google Scholar 

  6. Shagari, N. M., Idris, M. Y. I., Salleh, R. B., Ahmedy, I., Murtaza, G., & Shehadeh, H. A. (2020). Heterogeneous energy and traffic aware sleep-awake cluster-based routing protocol for wireless sensor network. IEEE Access, 8, 12232–12252.

    Article  Google Scholar 

  7. Arjunan, S., & Pothula, S. (2019). A survey on unequal clustering protocols in wireless sensor networks. Journal of King Saud University-Computer and Information Sciences, 31(3), 304–317.

    Article  Google Scholar 

  8. Wu, Y., Li, X.-Y., Li, Y., & Lou, W. (2009). Energy-efficient wake-up scheduling for data collection and aggregation. IEEE Transactions on Parallel and Distributed Systems, 21(2), 275–287.

    Article  Google Scholar 

  9. Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 4, 366–379.

    Article  Google Scholar 

  10. Neamatollahi, P., Naghibzadeh, M., Abrishami, S., & Yaghmaee, M.-H. (2018). Distributed clustering-task scheduling for wireless sensor networks using dynamic hyper round policy. IEEE Transactions on Mobile Computing, 17(2), 334–347.

    Article  Google Scholar 

  11. Neamatollahi, P., Abrishami, S., Naghibzadeh, M., Moghaddam, M. H. Y., & Younis, O. (2018). Hierarchical clustering-task scheduling policy in cluster-based wireless sensor networks. IEEE Transactions on Industrial Informatics, 14(5), 1876–1886.

    Article  Google Scholar 

  12. Haseeb, K., Bakar, K. A., Abdullah, A. H., & Darwish, T. (2017). Adaptive energy aware cluster-based routing protocol for wireless sensor networks. Wireless Networks, 23(6), 1953–1966.

    Article  Google Scholar 

  13. Li, C., Ye, M., Chen, G., & Wu, J. (2005). An energy-efficient unequal clustering mechanism for wireless sensor networks. In IEEE international conference on mobile adhoc and sensor systems conference (pp. 8–604). IEEE.

  14. Wang, T., Wang, Y., & Han, C. (2017). An improved clustering routing mechanism for wireless Ad hoc network. Journal of Intelligent & Fuzzy Systems, 32(5), 3401–3412.

    Article  Google Scholar 

  15. Afsar, M. M., & Tayarani-N, M.-H. (2014). Clustering in sensor networks: A literature survey. Journal of Network and Computer Applications, 46, 198–226.

    Article  Google Scholar 

  16. Chen, H., Chi, K. T., & Feng, J. (2009). Impact of topology on performance and energy efficiency in wireless sensor networks for source extraction. IEEE Transactions on Parallel and Distributed Systems, 20(6), 886–897.

    Article  Google Scholar 

  17. Zhang, D.-G., Liu, S., Zhang, T., & Liang, Z. (2017). Novel unequal clustering routing protocol considering energy balancing based on network partition & distance for mobile education. Journal of Network and Computer Applications, 88, 1–9.

    Article  Google Scholar 

  18. Yuan, H.-Y., Yang, S.-Q., & Yi Y.-Q. (2011). An energy-efficient unequal clustering method for wireless sensor networks," in 2011 international conference on computer and management (CAMAN) (pp. 1–4). IEEE.

  19. Wang, J., Cao, Y., Cao, J., Ji, H., & Yu, X. (2016). Energy-balanced unequal clustering routing algorithm for wireless sensor networks. In Advances in computer science and ubiquitous computing (pp. 352–359). Springer.

  20. Purkait, R., & Tripathi, S. (2015). Fuzzy based unequal energy aware clustering with multi-hop routing in wireless sensor network. In 2015 IEEE workshop on computational intelligence: theories, applications and future directions (WCI) (pp. 1–10). IEEE.

  21. Li, H., Liu, Y., Chen, W., Jia, W., Li, B., & Xiong, J. (2013). COCA: Constructing optimal clustering architecture to maximize sensor network lifetime. Computer Communications, 36(3), 256–268.

    Article  Google Scholar 

  22. 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 

  23. Kang, S. H., & Nguyen, T. (2012). Distance based thresholds for cluster head selection in wireless sensor networks. IEEE Communications Letters, 16(9), 1396–1399.

    Article  Google Scholar 

  24. Chen, C., Rao, F., Zhang, X., & Dong, Y. (2015). An asynchronous cluster head rotation scheme for wireless sensor networks. In 2015 International wireless communications and mobile computing conference (IWCMC) (pp. 551–556). IEEE.

  25. Gajendran, E. (2017). Ring structured clustering algorithm in wireless sensor networks using integrated clustering. Asian Journal of Applied Science and Technology (AJAST), 1, 380–390.

    Google Scholar 

  26. Panag, T. S., & Dhillon, J. (2018). Dual head static clustering algorithm for wireless sensor networks. AEU-International Journal of Electronics and Communications, 88, 148–156.

    Article  Google Scholar 

  27. Gupta, V., & Pandey, R. (2016). An improved energy aware distributed unequal clustering protocol for heterogeneous wireless sensor networks. An International Journal of Engineering Science and Technology 19(2), 1050–1058.

    Article  Google Scholar 

  28. Yang, J., & Zhang, D. (2009). An energy-balancing unequal clustering protocol for wireless sensor networks. Information Technology Journal, 8(1), 57–63.

    Article  Google Scholar 

  29. Ever, E., Luchmun, R., Mostarda, L., Navarra, A., & Shah, P. (2012). Uheed-an unequal clustering algorithm for wireless sensor networks.

  30. Han, T., Bozorgi, S. M., Orang, A. V., Hosseinabadi, A. A. R., Sangaiah, A. K., & Chen, M.-Y. (2019). A hybrid unequal clustering based on density with energy conservation in wireless nodes. Sustainability, 11(3), 746.

    Article  Google Scholar 

  31. Yu, B., Choi, W., Lee, T., & Kim, H. (2018). Clustering algorithm considering sensor node distribution in wireless sensor networks. Journal of Information Processing Systems, 14(4), 926–940.

  32. 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.

    Article  Google Scholar 

  33. Lobiyal, D. (2018). Energy consumption reduction in S-MAC protocol for wireless sensor network. Procedia Computer Science, 143, 757–764.

    Article  Google Scholar 

  34. Kritsis, K., Papadopoulos, G. Z., Gallais, A., Chatzimisios, P., & Theoleyre, F. (2018). A tutorial on performance evaluation and validation methodology for low-power and lossy networks. IEEE Communications Surveys and Tutorials, 20(3), 1799–1825.

    Article  Google Scholar 

  35. Li, Z., Liu, Y., Liu, A., Wang, S., & Liu, H. (2018). Minimizing convergecast time and energy consumption in green Internet of Things. IEEE transactions on emerging topics in computing, 8, 797–813.

  36. Khan, W. Z., Saad, N., & Aalsalem, M. Y. (2012). An overview of evaluation metrics for routing protocols in wireless sensor networks. In 2012 4th international conference on intelligent and advanced systems (ICIAS2012), 2012 (vol. 2, pp. 588–593). IEEE.

  37. Sha, K., Du, J., & Shi, W. (2006). WEAR: A balanced, fault-tolerant, energy-aware routing protocol in WSNs. International Journal of Sensor Networks, 1(3–4), 156–168.

    Article  Google Scholar 

Download references

Acknowledgements

This research acknowledged the financial support by the University of Malaya under the Impact-Oriented-Interdisciplinary Research Grant Programme (IIRG) IIRG003A-19IISS and Fundamental Research Grant Scheme (FRGS) FP055-2019A

Author information

Authors and Affiliations

  1. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Nura Modi Shagari, Mohd Yamani Idna Idris, Rosli Bin Salleh, Ismail Ahmedy, Ghulam Murtaza & Aznul Qalid Bin Md Sabri

  2. Department of Computer Science, Sokoto State University, Sokoto, Nigeria

    Nura Modi Shagari

  3. Centre for Mobile Cloud, Computing University of Malaya, 50603, Kuala Lumpur, Malaysia

    Mohd Yamani Idna Idris

  4. Department of Computer Science, Sukkur IBA University, Sukkur, Pakistan

    Ghulam Murtaza

Authors
  1. Nura Modi Shagari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohd Yamani Idna Idris
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rosli Bin Salleh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ismail Ahmedy
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ghulam Murtaza
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Aznul Qalid Bin Md Sabri
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The research investigation is done by Nura M. Shagari. Supervision, project administration and funding acquisition: Mohd Yamani Idna Idris, Rosli Bin Salleh, Ismail Ahmedy, and Aznul Qalid Bin MD Sabri. Reviewing and editing: Gulam Murtaza.

Corresponding author

Correspondence to Mohd Yamani Idna Idris.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

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

Shagari, N.M., Idris, M.Y.I., Salleh, R.B. et al. A hybridization strategy using equal and unequal clustering schemes to mitigate idle listening for lifetime maximization of wireless sensor network. Wireless Netw 27, 2641–2670 (2021). https://doi.org/10.1007/s11276-021-02608-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-021-02608-z

Keywords

Search

Navigation