Skip to main content
SpringerLink
Log in

Cluster Estimation in Terrestrial and Underwater Sensor Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In Wireless Sensor Network (WSN), clustering is considered as an efficient network topology which maximizes the received data at the sink by minimizing a direct transmission of data from the sensor nodes. Limiting direct communication between sensor nodes and the sink is achieved by confining sensor node’s transmission within a certain region known as clusters. Once data are being collected from all the sensors in the cluster it is sent to the sink by a node designated to communicate with the sink within a cluster. This technique not only reduces the network congestion, but it increases data reception, and conserves network energy. To achieve an increase in data received at the sink, it is necessary that the correct number of clusters are created within a sensing field. In this paper a new heuristic approach is presented to find the optimal number of clusters in a mobility supported terrestrial and underwater sensor networks. To maintain a strong association between sensor nodes and the node designated known as cluster-head (CH), it is necessary that sensor node’s mobility should also be considered during the cluster setup operation. This approach not only reduces the direct transmission between the sensor nodes and sink, but it also increases sensor node’s connectivity with its CH for the transmission of sensed data which results in the creation of a stable network structure. The proposed analytical estimate considers sensor node’s transmission range and sensing field dimensions for finding the correct number of the clusters in a sensing field. With this approach a better network coverage and connectivity during the exchange of data can be achieved, which in turn increases the network performance.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Rezazadeh, J., Moradi, M., & Ismail, A. S. (2012). Mobile wireless sensor networks overview. IJCCN International Journal of Computer Communications and Networks, 2(1), 17–22.

    Google Scholar 

  2. Kansal, A., Somasundara, A. A., ZhouJea, D. D., Srivastava, M. B., & Estrin, D. (2004). Intelligent fluid infrastructure for embedded networks. In MobiSys ’04 Proceedings of the 2nd international conference on Mobile systems, applications, and services (pp. 111–124).

  3. Sikander, G., Zafar, M. H., Raza, A., Inayatullah, M. B., Mahmud, S. A., & Khan, G. M. (2013). A survey of cluster-based routing schemes for wireless sensor networks. Smart Computing Review, 3(4), 261–275.

    Article  Google Scholar 

  4. Ephremides, A. (2002). Energy concerns in wireless networks. Wireless Communications, 9, 48–59.

    Article  Google Scholar 

  5. Janani, E. S. V., & Kumar, G. P. (2015). Energy efficient cluster based scheduling scheme for wireless sensor networks. The Scientific World Journal, 2015, 9.

    Google Scholar 

  6. Heidemann, J., Stojanoic, M., & Zorzi, M. (2012). Underwater sensor networks: applications, advances and challenges. Philosophical Transactions of Royal Society A Mathematical and Physical Engineering, 370, 158–175.

    Article  Google Scholar 

  7. Awan, K. M., Shah, P. A., Iqbal, K., Gillani, S., Ahmad, W., & Nam, Y. (2019). Underwater wireless sensor networks: A review of recent issues and challenges. Wireless Communications and Mobile Computing,. https://doi.org/10.1155/2019/6470359.

    Article  Google Scholar 

  8. Heinzelman, W. B. (2000). Application-specific protocol architectures for wireless networks. Ph.D. thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology.

  9. Amini, N., Vahdatpour, A., Xu, W., Gerla, M., & Sarrafzadeh, M. (2012). Cluster size optimization in sensor networks with decentralized cluster-based protocols. Computer Communications, 35(2), 207–220.

    Article  Google Scholar 

  10. Sabor, N., Sabah, M. A., Abo-Zahhad, M., & Sasaki, S. (2018). ARBIC: An adjustable range based immune hierarchy clustering protocol supporting mobility of wireless sensor networks. Pervasive and Mobile Computing, 43, 27–48.

    Article  Google Scholar 

  11. Sundararaj, V., Muthukumar, S., & Kumar, R. S. (2018). An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Computers and Security, 77, 277–288.

    Article  Google Scholar 

  12. Gupta, G. P., & Jha, S. (2018). Integrated clustering and routing protocol for wireless sensor networks using Cuckoo and Harmony Search based metaheuristic techniques. Engineering Applications of Artificial Intelligence, 68, 101–109.

    Article  Google Scholar 

  13. Wang, L., Wang, C., & Liu, C. (2009). Optimal number of clusters in dense wireless sensor networks: A cross-layer approach. IEEE Transactions on Vehicular Technology, 58(2), 966–976.

    Article  MathSciNet  Google Scholar 

  14. Raghuvanshi, A. S., Tiwari, S., Tripathi, R., & Kishor, N. (2010). Optimal number of clusters in wireless sensor networks: An FCM approach. In International conference on computer and communication technology (pp. 817–823). ICCCT.

  15. Selvakennedya, S., Sinnappanb, S., & Shangc, Y. (2007). A biologically-inspired clustering protocol for wireless sensor networks. Computer Communications, 30(14–15), 2786–2801.

    Article  Google Scholar 

  16. Islam, Alim Al, & A. B. M., Hyder, C.H., Kabir, H., & Naznin, M., (2010). Finding the optimal percentage of cluster heads from a new and complete mathematical model on LEACH. Wireless Sensor Network, 2, 129–140.

  17. Yadav, S., & Kumar, V. (2017). Optimal clustering in underwater wireless sensor networks: Acoustic, EM and FSO communication compliant technique. IEEE Access, 5, 12761–12776.

    Article  Google Scholar 

  18. Wang, K., Gao, H., Xu, X., Jiang, J., & Dong, Yue. (2016). An energy-efficient reliable data transmission scheme for complex environmental monitoring in underwater acoustic sensor networks. IEEE Sensors Journal, 16(11), 4051–4062.

    Article  Google Scholar 

  19. Amini, A., Vahdatpour, A., Dabiri, F., Noshadi, H., & Sarrafzadeh, M. (2011). Joint consideration of energy-efficiency andcoverage-preservation in microsensor networks. Wireless Communications and Mobile Computing, 11(6), 707–722.

    Article  Google Scholar 

  20. Durrani, M. Y., Tariq, R., Aadil, F., Maqsood, M., Nam, Y., & Muhammad, K. (2019). Adaptive node clustering technique for smart ocean under water sensor network (SOSNET). Sensors, 19(5), 1145.

    Article  Google Scholar 

  21. Wang, S., Nguyn, T. L. N., & Shin, Y. (2019). Energy-efficient clustering algorithm for magnetic induction-based underwater wireless sensor networks. IEEE Access, 7, 82027–82037.

    Article  Google Scholar 

  22. Ismat, N., Qureshi, R., & ul Imam, M. (2014). Efficient clustering for mobile wireless sensor networks. In IEEE 17th international multi topic conference (INMIC 2014) (pp. 110 – 114). IEEE.

  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. Enam, R. N., Ismat, N., & Farooq, F. (2017). Connectivity and coverage based grid-cluster size calculation in wireless sensor networks. Wireless Personal Communications, 95(2), 429–443.

    Article  Google Scholar 

  25. Hindu, S. K., Hyder, W., Luque-Nieto, M., Poncela, J., & Otero, P. (2019). Self-organizing and scalable routing protocol (SOSRP) for underwater acoustic sensor networks. Sensors, 19, 3130.

    Article  Google Scholar 

  26. Che, X., Wells, I., Dickers, G., Kear, P., & Gong, X. (2010). Re-evaluation of RF electromagnetic communication in underwater sensor networks. IEEE Communications Magazine, 48(12), 143–151.

    Article  Google Scholar 

  27. Munasinghe, K., Aseeri, M., Almorqi, S., Hossain, M. F., Ahmad, Wali, & M, B., & Jamalipour, A., (2017). EM-based high speed wireless sensor networks for underwater surveillance and target tracking. Journal of Sensors,. https://doi.org/10.1155/2017/6731204.

  28. Malajne, M., Benkič, C., & Planinsič, P. (2013). A new study regarding the comparison of calculated and measured RSSI values under different experimental conditions. Przeglad Elektrotechniczny, 89(11), 214–219.

    Google Scholar 

  29. Türkoral, T., Tamer, O., Yetiş, S., Inanç, E., & Çetin, L. (2017). Short range indoor distance estimation by using RSSI metric. IU-JEEE, 17(2), 3295–3302.

    Google Scholar 

  30. Camp, T., Boleng, J., & Davies, V. (2002). A survey of mobility models for ad hoc network research. Mobile Ad Hoc Networking Research, Trends and Applications, 2(5), 483–502.

    Google Scholar 

  31. Kim, D.-S., Chung, Y.-J., & Davies, V. (2006). Self-organization routing protocol supporting mobile nodes for wireless sensor network Computer and Computational Sciences, 2006. In IMSCCS’06. First international multi-symposiums (Vol. 2, pp. 622–626).

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Sir Syed University of Engineering and Technology, Karachi, 73500, Pakistan

    Najma Ismat, Rehan Qureshi, Rabia Noor Enam, Shaheena Noor & Muhammad Tahir

Authors
  1. Najma Ismat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rehan Qureshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rabia Noor Enam
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shaheena Noor
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Muhammad Tahir
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Najma Ismat.

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

Ismat, N., Qureshi, R., Enam, R.N. et al. Cluster Estimation in Terrestrial and Underwater Sensor Networks. Wireless Pers Commun 116, 1443–1462 (2021). https://doi.org/10.1007/s11277-020-07851-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07851-9

Keywords

Search

Navigation