Skip to main content
Log in

NHCDRA: a non-uniform hierarchical clustering with dynamic route adjustment for mobile sink based heterogeneous wireless sensor networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

In wireless sensor networks (WSNs), various routing protocols have been proposed based on clustering to achieve energy efficiency. The performance of such routing protocols can further be improved by deploying heterogeneous sensor nodes since responsibilities can be divided among different sensor nodes according to their heterogeneity. In this paper, we propose a novel non-uniform hierarchical clustering with dynamic route adjustment scheme, termed as NHCDRA, for heterogeneous WSNs considering two mobile sinks moving around the periphery of the sensor field. NHCDRA divides the sensor field into several non-uniform sized hierarchical clusters. The size of boundary clusters (near to the sink’s mobility path) is kept relatively large to accommodate more number of sensor nodes so that the responsibilities of cluster heads can be rotated efficiently to distribute the load. Furthermore, a set of dynamic route adjustment rules are defined to manage the routing paths as a consequence of mobility of the sinks. These rules reduce the overhead of route adjustment as well as ensure data delivery to the sink in minimal number of hops. Simulation results show that NHCDRA significantly reduces the data delivery delay and improves network lifetime when compared with state-of-the-art.

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

Access this article

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

Similar content being viewed by others

References

  1. Aanchal, K., Sushil, K., Omprakash, K., Nauman, A., Neeru, M., & Hanan, A. A. (2018). Towards green computing in wireless sensor networks: Controlled mobilityaided balanced tree approach. International Journal of Communication Systems, 31(7), e3463.

    Article  Google Scholar 

  2. Agrawal, A., Singh, V., Jain, S., & Gupta, R. K. (2018). Gcrp: Grid-cycle routing protocol for wireless sensor network with mobile sink. AEU - International Journal of Electronics and Communications, 94, 1–11.

    Article  Google Scholar 

  3. Azharuddin, M., & Jana, P. K. (2015). A distributed algorithm for energy efficient and fault tolerant routing in wireless sensor networks. Wireless Networks, 21(1), 251–267.

    Article  Google Scholar 

  4. Bajaber, F., & Awan, I. (2014). An efficient cluster-based communication protocol for wireless sensor networks. Telecommunication Systems, 55(3), 387–401.

    Article  Google Scholar 

  5. Cassandras, C.G., Wang, T., & Pourazarm, S. (2014). Optimal routing and energy allocation for lifetime maximization of wireless sensor networks with nonideal batteries. IEEE Transactions on Control of Network Systems 1(1), 86–98. https://doi.org/10.1109/TCNS.2014.2304367

  6. Chanak, P., Banerjee, I., & Sherratt, R. S. (2020). A green cluster-based routing scheme for large-scale wireless sensor networks. International Journal of Communication Systems, 33(9), e4375.

    Article  Google Scholar 

  7. Chen, T. S., Tsai, H. W., Chang, Y. H., & Chen, T. C. (2013). Geographic convergecast using mobile sink in wireless sensor networks. Computer Communications, 36(4), 445–458.

    Article  Google Scholar 

  8. Christopher, V., & Jasper, J. (2020). Dhgrp: Dynamic hexagonal grid routing protocol with mobile sink for congestion control in wireless sensor networks. Wireless Personal Communications. https://doi.org/10.1007/s11277-020-07146-z

  9. Dhage, M. R., & Vemuru, S. (2018). Routing design issues in heterogeneous wireless sensor network. International Journal of Electrical and Computer Engineering, 8(2), 1028.

    Google Scholar 

  10. Fanian, F., & Kuchaki Rafsanjani, M. (2019). Cluster-based routing protocols in wireless sensor networks: A survey based on methodology. Journal of Network and Computer Applications, 142, 111–142.

    Article  Google Scholar 

  11. Jain, S., Pattanaik, K., & Shukla, A. (2019). Qwrp: Query-driven virtual wheel based routing protocol for wireless sensor networks with mobile sink. Journal of Network and Computer Applications, 147, 102430.

    Article  Google Scholar 

  12. Jain, S., Pattanaik, K.K., Verma, R.K., Bharti, S., & Shukla, A. (2020). Delay-aware green routing for mobile sink based wireless sensor networks. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2020.3030120

  13. Jain, S., Pattanaik, K.K., Verma, R.K., & Shukla, A. (2019). Qrrp: A query-driven ring routing protocol for mobile sink based wireless sensor networks. In TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON) (pp. 1986–1991). https://doi.org/10.1109/TENCON.2019.8929714

  14. Jain, S., Sharma, S., & Bagga, N. (2016). A vertical and horizontal segregation based data dissemination protocol. In Emerging research in computing, information, communication and applications (pp. 401–412). Springer

  15. Jannu, S., & Jana, P. K. (2016). A grid based clustering and routing algorithm for solving hot spot problem in wireless sensor networks. Wireless Networks, 22(6), 1901–1916.

    Article  Google Scholar 

  16. Khan, A. W., Bangash, J. I., Ahmed, A., & Abdullah, A. H. (2019). Qdvgdd: Query-driven virtual grid based data dissemination for wireless sensor networks using single mobile sink. Wireless Networks, 25, 241–253.

    Article  Google Scholar 

  17. Lin, H., Bai, D., Gao, D., & Liu, Y. (2016). Maximum data collection rate routing protocol based on topology control for rechargeable wireless sensor networks. Sensors, 16(8), 1201.

    Article  Google Scholar 

  18. Maurya, S., Gupta, V., & Jain, V.K. (2017). Lbrr: Load balanced ring routing protocol for heterogeneous sensor networks with sink mobility. In: 2017 IEEE wireless communications and networking Conference (WCNC) (pp 1–6). https://doi.org/10.1109/WCNC.2017.7925728

  19. Maurya, S., Jain, V. K., & Chowdhury, D. R. (2019). Delay aware energy efficient reliable routing for data transmission in heterogeneous mobile sink wireless sensor network. Journal of Network and Computer Applications, 144, 118–137.

    Article  Google Scholar 

  20. Mehto, A., Tapaswi, S., & Pattanaik, K. (2020). Virtual grid-based rendezvous point and sojourn location selection for energy and delay efficient data acquisition in wireless sensor networks with mobile sink. Wireless Networks. https://doi.org/10.1007/s11276-020-02293-4

  21. Naghibi, M., & Barati, H. (2020). Egrpm: Energy efficient geographic routing protocol based on mobile sink in wireless sensor networks. Sustainable Computing: Informatics and Systems, 25, 100377.

    Google Scholar 

  22. Perera, C., Zaslavsky, A., Liu, C. H., Compton, M., Christen, P., & Georgakopoulos, D. (2013). Sensor search techniques for sensing as a service architecture for the internet of things. IEEE Sensors Journal, 14(2), 406–420.

    Article  Google Scholar 

  23. Saoudi, M., Lalem, F., Bounceur, A., Euler, R., Kechadi, M. T., Laouid, A., et al. (2017). D-lpcn: A distributed least polar-angle connected node algorithm for finding the boundary of a wireless sensor network. Ad Hoc Networks, 56, 56–71.

    Article  Google Scholar 

  24. Sha, C., Qiu, J.m., Li, S.y., Qiang, M.y., & Wang, R.c. (2016). A type of energy-efficient data gathering method based on single sink moving along fixed points. Peer-to-Peer Networking and Applications. https://doi.org/10.1007/s12083-016-0534-4

  25. Singh, S. K., & Kumar, P. (2020). A comprehensive survey on trajectory schemes for data collection using mobile elements in wsns. Journal of Ambient Intelligence and Humanized Computing, 11(1), 291–312.

    Article  Google Scholar 

  26. Verma, R.K., Pattanaik, K., & Bharti, S. (2015). An adaptive mechanism for improving resiliency in wireless sensor networks. In 2015 IEEE 10th international Conference on industrial and information systems (ICIIS) (pp. 525–530). IEEE

  27. Verma, R. K., Pattanaik, K., Bharti, S., & Saxena, D. (2019). In-network context inference in iot sensory environment for efficient network resource utilization. Journal of Network and Computer Applications, 130, 89–103.

    Article  Google Scholar 

  28. Wang, J., Cao, J., Ji, S., & Park, J. H. (2017). Energy-efficient cluster-based dynamic routes adjustment approach for wireless sensor networks with mobile sinks. The Journal of Supercomputing, 73(7), 3277–3290.

    Article  Google Scholar 

  29. Wen, W., Zhao, S., Shang, C., & Chang, C. Y. (2018). Eapc: Energy-aware path construction for data collection using mobile sink in wireless sensor networks. IEEE Sensors Journal, 18(2), 890–901.

    Article  Google Scholar 

  30. Wu S Chou W, & N.J.G.M. (2018). Delay-aware energy-efficient routing towards a path-fixed mobile sink in industrial wireless sensor networks. Sensors 18(3), 899

  31. Yarinezhad, R., & Naser Hashemi, S. (2018). An efficient data dissemination model for wireless sensor networks. Wireless Networks. https://doi.org/10.1007/s11276-018-1845-6

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shushant Kumar Jain.

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

Jain, S.K., Venkatadari, M., Shrivastava, N. et al. NHCDRA: a non-uniform hierarchical clustering with dynamic route adjustment for mobile sink based heterogeneous wireless sensor networks. Wireless Netw 27, 2451–2467 (2021). https://doi.org/10.1007/s11276-021-02585-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-021-02585-3

Keywords

Navigation