Joint Nodes and Sink Mobility Based Immune Routing-Clustering Protocol for Wireless Sensor Networks

Abstract

Recently, mobile wireless sensor network has drawn attention widely. In this paper, Joint Nodes and Sink Mobility based Immune routing-Clustering protocol (JNSMIC) is proposed to support the mobility of the sink and the sensor nodes together. It depends on using the mobile sink for solving the hot spot problem and the Multi-Objective Immune Algorithm (MOIA) for clustering the network and finding the visiting locations of the mobile sink. The JNSMIC protocol considers different objectives during the clustering process, namely the consumption energy, network coverage, link connection time (LCT), residual energy and mobility. Also, it reduces the computational time of finding cluster heads (CHs) by dividing it into two phases. In the first phase, the candidate CHs set is formed based on residual energy, mobility factor and LCT of sensor nodes. While in the second phase, the MOIA algorithm is utilized to determine the final CHs subject to reducing the communication cost, improving the packet delivery ratio and ensuring network stability. JNSMIC performs the clustering process only if the remaining energy is below a threshold value thus the computational time and overhead control packets are reduced. In JNSMIC, the deputy CH concept is considered to perform the task of CH during CH failure. Furthermore, the proposed protocol performs a fault-tolerance process after transmitting each frame to maintain the link stability among CHs and their members which improves the throughput. Simulation results show that the JNSMIC protocol can effectively ameliorate the throughput while simultaneously giving lower energy expenditure and end-to-end delay.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  1. 1.

    Pan, J. S., Kong, L., Sung, T. W., Tsai, P. W., & Snasel, W. (2018). α-fraction first strategy for hierarchical wireless sensor networks. International Journal of Technology, 19(6), 1717–1726.

    Google Scholar 

  2. 2.

    Silva, R., Zinonos, Z., Silva, J. S., & Vassiliou, V. (2011). Mobility in WSNs for critical applications. In IEEE Symposium on Computers and Commun., Kerkyra, Greece, ISCC (pp. 451–456).

  3. 3.

    Zhang, Y., Sun, L., Song, H., & Cao, X. (2014). Ubiquitous WSN for Healthcare: Recent advances and future prospects. IEEE Internet of Things J., 1(4), 311–318.

    Article  Google Scholar 

  4. 4.

    Chanak, P., Indrajit, B. I., Wang, J., & Sherratt, R. S. (2014). Obstacle avoidance routing scheme through optimal sink movement for home monitoring and mobile robotic consumer devices. IEEE Transactions on Consumer Electronics, 60(4), 596–604.

    Article  Google Scholar 

  5. 5.

    Wang, J., Zhang, Z., Li, B., Lee, S., & Sherratt, R. S. (2014). An enhanced fall detection system for elderly person monitoring using consumer home networks. IEEE Transactions on Consumer Electronics, 60(1), 23–29.

    Article  Google Scholar 

  6. 6.

    Yin, C., Xi, J., Sun, R., & Wang, J. (2018). Location privacy protection based on differential privacy strategy for big data in industrial internet of things. IEEE Transactions on Industrial Informatics, 14(8), 3628–3636.

    Article  Google Scholar 

  7. 7.

    Balamurali, R., & Kathiravan, K. (2015). A survey on mitigating hotspot problems in wireless sensor networks. International Journal of Applied Engineering Research, 10(3), 5913–5921.

    Google Scholar 

  8. 8.

    Zhao, M., Yang, Y., & Wang, C. (2015). Mobile data gathering with load balanced clustering and dual data uploading in wireless sensor networks. IEEE Transactions on Mobile Computing, 14(4), 770–785.

    Article  Google Scholar 

  9. 9.

    Zhu, C., Wu, S., Han, G., Shu, L., & Wu, H. (2015). A tree-cluster-based data-gathering algorithm for industrial WSNs with a mobile sink. IEEE Access, 3, 381–396.

    Article  Google Scholar 

  10. 10.

    Xie, G., & Pan, F. (2016). Cluster-based routing for the mobile sink in wireless sensor networks with obstacles. IEEE Access, 4, 2019–2028.

    Article  Google Scholar 

  11. 11.

    Wang, J., Gao, Y., Yin, X., Li, F., & Kim, H. (2018). An enhanced PEGASIS algorithm with mobile sink support for wireless sensor networks. Wireless Communications and Mobile Computing, 2018, 1–9.

    Google Scholar 

  12. 12.

    Velmani, R., & Kaarthick, B. (2015). An efficient cluster-tree based data collection scheme for large mobile wireless sensor networks. IEEE Sensors Journal, 15(4), 2377–2390.

    Article  Google Scholar 

  13. 13.

    Rady, A., Shokair, M., El-Rabaie, S., Saad, W., & Benaya, A. (2019). A novel energy efficient routing protocol using sink mobility for wireless sensor networks. IET Wireless Sensor Systems, 9(6), 405–415.

    Article  Google Scholar 

  14. 14.

    Sasirekha, S., & Swamynathan, S. (2017). Cluster-chain mobile agent routing algorithm for efficient data aggregation in wireless sensor network. Journal of Communications and Networks, 19(4), 392–401.

    Article  Google Scholar 

  15. 15.

    Abo-Zahhad, M., Ahmed, S. M., Sabor, N., & Sasaki, S. (2015). Mobile sink-based adaptive immune energy-efficient clustering protocol for improving the lifetime and stability period of wireless sensor networks. IET Sensor Journal, 15(8), 4576–4586.

    Article  Google Scholar 

  16. 16.

    Sangeetha, M., & Sabari, A. (2018). Prolonging network lifetime and optimizing energy consumption using swarm optimization in mobile wireless sensor networks. Sensor Review, 38(4), 534–541.

    Article  Google Scholar 

  17. 17.

    Tashtarian, F., Hossein, Y. M. M., Sohraby, K., & Effati, S. (2015). On maximizing the lifetime of wireless sensor networks in event-driven applications with mobile sinks. IEEE Transactions on Vehicular Technology, 64(7), 3177–3189.

    Google Scholar 

  18. 18.

    Gao, Y., Wang, J., Wu, W., Sangaiah, A. K., & Lim, S. (2019). A hybrid method for mobile agent moving trajectory scheduling using ACO and PSO in WSNs. Sensors, 19(3), 575.

    Article  Google Scholar 

  19. 19.

    Do-Seong, K., & Yeong-Jee, C. (2006). Self-organization routing protocol supporting mobile nodes for wireless sensor network. First Int. Multi- Symposiums on Computer and Computational Sciences, Hanzhou, Zhejiang, China (pp. 622–626).

  20. 20.

    Awwad, S. A. B., Ng, C. K., Noordin, N. K., & Rasid, M. F. A. (2011). Cluster based routing protocol for mobile nodes in wireless sensor network. Wireless Personal Communications, 61(2), 251–281.

    Article  Google Scholar 

  21. 21.

    Cakici, S., Erturk, I., Atmaca, S., & Karahan, A. (2014). A novel cross-layer routing protocol for increasing packet transfer reliability in mobile sensor networks. Wireless Personal Communications, 77(3), 2235–2254.

    Article  Google Scholar 

  22. 22.

    Sabor, N., Ahmed, S. M., 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 

  23. 23.

    Rady, A., Sabor, N., Shokair, M., & El-Rabaie, E. M. (2018). Mobility based genetic algorithm hierarchical routing protocol in mobile wireless sensor networks. In International Japan-Africa Conference on Electronics, Communications and Computations (JAC-ECC), Alexandria, Egypt (pp. 83–86).

  24. 24.

    Sara, G. S., & Sridharan, D. (2014). Routing in mobile wireless sensor network: A survey. Telecommunication System, 57(1), 51–79.

    Article  Google Scholar 

  25. 25.

    Chatterjee, S., Chakraborty, A., Dey, R., Banerjee, H., Pareek, S., Nayak, S., Gautam, A., Saha, H. N., Neyaz, M., & Dokania, M. (2017). A load balanced routing protocol for mobile sensor network. In 8th Annual Industrial Automation and Electromechanical Engineering Conference (IEMECON), Bangkok, Thailand (pp. 131–136).

  26. 26.

    Sarma, H. K. D., Mall, R., & Kar, A. (2016). E2R2: Energy-efficient and reliable routing for mobile wireless sensor networks. IEEE Systems Journal, 10(2), 604–616.

    Article  Google Scholar 

  27. 27.

    Saeed, N., & Nam, H. (2017). Energy efficient localization algorithm with improved accuracy in cognitive radio networks. IEEE Communications Letters, 21(9), 1–11.

    Article  Google Scholar 

  28. 28.

    Wang, B. (2011). Coverage problems in sensor networks: A survey. ACM Computing Surveys, 43(4), 1–53.

    MathSciNet  Article  Google Scholar 

  29. 29.

    Meng, J. T., Yuan, J. R., Feng, S. Z., & Wei, Y. J. (2013). An energy efficient clustering scheme for data aggregation in wireless sensor networks. Journal of Computer Science and Technology, 28(3), 564–573.

    MathSciNet  Article  Google Scholar 

  30. 30.

    Celestine, J., Vallepalli, K., Vinayaraj, T., Almotir J., & Abuzneid, A., (2015). An energy efficient flooding protocol for enhanced security in Wireless Sensor Networks’, Long Island Systems, Applied Science and Technology, Farmingdale, NY (pp. 1–6).

  31. 31.

    Fabio, F., Coello, C. A. C., & Repetto, M. (2009). Multi objective optimization and artificial immune system: A review. In: Handbook of Research on Artificial Immune Systems and Natural Computing, Medical Information Science Reference, Hershey, New York, NY, USA (pp. 1–21).

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Asmaa Rady.

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

Verify currency and authenticity via CrossMark

Cite this article

Rady, A., Shokair, M., El-Rabaie, ES.M. et al. Joint Nodes and Sink Mobility Based Immune Routing-Clustering Protocol for Wireless Sensor Networks. Wireless Pers Commun (2021). https://doi.org/10.1007/s11277-020-08066-8

Download citation

Keywords

  • Mobile wireless sensor networks (MWSN)
  • Mobile sink
  • Mobile sensors
  • Multi-objective immune algorithm
  • Connectivity