Skip to main content
Log in

Optimization Based Multi-Objective Weighted Clustering For Remote Monitoring System in WSN

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Wireless Sensor Network (WSN) is generally considered as a standout amongst the most critical advancements for the twenty-first century, it normally comprises multifunctional wireless sensor nodes, with detecting, communications, and calculation capacities. Clustering the random nodes in WSN is a challenging task with high performance. This paper presents the new clustering model to monitor the eco-friendly mobile network by clustering the sensor nodes and to enhance the Quality of Service of that optimal network in WSN. The proposed Multi-Objective Weighted Clustering model groups the arbitrary nodes and afterward the optimal network is achieved by the optimization of network parameters. For optimizing the network parameters, a metaheuristic algorithm i.e. Improved Fruit Fly Optimization is introduced. With the goal of assessing the Coverage Efficiency (CE) and network user satisfaction of the accomplished optimal mobile network in WSN, the remote sensor monitoring process is applied. Sensor monitoring helps to know the network users and furthermore to improve the CE of WSN, contrasted with existing work.

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
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Abdolmaleki, N., Ahmadi, M., Malazi, H. T., & Milardo, S. (2017). Fuzzy topology discovery protocol for SDN-based wireless sensor networks. Simulation Modelling Practice and Theory, 79, 54–68.

    Article  Google Scholar 

  2. Ahmed, G., Zou, J., Fareed, M. M. S., & Zeeshan, M. (2016). Sleep-awake energy efficient distributed clustering algorithm for wireless sensor networks. Computers & Electrical Engineering, 56, 385–398.

    Article  Google Scholar 

  3. Althunibat, S., Khalifeh, A., & Mesleh, R. (2018). A low-interference decision-gathering scheme for critical event detection in clustered wireless sensor network. Physical Communication, 26, 149–155.

    Article  Google Scholar 

  4. Alumona, T. L., Idigo, V. E., & Nnoli, K. P. (2014). Remote monitoring of patients health using wireless sensor networks (WSNs). IPASJ International Journal of Electronics & Communication, 2(9), 90–95.

    Google Scholar 

  5. Bozorgi, S. M., Rostami, A. S., Hosseinabadi, A. A. R., & Balas, V. E. (2017). A new clustering protocol for energy harvesting-wireless sensor networks. Computers & Electrical Engineering, 64, 233–247.

    Article  Google Scholar 

  6. Chatei, Y., Ghoumid, K., Hammouti, M., & Hajji, B. (2017). Efficient coding techniques algorithm for cluster-heads communication in wireless sensor networks. AEU-International Journal of Electronics and Communications, 82, 294–304.

    Article  Google Scholar 

  7. Chen, D. R. (2015). A link-and hop-constrained clustering for multi-hop wireless sensor networks. Computer Communications, 72, 78–92.

    Article  Google Scholar 

  8. Deepa, O., & Suguna, J. (2017). An optimized QoS-based clustering with multipath routing protocol for wireless sensor networks. Journal of King Saud University-Computer and Information Sciences, 32(7), 763–774. https://doi.org/10.1016/j.jksuci.2017.11.007.

    Article  Google Scholar 

  9. Deif, D., & Gadallah, Y. (2015, December). Wireless Sensor Network deployment using stochastic optimization techniques-a comparative study. In Computing and Network Communications (CoCoNet), 2015 International Conference on (pp. 131–138). IEEE.

  10. Elhoseny, M., Tharwat, A., Yuan, X., & Hassanien, A. E. (2018). Optimizing K-coverage of mobile WSNs. Expert Systems with Applications, 92, 142–153.

    Article  Google Scholar 

  11. 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 

  12. Hacioglu, G., Kand, V. F. A., & Sesli, E. (2016). Multi objective clustering for wireless sensor networks. Expert Systems with Applications, 59, 86–100.

    Article  Google Scholar 

  13. Jovanovic, M. D., Stojanovic, I. Z., Djosic, S. M., & Djordjevic, G. L. (2016). Intra-cluster tone-based contention resolution mechanism for wireless sensor networks. Computers & Electrical Engineering, 56, 485–497.

    Article  Google Scholar 

  14. Khedo, K. K., Perseedoss, R., & Mungur, A. (2010). A wireless sensor network air pollution monitoring system. arXiv preprint arXiv:1005.1737.

  15. Lakshmi, N. S. R., Babu, S., & Bhalaji, N. (2017). Analysis of clustered QoS routing protocol for the distributed wireless sensor network. Computers & Electrical Engineering, 64, 173–181.

    Article  Google Scholar 

  16. Mann, P. S., & Singh, S. (2017). Improved metaheuristic based energy-efficient clustering protocol for wireless sensor networks. Engineering Applications of Artificial Intelligence, 57, 142–152.

    Article  Google Scholar 

  17. Mirzaie, M., & Mazinani, S. M. (2017). Adaptive MCFL: An adaptive multi-clustering algorithm using fuzzy logic in wireless sensor network. Computer Communications, 111, 56–67.

    Article  Google Scholar 

  18. Moh’d Alia, O. (2017). Dynamic relocation of mobile base station in wireless sensor networks using a cluster-based harmony search algorithm. Information Sciences, 385, 76–95.

    Article  Google Scholar 

  19. Mujica, G., Portilla, J., & Riesgo, T. (2015). Performance evaluation of an AODV-based routing protocol implementation by using a novel in-field WSN diagnosis tool. Microprocessors and Microsystems, 39(8), 920–938.

    Article  Google Scholar 

  20. Narawade, V., & Kolekar, U. D. (2018). ACSRO: adaptive cuckoo search based rate adjustment for optimized congestion avoidance and control in wireless sensor networks. Alexandria Engineering Journal, 57, 131–145.

    Article  Google Scholar 

  21. Oladimeji, M. O., Turkey, M., & Dudley, S. (2017). HACH: Heuristic Algorithm for Clustering Hierarchy protocol in wireless sensor networks. Applied Soft Computing, 55, 452–461.

    Article  Google Scholar 

  22. Ouchitachen, H., Hair, A., & Idrissi, N. (2017). Improved multi-objective weighted clustering algorithm in Wireless Sensor Network. Egyptian Informatics Journal, 18, 45–54.

    Article  Google Scholar 

  23. Pan, W. T. (2012). A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowledge-Based Systems, 26, 69–74.

    Article  Google Scholar 

  24. Ram, S. S., Nedic, A., & Veeravalli, V. V. (2007). Stochastic incremental gradient descent for estimation in sensor networks. In Signals, Systems and Computers, 2007. ACSSC 2007. Conference Record of the Forty-First Asilomar Conference on (pp. 582–586). IEEE.

  25. Rekha, K. S., Sreenivas, T. H., & Kulkarni, A. D. (2018). Remote monitoring and reconfiguration of environment and structural health using wireless sensor networks. Materials Today: Proceedings, 5, 1169–1175.

    Google Scholar 

  26. Rotariu, C., Bozomitu, R. G., Cehan, V., Pasarica, A., & Costin, H. (2015). A wireless sensor network for remote monitoring of bioimpedance. In Electronics Technology (ISSE), 2015 38th International Spring Seminar on IEEE, 487–490.

  27. Shokouhifar, M., & Jalali, A. (2017). Optimized sugeno fuzzy clustering algorithm for wireless sensor networks. Engineering applications of artificial intelligence, 60, 16–25.

    Article  Google Scholar 

  28. Song, C., & Fan, Y. (2018). Coverage control for mobile sensor networks with limited communication ranges on a circle. Automatica, 92, 155–161.

    Article  MathSciNet  Google Scholar 

  29. 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 & Security, 77, 277–288.

    Article  Google Scholar 

  30. Van Khoa, V., & Takayama, S. (2018). Wireless sensor network in landslide monitoring system with remote data management. Measurement, 118, 214–229.

    Article  Google Scholar 

  31. Xu, X., Liang, W., & Xu, Z. (2014). Remote monitoring cost minimization for an unreliable sensor network with guaranteed network throughput. Information Processing in Agriculture, 1(2), 83–94.

    Article  Google Scholar 

  32. Zhang, L., Cai, L. B., Li, M., & Wang, F. H. (2009). A method for least-cost QoS multicast routing based on genetic simulated annealing algorithm. Computer Communications, 32, 105–110.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tibin Mathew Thekkil.

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

Thekkil, T.M., Prabakaran, N. Optimization Based Multi-Objective Weighted Clustering For Remote Monitoring System in WSN. Wireless Pers Commun 117, 387–404 (2021). https://doi.org/10.1007/s11277-020-07874-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07874-2

Keywords

Navigation