An Energy Efficient Routing Algorithm for WSNs Using Intelligent Fuzzy Rules in Precision Agriculture

  • V. PandiyarajuEmail author
  • R. Logambigai
  • Sannasi Ganapathy
  • Arputharaj Kannan


Many agricultural activities can be highly enhanced by using sensor networks and data mining techniques. One of these activities is the regulation of the quantity of water in cultivated fields. Moreover, wireless sensor network have become a more emerging technology in precision agriculture during the recent years. The important issue in the design of wireless sensor networks is the utilization of energy and to enhance the lifetime of the sensor nodes. In this paper, a new intelligent routing protocol has been proposed to improve the network lifetime and to provide energy efficiency in the routing process which is used to provide data to the irrigation system. This novel intelligent energy efficient routing protocol uses fuzzy rules and the protocol is called as Terrain based Routing using Fuzzy rules for precision agriculture. The fuzzy inference system developed in this work has been used to take decisions for routing. The system has been implemented and compared with two routing algorithms called Region Based Routing and Equalized Cluster Head Election Routing Protocol. The experimental results show that the proposed algorithm performs better than the other existing algorithms.


Terrain Wireless sensor networks Precision agriculture Cluster head Fuzzy inference Routing 



  1. 1.
    Chaudhary, D. D., Nayse, S. P., & Waghmare, L. M. (2011). Application of wireless sensor networks for greenhouse parameter control in precision agriculture. International Journal of Wireless and Mobile Networks (IJWMN),3, 140–149.CrossRefGoogle Scholar
  2. 2.
    Rault, T., Bouabdallah, A., & Challal, Y. (2014). Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks,67, 104–122.CrossRefGoogle Scholar
  3. 3.
    Logambigai, R., & Kannan, A. (2014). QEER: QoS aware energy efficient routing protocol for wireless sensor networks. In: 2014 Sixth international conference on advanced computing (ICoAC) (pp. 57–60). IEEE.Google Scholar
  4. 4.
    Arunraja, M., Malathi, V., & Sakthivel, E. (2015). Energy conservation in WSN through multilevel data reduction scheme. Microprocessors and Microsystems,39, 348–357.CrossRefGoogle Scholar
  5. 5.
    Selvi, M., Logambigai, R., Ganapathy, S., Sai Ramesh, L., Khanna Nehemiah, H. & Kannan A. (2016). Fuzzy temporal approach for energy efficient routing in WSN. In: Proceedings of the international conference on informatics and analytics (pp. 1–5). ACM.Google Scholar
  6. 6.
    Muthurajkumar, S., Ganapathy, S., Vijayalakshmi, M., & Kannan, A. (2017). An intelligent secured and energy efficient routing algorithm for MANETs. Wireless Personal Communications,96(2), 1753–1769.CrossRefGoogle Scholar
  7. 7.
    Heinzelman, W. R., Chandrakasan, A. & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Hawaii international conference on system sciences (pp. 1–10).Google Scholar
  8. 8.
    Lung, C. H., & Zhou, C. (2010). Using hierarchical agglomerative clustering in wireless sensor networks: An energy-efficient and flexible approach. Ad Hoc Networks,8, 328–344.CrossRefGoogle Scholar
  9. 9.
    Dong, X., Vuran, M. C., & Irmak, S. (2013). Autonomous precision agriculture through integration of wireless underground sensor networks with center pivot irrigation systems. Ad Hoc Networks,11, 1975–1987.CrossRefGoogle Scholar
  10. 10.
    Vellidis, G., Tucker, M., Perry, C., Kvien, C., & Bednarz, C. (2008). A real-time wireless smart sensor array for scheduling irrigation. Computers and Electronics in Agriculture,61, 44–50.CrossRefGoogle Scholar
  11. 11.
    Sudha, M. N., Valarmathi, M. L., & Babu, A. S. (2011). Energy efficient data transmission in automatic irrigation system using wireless sensor networks. Computers and Electronics in Agriculture,78, 215–221.CrossRefGoogle Scholar
  12. 12.
    Goumopoulos, C., Flynn, B., & Kameas, A. (2014). Automated zone-specific irrigation with wireless sensor actuator network and adaptable decision support. Computers and Electronics in Agriculture,105, 20–33.CrossRefGoogle Scholar
  13. 13.
    Shah, S. K., Rane, S. J., & Vishwakarma, D. (2012). A simulation study of behaviour of wireless motes with reference to parametric variation. International Journal of Advanced Research in Electrical Electronics and Instrumentation Engineering,1, 91–95.Google Scholar
  14. 14.
    Dehghani, S., Pourzaferani, M., & Barekatain, B. (2015). Comparison on energy-efficient cluster based routing algorithms in wireless sensor network. Procedia Computer Science,72, 535–542.CrossRefGoogle Scholar
  15. 15.
    More, A., & Raisinghani, V. (2017). A survey on energy-efficient coverage protocols in wireless sensor networks. Journal of King Saud University - Computer and Information Sciences, 29(4), 428–448.CrossRefGoogle Scholar
  16. 16.
    Selvi, M., Velvizhy, P., Ganapathy, S., Khanna Nehemiah, H., & Kannan, A. (2017). A rule based delay constrained energy efficient routing technique for wireless sensor networks. Cluster Computing,22, 10839–10848. Scholar
  17. 17.
    Sabet, M., & Naji, H. (2016). An energy efficient multi-level route-aware clustering algorithm for wireless sensor networks: A self-organized approach. Computers & Electrical Engineering,56, 399–417.CrossRefGoogle Scholar
  18. 18.
    Zhang, W., Han, G., Feng, Y., & Lloret, J. (2017). IRPL: An energy efficient routing protocol for wireless sensor networks. Journal of Systems Architecture,75, 35–49.CrossRefGoogle Scholar
  19. 19.
    Mohemed, R. E., Saleh, A. I., Abdelrazzak, M., & Samra, A. S. (2017). Energy-efficient routing protocols for solving energy hole problem in wireless sensor networks. Computer Networks,114, 51–66.CrossRefGoogle Scholar
  20. 20.
    Thangaramya, K., Logambigai, R., SaiRamesh, L., Kulothungan, K., Kannan, A., & Ganapathy, S. (2017). An energy efficient clustering approach using spectral graph theory in wireless sensor networks. In: Second international conference on recent trends and challenges in computational models (ICRTCCM) (pp. 126–129). IEEE.Google Scholar
  21. 21.
    Sun, X., Chen, H., Wu, X., Yin, X., & Song, W. (2016). Opportunistic communications based on distributed width-controllable braided multipath routing in wireless sensor networks. Ad Hoc Networks,36, 349–367.CrossRefGoogle Scholar
  22. 22.
    Kumar, V., & Kumar, S. (2016). Energy balanced position-based routing for lifetime maximization of wireless sensor networks. Ad Hoc Networks,52, 117–129.CrossRefGoogle Scholar
  23. 23.
    Javaid, N., Hussain, S., Ahmad, A., Imran, M., Khan, A., & Guizani, M. (2017). Region based cooperative routing in underwater wireless sensor networks. Journal of Network and Computer Applications,92, 31–41.CrossRefGoogle Scholar
  24. 24.
    Ganapathy, S., Sethukkarasi, R., Yogesh, P., Vijayakumar, P., & Kannan, A. (2014). An intelligent temporal pattern classification system using fuzzy temporal rules and particle swarm optimization. Sadhana,39, 283–302.MathSciNetCrossRefGoogle Scholar
  25. 25.
    Singh, R., & Verma, A. K. (2017). Energy efficient cross layer based adaptive threshold routing protocol for WSN. AEU-International Journal of Electronics and Communications,72, 166–173.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • V. Pandiyaraju
    • 1
    Email author
  • R. Logambigai
    • 1
  • Sannasi Ganapathy
    • 2
  • Arputharaj Kannan
    • 1
  1. 1.Department of Information Science and Technology, CEG CampusAnna UniversityChennaiIndia
  2. 2.School of Computing Science and EngineeringVellore Institute of TechnologyChennaiIndia

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