Cluster Computing

, Volume 22, Supplement 5, pp 10839–10848 | Cite as

A rule based delay constrained energy efficient routing technique for wireless sensor networks

  • M. Selvi
  • P. VelvizhyEmail author
  • S. Ganapathy
  • H. Khanna Nehemiah
  • A. Kannan


In wireless sensor networks (WSN), the nodes are used to collect and gather the data from different environments. Hence, the network consumes more energy which is the main and challenging issue in WSNs. Since the sensor is operating under battery, recharging is impossible and hence the lifetime of each sensor is an important issue. Therefore, it is necessary to introduce new and efficient techniques to extend the network lifetime. In this paper, a new delay constrained energy efficient routing technique is proposed for performing effective routing in WSNs. This approach introduces a delay constraint based reliable routing approach which reduces the energy consumption by constructing efficient clusters without increasing the end-to-end delay. Moreover, the proposed technique called the rule based clustering for routing model provides better performance in terms of network lifetime than the other existing techniques since they consume more energy during the formation of clusters and finding the shortest path. Moreover, additional overhead on the cluster head selection is tackled also using rules in this proposed model in an efficient manner by building balanced clusters. The main advantage of the proposed approach is that it extends the lifetime of the network and increases the throughput, energy efficiency, link quality and scalability. The experimental verification of this technique has been carried out using MATLAB simulations and proved that this model increases the packet delivery rate, network performance and reduces the delay and energy consumption.


Wireless sensor network Energy efficiency Depth First Search routing TED based clustering Data gathering Cluster head 


  1. 1.
    Anand, K., Ganapathy, S., Kulothungan, K., Yogesh, P., Kannan, A.: A rule based approach for attribute selection and intrusion detection in wireless sensor networks. Procedia Eng. 38, 1658–1664 (2012)CrossRefGoogle Scholar
  2. 2.
    Elain, R., Kevin, K.: Artificial Intelligent. Tata McGraw Hill, New York (2000)Google Scholar
  3. 3.
    Guo, P., Jiang, T., Zhang, K., Chen, H.-H.: Clustering algorithm in initialization of multi-hop wireless sensor networks. IEEE Trans. Wirel. Commun. 8(12), 5713–5717 (2009)CrossRefGoogle Scholar
  4. 4.
    Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of IEEE 33rd Annual Hawaii International Conference on System Sciences, pp. 1–10 (2000)Google Scholar
  5. 5.
    Heinzelman, W.B., Chandrakasan, A.P., Balakrishnan, H.: An application specific protocol architecture for wireless sensor network. IEEE Trans. Wirel. Commun. 1(4), 660–670 (2002)CrossRefGoogle Scholar
  6. 6.
    Hsieh, M.Y.: Data aggregation model using energy-efficient delay scheduling in multi-hop hierarchical wireless sensor networks. IET Commun. 5(18), 2703–2711 (2011)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Huang, R., Zhu, J., Yu, X.-T.: The Ant-based Algorithm for the data gathering routing structure in sensor networks. In: Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, pp. 4473–4478 (2006)Google Scholar
  8. 8.
    Huynh, T., Dinh-Duc, A.-V., Tran, C.-H.: Delay-constrained energy-efficient cluster-based multi-hop routing in wireless sensor networks. J. Commun. Netw. 18(4), 580–588 (2016)CrossRefGoogle Scholar
  9. 9.
    Kim, H.-Y.: An energy-efficient load balancing scheme to extend lifetime in wireless sensor networks. Cluster Comput. 19, 279–283 (2016)CrossRefGoogle Scholar
  10. 10.
    Kulothungan, K., Angel Arul Jothi, J., Kannan, A.: An adaptive fault tolerant routing protocol with error reporting scheme for wireless sensor networks. Eur. J. Sci. Res. 16(1), 19–32 (2011)Google Scholar
  11. 11.
    Logambigai, R., Arputharaj, K.: Fuzzy logic based unequal clustering for wireless sensor networks. Wirel. Netw. 22, 945–957 (2016)CrossRefGoogle Scholar
  12. 12.
    Mohamed, E., Zygmunt, J.H.: Energy-efficient protocol for cooperative networks. IEEE/ACM Trans. Netw. 19(2), 561–574 (2011)CrossRefGoogle Scholar
  13. 13.
    Park, J., Jung, Y., Kim, Y.-M.: Cost-effective multicast routings in wireless mesh networks with multiple gateways. Cluster Comput. 19, 1599–1605 (2016)CrossRefGoogle Scholar
  14. 14.
    Selvi, M., Logambigai, R., Ganapathy, S., Sai Ramesh, L., Khanna Nehemiah, H., Kannan, A.: Fuzzy temporal approach for energy efficient routing in WSN. In: Proceedings of the International Conference on Informatics and Analytics ACM, pp. 1–5 (2016)Google Scholar
  15. 15.
    Selvi, M., Kannan, A.: Agent based intelligent routing algorithm for energy efficient routing in wireless sensor network. Asian J. Res. Soc. Sci. Hum. 6(12), 642–650 (2016)Google Scholar
  16. 16.
    Shokouhifar, M., Jalali, A.: Optimized sugeno fuzzy clustering algorithm for wireless sensor networks. Eng. Appl. Artif. Intell. 60, 16–25 (2017)CrossRefGoogle Scholar
  17. 17.
    Singh, S., Chand, S., Kumar, R., Malik, A., Kumar, B.: NEECP: Novel energy-efficient clustering protocol for prolonging lifetime of WSNs. IET Wirel. Sens. Syst. 6(5), 151–157 (2016)CrossRefGoogle Scholar
  18. 18.
    Sivanantham, E., Ramakrishnan, M.: Energy-efficient sustainable cluster based neighbor discovery technique for wireless networks with directional antennas. Cluster Comput. 20, 1527–1534 (2017)CrossRefGoogle Scholar
  19. 19.
    Stephanie, L., Raghavendra, C.S.: PEGASIS: power-efficient gathering in sensor information systems. In: Proceedings of IEEE Aerospace Conference, pp. 1125–1130 (2002)Google Scholar
  20. 20.
    Tao, Z., Sheng, W., Shizhong, X., Hongfang, Y., Du, X.: Time delay based clustering in wireless sensor networks. In: International Conference on Wireless Communications and Networking, pp. 3956–3960 (2007)Google Scholar
  21. 21.
    Thota, C., Sundarasekar, R., Manogaran, G., Varatharajan, R., Priyan, M.K.: Centralized fog computing security platform for IoT and cloud in healthcare system. In: Exploring the Convergence of Big Data and the Internet of Things (IGI Global), pp. 141–154 (2018). doi: 10.4018/978-1-5225-2947-7.ch011
  22. 22.
    Tomoya, E., Makoto, A., Takizawa, M.: Energy-efficient delay time-based process allocation algorithm for heterogeneous server clusters. In: IEEE 29th International Conference on Advanced Information Networking and Applications, pp. 279–286 (2015)Google Scholar
  23. 23.
    Varatharajan, R., Manogaran, G., Priyan, M.K., Balaş, V.E., Barna, C.: Visual analysis of geospatial habitat suitability model based on inverse distance weighting with paired comparison analysis. Multimed. Tools Appl. (2017). doi: 10.1007/s11042-017-4768-9
  24. 24.
    Varatharajan, R., Manogaran, G., Priyan, M.K., Sundarasekar, R.: Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm. Cluster Comput. (2017). doi: 10.1007/s10586-017-0977-2
  25. 25.
    Varatharajan, R., Vasanth, K., Gunasekaran, M., Priyan, M., Gao, X.Z.: An adaptive decision based kriging interpolation algorithm for the removal of high density salt and pepper noise in images. Comput. Electr. Eng. (2017). doi: 10.1016/j.compeleceng.2017.05.035
  26. 26.
    Yao, M., Lin, C., Tian, Y.: Energy and delay minimization in cluster-based wireless sensor networks. In: IEEE International Conference on Green Computing and Communications, pp. 593–612 (2012)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • M. Selvi
    • 1
  • P. Velvizhy
    • 2
    Email author
  • S. Ganapathy
    • 3
  • H. Khanna Nehemiah
    • 4
  • A. Kannan
    • 1
  1. 1.Department of Information Science and TechnologyAnna UniversityChennaiIndia
  2. 2.Department of Computer Science and EngineeringAnna UniversityChennaiIndia
  3. 3.School of Computing Science and EngineeringVIT University-Chennai CampusChennaiIndia
  4. 4.Ramanujan Computing CentreAnna UniversityChennaiIndia

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