Skip to main content

Advertisement

Springer Nature Link
Log in

Fuzzy enabled congestion control by cross layer protocol utilizing OABC in WSN: combining MAC, routing, non-similar clustering and efficient data delivery

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Congestion control in networks turns to be a crucial and challenging issue. This paper presents the cross layer mechanism for the management of congestion using Fuzzy based Cross layer mechanism using Oppostional Artificial Bee Colony (FCOABC) protocol. The proposed protocol integrates the notion of media accessibility and energy proficient hierarchical based cluster routing for increasing the network lifespan and energy efficiency. This proposed cross layer protocol uses fuzzy logic by means of considering the fuzzy descriptors such as, communication link reliability, number of neighboring nodes and precipitant (residual energy) for CH selection. Then, the network is organized into non-similar sized clusters (i.e. the clusters that are much closer to the MS holding smaller sizes and the clusters that are far away from the MS having larger sizes) for addressing the hot spots problem in WSN. The smaller sized cluster are considered because, the CHs of the smaller sized clusters are largely closer to the master station; thus, they experienced only small amount of intra-cluster congestion and forwards the relay traffic effectively using their preserved energy. Finally, the proposed FCOABC protocol employed the Oppositional Artificial Bee Colony optimization algorithm for performing inter cluster multi-hop routing from CHs to master station; therefore, an energy efficient and reliable data transfer is achieved to the master station. The operation of FCOABC protocol is primarily divided into three stages namely, Network-Association stage, nearest node detection stage and consistent-state stage. The results of conventional clustering protocols such as, ULCA, UCR, IFUC, and EAUCF is used for comparing the performance of proposed FCOABC protocol. The simulation results proved the efficiency of proposed FCOABC protocol on other clustering protocols in terms of the evaluation metrics such as, network lifespan, energy consumption, and scalability.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Journal on Computer networks,52(12), 2292–2330.

    Article  Google Scholar 

  2. Gajjar, S. H., Pradhan, S. N., & Dasgupta, K. S. (2011). Wireless sensor network: Application led research perspective. In Recent advances in intelligent computational systems (RAICS) (pp. 025–030). IEEE.

  3. Rault, T., Bouabdallah, A., & Challal, Y. (2007). Energy efficiency in wireless sensor networks: A top-down survey. Journal on Computer Networks,67, 104–122.

    Article  Google Scholar 

  4. Wang, F., & Liu, J. (2011). Networked wireless sensor data collection: issues, challenges, and approaches. IEEE Communications Surveys & Tutorials,13(4), 673–687.

    Article  Google Scholar 

  5. Gajjar, S., Choksi, N., Sarkar, M., & Dasgupta, K. (2014). Comparative analysis of wireless sensor network motes. In Signal processing and integrated networks (SPIN) (pp. 426–431). IEEE.

  6. Chen, G., Li, C., Ye, M., & Jie, W. (2009). An unequal cluster-based routing protocol in wireless sensor networks. Journal on Wireless Networks,15(2), 193–207.

    Article  Google Scholar 

  7. Zhao, X., & Wang, N. (2014). An unequal layered clustering approach for large scale wireless sensor networks. International Journal on Future Computer and Communication,1(2), 750–756.

    Google Scholar 

  8. Bagci, H., & Yazici, A. (2013). An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Journal of Applied Soft Computing,13(4), 1741–1749.

    Article  Google Scholar 

  9. Sundararaj, V. (2018). Optimal task assignment in mobile cloud computing by queue based Ant-Bee algorithm. Wireless Personal Communications. https://doi.org/10.1007/s11277-018-6014-9.

    Article  Google Scholar 

  10. 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 and Security, 77, 277–288.

    Article  Google Scholar 

  11. 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.

    Article  Google Scholar 

  12. Tambe, S., Kumar, V., & Bhusari, R. (2018). Magnetic induction based cluster optimization in non-conventional WSNs: A cross layer approach. AEU - International Journal of Electronics and Communications, 93, 53–62.

    Article  Google Scholar 

  13. Singh, R., Rai, B. K., & Bose, S. K. (2017). A low delay cross-layer MAC protocol for k-covered event driven wireless sensor networks. IEEE Sensors Letters, 1(6), 1–4.

    Article  Google Scholar 

  14. Ahmed, A. M., & Paulus, R. (2017). Congestion detection technique for multipath routing and load balancing in WSN. Wireless Networks, 23(3), 881–888.

    Article  Google Scholar 

  15. Kalaikumar, K., & Baburaj, E. (2018). FABC-MACRD: Fuzzy and artificial Bee colony based implementation of MAC, clustering, routing and data delivery by cross-layer approach in WSN. Wireless Personal Communications. https://doi.org/10.1007/s11277-018-5872-5.

    Article  Google Scholar 

  16. Tizhoosh, H. R. (2005). Opposition-based learning: A new scheme for machine intelligence. In Proceedings of international conference on computational intelligence for modelling, control and automation (pp. 695–701).

  17. Sundararaj, V. (2016). An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. International Journal of Intelligent Engineering and Systems, 9(3), 117–126.

    Article  Google Scholar 

  18. Mao, S., Zhao, C., Zhou, Z., & Ye, Y. (2015). An improved fuzzy unequal clustering algorithm for wireless sensor network. Journal of Mobile Network Application,18(2), 206–214.

    Article  Google Scholar 

  19. Kim, J., et al. (2008). CHEF: Cluster Head Election mechanism using Fuzzy logic in Wireless Sensor Networks. In International conference on advanced communication technology (pp. 654–659). IEEE.

  20. Leonard, B., et al. (2011). Evaluation of an intelligent fuzzy based cluster head selection system using different parameters. In International conference on advanced information networking and applications (pp. 388–395). IEEE.

  21. Hoda, T., et al. (2012). An energy aware distributed clustering protocol in Wireless Sensor Networks using fuzzy logic. Journal of Ad hoc Networks,10(7), 1469–1481.

    Article  Google Scholar 

  22. Wei, D., et al. (2011). An energy efficient clustering solution for Wireless Sensor Networks. IEEE Transactions on Wireless Communications,10(11), 3973–3983.

    Article  Google Scholar 

  23. Ebrahimnejad, A., Tavana, M., & Alrezaamiri, H. (2016). A novel artificial bee colony algorithm for shortest path problems with fuzzy arc weights. Measurement,93, 48–56.

    Article  Google Scholar 

  24. Hashim, H. A., Ayinde, B. O., & Abido, M. A. (2014). Optimal placement of relay nodes in wireless sensor network using artificial bee colony algorithm. Journal of Network and Computer Applications,64, 239–248.

    Article  Google Scholar 

  25. Karaboga, D., Gorkemli, B., Ozturk, C., & Karaboga, N. (2014). A comprehensive survey: Artificial bee colony (ABC) algorithm and applications. Journal of Artificial Intelligence,42(1), 21–57.

    Google Scholar 

  26. Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless micro sensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences (Vol. 2).

  27. Tyagi, S., Gupta, S., Tanwar, S., & Kumar, N. (2013). Ehe-leach: enhanced heterogeneous leach protocol for life time enhancement of wirelesss ns. In 2013 international conference on advances in computing, communications and informatics (ICACCI) (pp. 1485–1490).

  28. Jiang, C.-J., Shi, W. R., Xiang, M., & Tang, X. L. (2010). Energy-balanced unequal clustering protocol for wireless sensor networks. Journal of China Universities of Posts and Telecommunications,17(4), 94–99.

    Article  Google Scholar 

  29. Wu, D., Bao, L., & Liu, C. H. (2013). Scalable channel allocation and access scheduling for wireless internet-of-things. IEEE Sensors Journal,13(10), 3596–3604.

    Article  Google Scholar 

  30. Wu, D., Bao, L., Regan, A. C., & Talcott, C. L. (2013). Large-scale access scheduling in wireless mesh networks using social centrality. Journal of Parallel and Distributed Computing,73(8), 1049–1065.

    Article  MATH  Google Scholar 

  31. Pahlavan, K., & Levesque, A. H. (2005). Wireless information networks. New York: Wiley.

    Book  Google Scholar 

  32. Jang, J. S. R., Sun, C. T., & Mizutani, E. (2003). Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence. Upper Saddle River: Prentice Hall Publications.

    Google Scholar 

  33. Karaboga, D., & Akay, B. (2011). A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems. Applied Soft Computing,11, 3021–3031.

    Article  Google Scholar 

  34. Marappan, P., & Rodrigues, P. (2016). An energy efficient routing protocol for correlated data using CL-LEACH in WSN. Journal of Wireless Networks,22(4), 1415–1423.

    Article  Google Scholar 

  35. Sarwesh, P., Shet, N. S. V., & Chandrasekaran, K. (2018). ETRT–cross layer model for optimizing transmission range of nodes in low power wireless networks–an internet of things perspective. Physical Communication, 29, 307–318.

    Article  Google Scholar 

  36. Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications,1(4), 660–670. https://doi.org/10.1109/TWC.2002.804190.

    Article  Google Scholar 

  37. Rajagopalan, R., & Varshney, P. (2006). Data aggregation techniques in sensor networks: A survey. IEEE Communications and Surveys and Tutorials,8(4), 48–63. https://doi.org/10.1109/COMST.2006.283821.

    Article  Google Scholar 

  38. The Network Simulator NS-2 Documentation. http://www.isi.edu/nsnam/ns/. Accessed October 18, 2015.

  39. MathWorks Documentation Center. http://www.mathworks.in/help/matlab/. Accessed October 18, 2015.

  40. Arboleda, L. M., & Nasser, N. (2006). Comparison of clustering algorithms and protocols for wireless sensor networks. In IEEE conference on electrical and computer engineering (pp. 1787–1792).

Download references

Author information

Authors and Affiliations

  1. Anna University, Chennai, Tamilnadu, India

    K. Kalaikumar

  2. Department of Computer Science and Engineering, Marian Engineering College, Kazhakuttom, Kerala, India

    E. Baburaj

Authors
  1. K. Kalaikumar
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. E. Baburaj
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to K. Kalaikumar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kalaikumar, K., Baburaj, E. Fuzzy enabled congestion control by cross layer protocol utilizing OABC in WSN: combining MAC, routing, non-similar clustering and efficient data delivery. Wireless Netw 26, 1085–1103 (2020). https://doi.org/10.1007/s11276-018-1848-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-018-1848-3

Keywords