Skip to main content
Springer Nature Link
Log in

FABC-MACRD: Fuzzy and Artificial Bee Colony Based Implementation of MAC, Clustering, Routing and Data Delivery by Cross-Layer Approach in WSN

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

This paper demonstrates the Fuzzy and Artificial Bee Colony Based Implementation of MAC, Clustering, Routing and Data delivery by Cross-Layer approach in WSN (FABC-MACRD). The protocols of cross layer mechanism links of both the media accessibility and the energy proficient hierarchical based cluster routing. Thus for the selection of nodes the approach makes use of the fuzzy dependent CH selection technique. The major problem with hierarchical dependent clustering methodology is the congestion occurrence in CHs which are nearer to MS. This congestion generates the coverage problems as well as the network connectivity issues. Thus to rectify this issues the proposed FABC-MACRD approach combines of the network into non-similar clusters. The proposed approach utilizes the ABC optimization algorithm thus for the energy efficient and flexible transmission of data onto the Master Station, also performs the inter cluster routing commencing from CHs over the Master Station. The proposed methodology mainly includes three phases namely network association, nearest node detection phase and consistent-state stride. The performance analysis is carried out with different methodologies such as “UCR”, “ULCA”, “EAUCF” and with “IFUC”. After analysis our proposed FABC-MACRD approach shows better outcomes in terms of packet delivery, energy consumption and lifespan of the network.

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. Gong, W., Yang, X., Zhang, M., & Long, K. (2015). An adaptive path selection model for WSN multipath routing inspired by metabolism behaviors. Science China Information Sciences, 58(10), 1–15.

    Article  Google Scholar 

  7. Venayagamoorthy, G. K. K. (2009). A successful interdisciplinary course on computational intelligence. IEEE Computational Intelligence Magazine, 4(1), 14–23.

    Article  Google Scholar 

  8. 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 

  9. 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 

  10. Ranjan, R., & Varma, S. (2015). Challenges and implementation on cross layer design for wireless sensor networks. Wireless Personal Communications, 86(2), 1037–1060.

    Article  Google Scholar 

  11. Jia, D., Li, M., Zhu, H., & Zhang, B. (2016). Layer-cluster topology sensor node deployment for large-scale multi-nodes of WSN. Wireless Personal Communications, 94(4), 3035–3056.

    Article  Google Scholar 

  12. Rawat, P., Singh, K. D., Chaouchi, H., & Bonnin, J. M. (2014). Wireless sensor networks: a survey on recent developments and potential synergies. The Journal of supercomputing, 68(1), 1–48.

    Article  Google Scholar 

  13. Thangaraj, M., & Anuradha, S. (2016). Energy conscious deterministic self-healing new generation wireless sensor network: smart WSN using the Aatral framework. Journal of Wireless Networks, 23(4), 1267–1284.

    Article  Google Scholar 

  14. Kumar, N., Ghanshyam, C., & Sharma, A. K. (2015). Effect of multi-path fading model on T-ANT clustering protocol for WSN. Journal of Wireless Networks, 21(4), 1155–1162.

    Article  Google Scholar 

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

    Article  Google Scholar 

  16. 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 

  17. 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 

  18. 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 

  19. 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 

  20. 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 

  21. Xenakis, A., Foukalas, F., & Stamoulis, G. (2016). Cross-layer energy-aware topology control through simulated annealing for WSNs. Journal of Computers & Electrical Engineering, 56, 576–590.

    Article  Google Scholar 

  22. Gokturk, M. S., Gurbuz, O., & Erman, M. (2016). A practical cross layer cooperative MAC framework for WSNS. Journal of Computer Networks, 98, 57–71.

    Article  Google Scholar 

  23. Bagaa, M., Younis, M., Derhab, A., & Badache, N. (2014). Intertwined path formation and MAC scheduling for fast delivery of aggregated data in WSN. Journal on Computer Networks, 75, 331–350.

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. 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 

  26. Sasikala, T., Bhagyaveni, M. A., & Jawahar Senthil Kumar, V. (2016). Cross layered adaptive rate optimised error control coding for WSN. Journal of Wireless Networks, 22(6), 2071–2079.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Anna University, Chennai, Tamilnadu, India

    K. Kalaikumar

  2. Department of Computer Science and Engineering, Marian Engineering College, Trivandrum, 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.

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

Kalaikumar, K., Baburaj, E. FABC-MACRD: Fuzzy and Artificial Bee Colony Based Implementation of MAC, Clustering, Routing and Data Delivery by Cross-Layer Approach in WSN. Wireless Pers Commun 103, 1633–1655 (2018). https://doi.org/10.1007/s11277-018-5872-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-018-5872-5

Keywords