Modified firefly with fuzzy based clustering algorithm for cluster-based networks

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    Cluster-based network comprises a group of clusters with each cluster contains a collection of nodes. The control structures offer effective utilization of resources while managing large dynamic networks. Cluster based network is mainly used for effective load balancing. Different kinds of cluster-based architectures presented in the study for different usage. In cluster based networks, the choice of cluster heads (CH) in cluster based network is a challenging task. Presently, meta-heuristic algorithms become very popular and employed to select CHs effectively. In this paper, we introduce a modified firefly with fuzzy based clustering algorithm and it operates in two levels: modified firefly algorithm (MFA) for candidate CH selection and fuzzy logic for final CH selection. The traditional FF algorithm in modified by the inclusion of tumbling effect to develop MFA and it uses processing capability as a measure to identify the candidate CHs. Next, five input parameters named as residual energy, neighboring node distance, distance to main server, node centrality and node degree to select the final CHs from candidate CHs. An extensive experimentation takes place to verify the goodness of the MFFCA in terms of different performance measures and the results depicted that the MFFCA outperforms the compared clustering techniques.




  • Keywords

    Fiber Optic Communication; Dictionary Based Coding; Data Compression; Textual Dataset.

  • References

      [1] V.S. Pai, M. Aron, G. Banga, M. Svendsen, P. Druschel, W. Zwaenepoel, and E. Nahum, “Locality-aware request distribution in cluster-based network servers”, ACM Sigplan Notices, vol. 33, no. 11, pp.205-216, 1998.

      [2] M. Aron, D. Sanders, P. Druschel, and W. Zwaenepoel, “Scalable content-aware request distribution in cluster-based network servers”, In Proceedings of the 2000 Annual USENIX technical Conference (No. LABOS-CONF-2005-025), 2000.

      [3] Zhu, H., Tang, H. and Yang, T., “Demand-driven service differentiation in cluster-based network servers”, In INFOCOM 2001. Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE (Vol. 2, pp. 679-688). IEEE, 2001.

      [4] W.B. Heinzelman, A.P. Chandrakasan, and H. Balakrishnan, An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on wireless communications, vol. 1, no. 4, pp.660-670 2002.

      [5] S. Arjunan, and S. Pothula, “A survey on unequal clustering protocols in wireless sensor networks”, Journal of King Saud University-Computer and Information Sciences.

      [6] R. Xu, and D. Wunsch, “Survey of clustering algorithms”, IEEE Transactions on neural networks, vol. 16, no.3, pp.645-678, 2017.

      [7] M.T. Hagan, H.B. Demuth, M.H. Beale, and O. De Jesús, Neural network design, vol. 20, Boston: PwsPub, 1996.

      [8] R.S. Sutton, and A.G. Barto, Introduction to reinforcement learning (Vol. 135). Cambridge: MIT press, 1998.

      [9] G. Klir, and B. Yuan, Fuzzy sets and fuzzy logic (Vol. 4). New Jersey: Prentice hall, 1995.

      [10] J. Yen, and R. Langari, Fuzzy logic: intelligence, control, and information (Vol. 1). Upper Saddle River, NJ: Prentice Hall, 1999.

      [11] S. Arjunan, and P. Sujatha, “Lifetime maximization of wireless sensor network using fuzzy based unequal clustering and ACO based routing hybrid protocol”. Applied Intelligence, pp.1-18, 2017.

      [12] X.S. Yang, “Firefly algorithm, stochastic test functions and design optimization”. arXiv preprint arXiv:1003.1409, 2010.

      [13] S. Goyal, and M.S. Patterh, “Modified bat algorithm for localization of wireless sensor network”, Wireless Personal Communications, vol. 86, no.2, pp.657-670, 2016.

      [14] Deepak Gupta, Ashish Khanna, Lakshmanaprabu SK, Shankar K, Vasco Furtado, Joel J. P. C. Rodrigues, “Efficient Artificial Fish Swarm Based Clustering Approach on Mobility Aware Energy-Efficient for MANET”, Transactions on Emerging Telecommunications Technologies, 2018.

      [15] Pandi Selvam Raman, K. Shankar, Ilayaraja M, “Securing cluster-based routing against cooperative black hole attack in mobile ad hoc network”, International Journal of Engineering & Technology, 7. 9 (2018): 6-9.

      [16] Mohamed Elhoseny, K. Shankar, S. K. Lakshmanaprabu, Andino Maseleno, N. Arunkumar. Hybrid optimization with cryptography encryption for medical image security in Internet of Things. Neural Computing and Applications. 2018.

      [17] T. Avudaiappan, R. Balasubramanian, S. Sundara Pandiyan, M. Saravanan, S. K. Lakshmanaprabu, K. Shankar, “Medical Image Security Using Dual Encryption with Oppositional Based Optimization Algorithm, Journal of Medical Systems, 42.11 (2018) 1-11.

      [18] LakshmanaprabuS.K, Sachi Nandan Mohanty, K. Shankar, Arunkumar N, GustavoRamireze. “Optimal deep learning model for classification of lung cancer on CT images”, Future Generation Computer Systems. 2018.

      [19] K. Karthikeyan, R. Sunder, K. Shankar, S. K. Lakshmanaprabu, V. Vijayakumar, Mohamed Elhoseny, Gunasekaran Manogaran, Energy consumption analysis of Virtual Machine migration in cloud using hybrid swarm optimization (ABC–BA), The Journal of Supercomputing, 2018.




Article ID: 22843
DOI: 10.14419/ijet.v7i4.22843

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.