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Evaluation of Clustering Algorithms in Ad Hoc Mobile Networks

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Abstract

Ad hoc mobile networks are free of any infrastructures and their nodes are not aware of the connections of the network locating in, since the structure of these networks is dynamic. To send data to other nodes, each node should detect the sending path and then save it. Due to their dynamic nature, these types of networks face design complexity and limitations such as a lack of specific infrastructure and the change of the infrastructure with passing the time, the limitation of energy, bandwidth, and the considerations of quality and security. Therefore, bandwidth optimization, power and energy control and an improvement in transmission quality are challenges of these types of networks in routing. To meet these challenges, the node clustering methods were welcomed for less energy consumption and longer network lifetime. In this paper, we deal with a systematic literature review of different clustering methods and propose a general categorization for them. Furthermore, we compare the performance the methods as well as the related algorithms and their strengths and weaknesses. Finally, we rank the algorithms regarding the four parameters of transmission range, mobility speed, battery, and connectivity degree using the multi-criterion decision-making and analytical hierarchical process techniques.

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Notes

  1. Lowest ID adaptive ID reassignment algorithm.

  2. Cluster-based link state routing.

  3. Cluster topology control.

  4. Connected dominating set.

  5. Leader election protocol.

  6. Merging clustering algorithm.

  7. Backbone formation algorithm.

  8. Chang-Roberts.

  9. High-connectivity cluster.

  10. Connectivity based k-hop clustering.

  11. Adaptive cluster load balance method.

  12. Adaptive Multihop Clustering.

  13. Cluster Technique in Mobile ad hoc Networks.

  14. Proactive source routing protocol.

  15. Distance sequence distance vector.

  16. Mobility-based D-Hop clustering algorithm for mobile ad hoc networks.

  17. Mobility-based frame work for adaptive clustering.

  18. Stability-based clustering.

  19. Three-hop between adjacent clusterheads.

  20. Least cluster change algorithm.

  21. Load balancing clustering.

  22. Virtual ID.

  23. New clustering schemes for energy conservation.

  24. Weighted clustering algorithm.

  25. Vote-based clustering algorithm.

  26. Connectivity, energy and mobility-driven weighted clustering algorithm.

  27. Node-based cluster routing algorithm.

  28. Weight-based double star embedded clustering.

  29. Neighbor strength.

References

  1. Kale, A., Ruchia, Ms, & Gupta, S. R. (2013). An overview of MANET ad hoc network. International Journal of Computer Science and Applications,6(2), 257–264.

    Google Scholar 

  2. Singh, G., & Singh, J. (2012). MANET: A study of challenges and routing principles. International Journal of Advanced and Innovative Research,1(1), 514–523.

    Google Scholar 

  3. Bakshi, A., Sharma, A. K., & Mishra, A. (2013). Significance of mobile ad-hoc networks (MANETS). International Journal of Innovative Technology and Exploring Engineering,2(4), 899–912.

    Google Scholar 

  4. Goyal, N., & Gaba, A. (2013). A review over MANET—Issues and challenges. International Journal of Enhanced Research in Management & Computer Applications,2(5), 24–36.

    Google Scholar 

  5. Kumar, M., & Mishra, R. (2012). An overview of MANET: History, challenges and applications. Indian Journal of Computer Science and Engineering,3(1), 184–199.

    MathSciNet  Google Scholar 

  6. Rani, V., & Dhir, R. (2013). A study of ad-hoc network: A review. International Journal of Advanced Research in Computer Science and Software Engineering,3(3), 58–64.

    Google Scholar 

  7. Tai, W. Y., Tan, C. T., & Lau, S. P. (2012). Towards utilizing flow label IPv6 in implicit source routing for dynamic source routing in wireless ad hoc network. In IEEE symposium on computers & informatics (pp. 101–106).

  8. Gavalas, D., Pantziou, G., Konstantopulos, Ch., & Mamalis, B. (2006). Lowest-ID with adaptive ID reassignment: A novel mobile ad-hoc networks clustering algorithm. In The 1st IEEE international symposium on wireless pervasive computing (p. 5).

  9. Amis, A. D., Prakash, R., Vuong, T. H. P., & Huynh, D. T. (2000). Max-Min D-cluster formation in wireless ad hoc networks. In The 19th annual joint conference of the IEEE computer and communications societies (pp. 32–41).

  10. Guizani, B., Ayeb, B., & Koukam, A. (2012). A new cluster-based link state routing for mobile ad hoc networks. In IEEE international conference on communications and information technology (pp. 196–201).

  11. Dagdeviren, O., & Erciyes, K. (2008). A hierarchical leader election protocol for mobile ad hoc networks. Computational Science,5101(1), 509–511.

    Google Scholar 

  12. Gerla, M., & Tsai, J. T. (1995). Multicluster, mobile, multimedia radio network. Wireless Networks,1(5), 255–265.

    Article  Google Scholar 

  13. Chen, G., Nocetti, F., Gonzalez, J., & Stojmenovic, I. (2002). Connectivity based k-hop clustering in wireless networks. In: The 35th IEEE annual Hawaii international conference on system sciences (pp. 2450–2459).

  14. Li, F., Zhang, S., Wang, X., Xue, X., & Shen, H. (2004). Vote-based clustering algorithm in mobile ad hoc networks. In Springer international conference on networking technologies (pp. 13–23).

  15. Ohta, T., Inoue, S., & Kakuda, Y. (2003). An adaptive multihop clustering scheme for highly mobile ad hoc networks. In IEEE 6th international symposium on autonomous decentralized systems (pp. 293–300).

  16. Panda, I. (2003). A clustering approach in mobile ad-hoc networks routing. International journal of computer Science & Engineering Technology, Citeseer,4(03), 104–106.

    Google Scholar 

  17. Er, I. I., & Seah, W. K. G. (2004). Mobility-based D-hop clustering algorithm for mobile ad hoc networks. In IEEE conference on wireless communications and networking (pp. 2359–2364).

  18. Madonald, A. B., & Znati, T. F. (1999). A mobility-based frame work for adaptive clustering in wireless ad hoc networks. IEEE Journal on Selected Areas in Communications,17(8), 1466–1487.

    Article  Google Scholar 

  19. Yang, C. C., & Chang, Y. C. (2008). A stability-based clustering technique and routing protocol for mobile ad hoc networks. Journal of Information Science and Engineering,24(2), 469–481.

    Google Scholar 

  20. Yu, J. Y., & Chong, P. H. J. (2003). 3hBAC (3-hop between adjacent cluster heads): A novel non-overlapping clustering algorithm for mobile ad hoc networks. In IEEE Pacific Rim conference on communications, computers and signal processing (pp. 318–321).

  21. Chiang, C. C. et al. (1997). Routing in clustered multihop, mobile wireless networks with fading channel. In IEEE SICON (pp. 197–211).

  22. Amis, A. D., & Prakash, R. (2000). Load-balancing clusters in wireless ad hoc networks. In The 3rd IEEE symposium on application-specific systems and software engineering technology (pp. 25–32).

  23. Ryu, J. H., Song, S., & Cho, D. H. (2001). New clustering schemes for energy conservation in two-tiered mobile ad-hoc networks. IEEE Transactions on Vehicular Technology,3(6), 862–866.

    Google Scholar 

  24. Chatterjee, M., Das, S. K., & Turgut, D. (2000). An on-demand weighted clustering algorithm (WCA) for ad hoc networks. In IEEE conference on global telecommunications (pp. 1697–701).

  25. Tolba, F. D., Magoni, D., & Lorenz, P. (2007). Connectivity, energy & mobility driven weighted clustering algorithm. In IEEE conference on global telecommunications, (pp. 2786–2790).

  26. Uikey, C. (2013). Node based cluster routing algorithm for mobile ad-hoc network. IEEE International Journal of Advanced Research in Computer and Communication Engineering,2(7), 2567–2571.

    Google Scholar 

  27. Janakiraman, T. N., & Senthil Thilak, A. (2011). A weight based double star embedded, clustering of homogeneous mobile ad hoc networks using graph theory. In The 1st international conference on computer science and information technology (pp. 329–339).

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Acknowledgements

Authors would like to thank University of Kashan to support this study.

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Authors and Affiliations

  1. Department of Computer Engineering, University of Kashan, Kashan, Iran

    Seyed Ali Sharifi & Seyed Morteza Babamir

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  1. Seyed Ali Sharifi
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  2. Seyed Morteza Babamir
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Correspondence to Seyed Morteza Babamir.

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Sharifi, S.A., Babamir, S.M. Evaluation of Clustering Algorithms in Ad Hoc Mobile Networks. Wireless Pers Commun 109, 2147–2186 (2019). https://doi.org/10.1007/s11277-019-06673-8

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