Skip to main content

Advertisement

SpringerLink
Log in

CDABC: chaotic discrete artificial bee colony algorithm for multi-level clustering in large-scale WSNs

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Artificial bee colony or ABC is an interesting meta-heuristic algorithm designed to solve various continuous optimization problems. However, it cannot be directly applied to solve discrete problems such as clustering of sensor nodes in the wireless sensor networks (WSNs). For this purpose, in this paper, we present a chaotic discrete version of the ABC algorithm, denoted as chaotic discrete ABC (CDABC). By using the CDABC algorithm, we propose a novel clustering protocol that can be used to organize WSNs into multiple levels of clusters to reduce their energy consumption. The main objective of this protocol is to improve WSN’s lifetime by selecting appropriate nodes as cluster heads in each clustering level and reducing the energy costs of the inter-cluster and intra-cluster communications. Extensive simulations results validate the effectiveness of the proposed CDABC-based multi-level clustering protocol in improving the network lifetime.

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

Access this article

Log in via an institution

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
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28

Similar content being viewed by others

References

  1. Zhen H, Li Y, Zhang G-J (2013) Efficient and dynamic clustering scheme for heterogeneous multi-level wireless sensor networks. Acta Autom Sin 39:454–460

    Article  Google Scholar 

  2. Sert SA, Bagci H, Yazici A (2015) MOFCA: multi-objective fuzzy clustering algorithm for wireless sensor networks. Appl Soft Comput 30:151–165

    Article  Google Scholar 

  3. Masdari M, Nabavi SS, Ahmadi V (2016) An overview of virtual machine placement schemes in cloud computing. J Netw Comput Appl 66:106–127

    Article  Google Scholar 

  4. Low CP, Fang C, Ng JM, Ang YH (2008) Efficient load-balanced clustering algorithms for wireless sensor networks. Comput Commun 31:750–759

    Article  Google Scholar 

  5. Lung C-H, Zhou C (2010) Using hierarchical agglomerative clustering in wireless sensor networks: an energy-efficient and flexible approach. Ad Hoc Netw 8:328–344

    Article  Google Scholar 

  6. Saha S, Chaki R (2012) Hierarchical cluster based query-driven routing protocol for wireless sensor networks. In: Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (India 2012) held in Visakhapatnam, India, pp 657–667

  7. Masdari M, Bazarchi SM, Bidaki M (2013) Analysis of secure LEACH-based clustering protocols in wireless sensor networks. J Netw Comput Appl 36:1243–1260

    Article  Google Scholar 

  8. Ari AAA, Yenke BO, Labraoui N, Damakoa I, Gueroui A (2016) A power efficient cluster-based routing algorithm for wireless sensor networks: honeybees swarm intelligence based approach. J Netw Comput Appl 69:77–97

    Article  Google Scholar 

  9. Yuan X, Elhoseny M, El-Minir HK, Riad AM (2017) A genetic algorithm-based, dynamic clustering method towards improved WSN longevity. J Netw Syst Manag 25(1):21–46

    Article  Google Scholar 

  10. Barati H, Movaghar A, Rahmani AM (2015) EACHP: energy aware clustering hierarchy protocol for large scale wireless sensor networks. Wirel Pers Commun 85:765–789

    Article  Google Scholar 

  11. Baranidharan B, Santhi B (2016) DUCF: distributed load balancing unequal clustering in wireless sensor networks using fuzzy approach. Appl Soft Comput 40:495–506

    Article  Google Scholar 

  12. Bagci H, Yazici A (2013) An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Appl Soft Comput 13:1741–1749

    Article  Google Scholar 

  13. Aslam M, Shah T, Javaid N, Rahim A, Rahman Z, Khan Z (2012) CEEC: centralized energy efficient clustering a new routing protocol for WSNs. In: 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), pp 103–105

  14. Elhabyan RS, Yagoub MC (2015) Two-tier particle swarm optimization protocol for clustering and routing in wireless sensor network. J Netw Comput Appl 52:116–128

    Article  Google Scholar 

  15. Santos AC, Duhamel C, Belisário LS (2016) Heuristics for designing multi-sink clustered WSN topologies. Eng Appl Artif Intell 50:20–31

    Article  Google Scholar 

  16. Shankar T, Shanmugavel S, Rajesh A (2016) Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks. Swarm Evol Comput 30:1–10

    Article  Google Scholar 

  17. Song M, Zhao C (2011) Unequal clustering algorithm for WSN based on fuzzy logic and improved ACO. J China Univ Posts Telecommun 18:89–97

    Google Scholar 

  18. Wang J, Cao Y, Li B, Kim H, Lee S (2017) Particle swarm optimization based clustering algorithm with mobile sink for WSNs. Fut Gener Comput Syst 76:452–457

    Article  Google Scholar 

  19. Hacioglu G, Kand VFA, Sesli E (2016) Multi objective clustering for wireless sensor networks. Expert Syst Appl 59:86–100

    Article  Google Scholar 

  20. Iqbal M, Naeem M, Anpalagan A, Qadri N, Imran M (2016) Multi-objective optimization in sensor networks: optimization classification, applications and solution approaches. Comput Netw 99:134–161

    Article  Google Scholar 

  21. Ouchitachen H, Hair A, Idrissi N (2017) Improved multi-objective weighted clustering algorithm in wireless sensor network. Egypt Inform J 18(1):45–54

    Article  Google Scholar 

  22. Azharuddin M, Jana PK (2016) Particle swarm optimization for maximizing lifetime of wireless sensor networks. Comput Electr Eng 51:26–42

    Article  Google Scholar 

  23. Karaboga D, Okdem S, Ozturk C (2012) Cluster based wireless sensor network routing using artificial bee colony algorithm. Wirel Netw 18:847–860

    Article  Google Scholar 

  24. Yadav R, Kumar V, Kumar R (2015) A discrete particle swarm optimization based clustering algorithm for wireless sensor networks. In: Emerging ICT for Bridging the Future-Proceedings of the 49th Annual Convention of the Computer Society of India CSI Volume 2, pp 137–144

    Google Scholar 

  25. Guo W, Zhang B, Chen G, Wang X, Xiong N (2013) A PSO-optimized minimum spanning tree-based topology control scheme for wireless sensor networks. Int J Distrib Sens Netw 2013:985410

    Article  Google Scholar 

  26. Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, vol 2, p 10

  27. Younis O, Fahmy S (2004) HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans Mob Comput 3:366–379

    Article  Google Scholar 

  28. Lakhlef H (2015) A multi-level clustering scheme based on cliques and clusters for wireless sensor networks. Comput Electr Eng 48:436–450

    Article  Google Scholar 

  29. Loscri V, Morabito G, Marano S (2005) A two-levels hierarchy for low-energy adaptive clustering hierarchy (TL-LEACH). In: IEEE Vehicular Technology Conference, p 1809

  30. Mann PS, Singh S (2017) Improved metaheuristic based energy-efficient clustering protocol for wireless sensor networks. Eng Appl Artif Intell 57:142–152

    Article  Google Scholar 

  31. Kuila P, Jana PK (2014) A novel differential evolution based clustering algorithm for wireless sensor networks. Appl Soft Comput 25:414–425

    Article  Google Scholar 

  32. Amgoth T, Jana PK (2013) BDCP: a backoff-based distributed clustering protocol for wireless sensor networks. In: 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp 1012–1016

  33. Wang J, Cao Y-T, Xie J-Y, Chen S-F (2011) Energy efficient backoff hierarchical clustering algorithms for multi-hop wireless sensor networks. J Comput Sci Technol 26:283–291

    Article  Google Scholar 

  34. Gao W, Liu S (2011) Improved artificial bee colony algorithm for global optimization. Inf Process Lett 111:871–882

    Article  MathSciNet  Google Scholar 

  35. Soro S, Heinzelman WB (2005) Prolonging the lifetime of wireless sensor networks via unequal clustering. In: Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium, p 8

  36. Amgoth T, Ghosh N, Jana PK (2014) Energy-aware multi-level routing algorithm for two-tier wireless sensor networks. In: International Conference on Distributed Computing and Internet Technology, pp 111–121

    Chapter  Google Scholar 

  37. Amgoth T, Jana PK (2015) Energy-aware routing algorithm for wireless sensor networks. Comput Electr Eng 41:357–367

    Article  Google Scholar 

  38. Yu M, Leung KK, Malvankar A (2007) A dynamic clustering and energy efficient routing technique for sensor networks. IEEE Trans Wirel Commun 6:3069–3079

    Article  Google Scholar 

  39. Yu J, Qi Y, Wang G, Gu X (2012) A cluster-based routing protocol for wireless sensor networks with nonuniform node distribution. AEU-Int J Electron Commun 66:54–61

    Article  Google Scholar 

  40. Lalwani P, Das S, Banka H, Kumar C (2018) CRHS: clustering and routing in wireless sensor networks using harmony search algorithm. Neural Comput Appl 30(2):639–659

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Engineering Department, Urmia Branch, Islamic Azad University, Urmia, Iran

    Mohammad Masdari & Saeid Barshande

  2. Department of Computer Engineering, Gazi University, Ankara, Turkey

    Suat Ozdemir

Authors
  1. Mohammad Masdari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saeid Barshande
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Suat Ozdemir
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Masdari.

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

Masdari, M., Barshande, S. & Ozdemir, S. CDABC: chaotic discrete artificial bee colony algorithm for multi-level clustering in large-scale WSNs. J Supercomput 75, 7174–7208 (2019). https://doi.org/10.1007/s11227-019-02933-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-019-02933-3

Keywords

Search

Navigation