Skip to main content

Advertisement

SpringerLink
Log in

Clustering based on whale optimization algorithm for IoT over wireless nodes

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

IoT or Internet of Things can improve the possibility of interaction between various smart components in real time. In the infrastructure of IoT, wireless sensors can be used in order to reduce communication costs. Despite having positive effects, using wireless nodes add some challenges to the system. Limited resources, such as energy, CPU power and memory, are the main concerns in this technology. Energy consumption is the most challenging one. Designing an optimized routing pattern through heuristic algorithms is a common way to tackle this problem. Therefore, in the proposed algorithm, a WOA-based method has been proposed to expand the life span of the system. Also, a novel fitness function is defined for reducing the energy consumption of the network, load balancing and node coverage. Clustering is done unequally; it means that cluster heads (CHs) nearer to the base station (BS) have more energy for data relay. In this paper, for reducing the number of messages, a clustering stage is added at the beginning of each metaround. The number of rounds in a metaround is variable. The status of each node is analyzed by BS before each round. Low energy level causes a new metaround. Moreover, the CH–BS interaction is implemented through multi-hop pattern. Results suggest that there is an enhancement instability, energy-saving, throughput and lifespan.

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

Similar content being viewed by others

References

  • Abdul-Qawy ASH, Srinivasulu T (2018) SEES: a scalable and energy-efficient scheme for green IoT-based heterogeneous wireless nodes. J Ambient Intell Humaniz Comput 10:1571–1596

  • Afsar MM, Tayarani-N MH (2014) Clustering in sensor networks: a literature survey. J Netw Comput Appl 46:198–226

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Bozorgi SM, Amiri MG, Rostami AS, Mohanna F (2016) A novel dynamic multi-hop clustering protocol based on renewable energy for energy harvesting wireless sensor networks. In: Conference proceedings of 2015 2nd international conference on knowledge-based engineering and innovation, KBEI 2015

  • Bozorgi SM, Shokouhi Rostami A, Hosseinabadi AAR, Balas VE (2017) A new clustering protocol for energy harvesting-wireless sensor networks. Comput Electr Eng 64:233–247

    Article  Google Scholar 

  • Brest J, Zumer V, Maucec MS (2006) Self-adaptive differential evolution algorithm in constrained real-parameter optimization. IEEE Int Conf Evol Comput 2006:215–222

    Article  Google Scholar 

  • Chaurasiya SK, Pal T, Bit SD (2011) An enhanced energy-efficient protocol with static clustering for WSN. In: International conference on information networking 2011, ICOIN 2011, pp 58–63

  • Clerc M, Kennedy J (2002) The particle swarm—explosion, stability, and convergence in a\nmultidimensional complex space. IEEE Trans Evol Comput 6(1):58–73

    Article  Google Scholar 

  • Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186:311–338

    Article  Google Scholar 

  • Eberhart R, Shi Y (2004) Guest editorial special issue on particle swarm optimization. IEEE Trans Evol Comput 8(3):201–203

    Article  Google Scholar 

  • Gupta GP, Jha S (2018) Integrated clustering and routing protocol for wireless sensor networks using Cuckoo and Harmony Search based metaheuristic techniques. Eng Appl Artif Intell 68:101–109

    Article  Google Scholar 

  • Gupta V, Pandey R (2016) An improved energy aware distributed unequal clustering protocol for heterogeneous wireless sensor networks. Eng Sci Technol Int J 19(2):1050–1058

    Google Scholar 

  • Han T, Bozorgi SM, Orang AV, Hosseinabadi AR, Sangaiah AK, Chen MY (2019) A hybrid unequal clustering based on density with energy conservation in wireless nodes. Sustainability 11:1–26

    Google Scholar 

  • Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670

    Article  Google Scholar 

  • Hosseinabadi AR, Slowik A, Sadeghilalimi M, Farokhzad M, Babazadeh M, Sangaiah AK (2019) An ameliorative hybrid meta-heuristic algorithm for solving the capacitated vehicle routing problem. IEEE Access 7:175454–175465

    Article  Google Scholar 

  • Khalil EA, Attea BA (2011) Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks. Swarm Evol Comput 1(4):195–203

    Article  Google Scholar 

  • Khalil EA, Attea BA (2013) Stable-aware evolutionary routing protocol for wireless sensor networks. Wirel Pers Commun 69:1799–1817

    Article  Google Scholar 

  • Kuila P, Jana PK (2014a) Energy efficient clustering and routing algorithms for wireless sensor networks: particle swarm optimization approach. Eng Appl Artif Intell 33:127–140

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Kuila P, Gupta SK, Jana PK (2013) A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm Evol Comput 12:48–56

    Article  Google Scholar 

  • Kumar V, Kumar S (2016) Energy balanced position-based routing for lifetime maximization of wireless sensor networks. Ad Hoc Netw 52:117–129

    Article  Google Scholar 

  • Kumar A, Kumar V, Narottam C (2011) Energy efficient clustering and cluster head rotation scheme for wireless sensor networks. Int J Adv Comput Sci Appl 3(5):129–136

    Google Scholar 

  • Ma H, Simon D (2011) Blended biogeography-based optimization for constrained optimization. Eng Appl Artif Intell 24(3):517–525

    Article  Google Scholar 

  • Machado K, Rosário D, Cerqueira E, Loureiro A, Neto A, de Souza J (2013) A routing protocol based on energy and link quality for internet of things applications. Sensors 13(2):1942–1964

    Article  Google Scholar 

  • Malathi L, Gnanamurthy RK, Chandrasekaran K (2015) Energy efficient data collection through hybrid unequal clustering for wireless sensor networks. Comput Electr Eng 48:358–370

    Article  Google Scholar 

  • Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  • Rostami AS, Badkoobe M, Mohanna F, Hosseinabadi AR, Kardgar M, Balas VE (2016) Imperialist competition based clustering algorithm to improve the lifetime of wireless sensor network. In: 7th International workshop in soft computing applications (SOFA 2016), Springer, vol. 633. pp 189–202

  • Rostami AS, Badkoobe M, Mohanna F, Keshavarz H, Hosseinabadi AR, Kumar Sangaiah A (2018) Survey on clustering in heterogeneous and homogeneous wireless sensor networks. J Supercomput 74:277–323

    Article  Google Scholar 

  • Sabor N, Abo-Zahhad M, Sasaki S, Ahmed SM (2016) An unequal multi-hop balanced immune clustering protocol for wireless sensor networks. Appl Soft Comput J 43:372–389

    Article  Google Scholar 

  • Saemi B, Hosseinabadi AR, Kardgar M, Balas VE (2016) Nature inspired partitioning clustering algorithms: a review and analysis. In: 7th International workshop in soft computing applications (SOFA 2016), vol. 634. Springer, pp 96–116

  • Sangaiah AK, Sadeghilalimi M, Hosseinabadi AR, Zhang W (2019) Energy consumption in point-coverage wireless sensor networks via bat algorithm. IEEE Access 7:180258–180269

    Article  Google Scholar 

  • Shah SB, Chen Z, Yin F, Khan IU, Ahmad N (2018) Energy and interoperable aware routing for throughput optimization in clustered IoT-wireless sensor networks. Future Gener Comput Syst 81:372–381

    Article  Google Scholar 

  • 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 

  • Shokouhifar M, Jalali A (2015) A new evolutionary based application specific routing protocol for clustered wireless sensor networks. AEU Int J Electron Commun 69(1):432–441

    Article  Google Scholar 

  • Yu J, Qi Y, Wang G, Guo Q, Gu X (2011) An energy-aware distributed unequal clustering protocol for wireless sensor networks. Int J Distrib Sens Netw 7(1):2021–2045

  • Younis O, Member S, Fahmy S (2004) HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor. Networks 3(4):366–379

    Google Scholar 

  • Zanjireh MM, Larijani H (2015) A survey on centralised and distributed clustering routing algorithms for WSNs. 2015 IEEE 81st Vehicular Technology Conference (VTC Spring), pp 1–6

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Tehran North Branch, Islamic Azad University, Tehran, Iran

    Seyed Mostafa Bozorgi

  2. Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

    Mahdi Rohani Hajiabadi

  3. Department of Computer Science, University of Regina, Regina, Canada

    Ali Asghar Rahmani Hosseinabadi

  4. School of Computing Science and Engineering, Vellore Institute of Technology (VIT), Vellore, 632014, India

    Arun Kumar Sangaiah

Authors
  1. Seyed Mostafa Bozorgi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mahdi Rohani Hajiabadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Asghar Rahmani Hosseinabadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Arun Kumar Sangaiah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arun Kumar Sangaiah.

Ethics declarations

Conflict of interest

The author declares that he has no conflicts of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Research involving human participants and/or animals

This article does not contain any studies with human participants performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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

Bozorgi, S.M., Hajiabadi, M.R., Hosseinabadi, A.A.R. et al. Clustering based on whale optimization algorithm for IoT over wireless nodes. Soft Comput 25, 5663–5682 (2021). https://doi.org/10.1007/s00500-020-05563-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-05563-7

Keywords

Search

Navigation