Skip to main content
SpringerLink
Log in

Cluster based hybrid optimization and kronecker gradient factored approximate optimum path curvature network for energy efficiency routing in WSN

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

Sensors are placed in a specified area in Wireless Sensor Networks (WSNs) which sense the physical parameter of an environment. Upon sensing, information is analysed and transferred through a predetermined route to the base station. Certainly, energy consumption imbalances leading to premature node depletion and congestion due to data packet collisions are significant hotspot issues in WSNs. These issues arise, because detecting and communicating nodes often use more energy, which can result in shorter lifetimes for these nodes and hinder efficient data transmission in the network. Addressing these challenges is crucial for enhancing the performance and sustainability of WSNs. The clustering method stands out as a highly effective approach for preserving energy and extending the network’s life time. Hence, the Cluster based hybrid Artificial Tasmanian Devil Hummingbird Algorithm and Kronecker gradient Factored approximate Optimum path curvature network for Energy efficiency Routing in WSN (CAKFERW) are proposed to reduce the node's quick data transmission and energy consumption. Initially, the Cluster Head (CH) is elected using hybrid Artificial tasmanian devil humming bird algorithm to enhance the longevity and data throughput of the network. After the choice of CH, the optimal path is elected by kronecker gradient factored approximate optimum path curvature network. Secure data communication occurs along the chosen trust-optimal path. The suggested method is implemented by Network Simulator tool. The simulations outcomes show that the CAKFERW method attains 48% high alive nodes, 0.04 ms lower delay, 0.0012 J low energy consumption and 99.33% higher packet delivery ratio.

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

Similar content being viewed by others

Data availability

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

References  

  1. Sahu, S., Silakari, S (2022) Distributed Multilevel k-Coverage Energy-Efficient Fault-Tolerant Scheduling for Wireless Sensor Networks. Wirel. Pers. Commun. 1–30.

  2. Sahu, S., Silakari, S (2022) Energy Efficiency and Fault Tolerance in Wireless Sensor Networks: Analysis and Review. Soft Comput: Theories Appl. 389–402.

  3. Mohapatra H, Rath AK (2020) Survey on fault tolerance-based clustering evolution in WSN. IET Netw 9:145–155

    Google Scholar 

  4. Sharma R, Vashisht V, Singh U (2020) Metaheuristics-based energy efficient clustering in WSNs: challenges and research contributions. IET Wirel Sens Syst 10:253–264

    Google Scholar 

  5. Quoc DN, Liu N, Guo D (2021) A hybrid fault-tolerant routing based on Gaussian network for wireless sensor network. J Commun Netw 24:37–46

    Google Scholar 

  6. Amutha J, Sharma S, Sharma SK (2022) An energy efficient cluster based hybrid optimization algorithm with static sink and mobile sink node for Wireless Sensor Networks. Expert Syst Appl 203:117334

    Google Scholar 

  7. Sreedevi, P., Venkateswarlu, S (2022) Comparative analysis of energy efficient routing protocols with optimization in WSN. Int. J. Interact Des. Manuf. (IJIDeM),1–16.

  8. Maheshwari P, Sharma AK, Verma K (2021) Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization. Ad Hoc Netw 110:102317

    Google Scholar 

  9. Malisetti N, Pamula VK (2022) Energy efficient cluster based routing for wireless sensor networks using moth levy adopted artificial electric field algorithm and customized grey wolf optimization algorithm. Microprocess Microsyst 93:104593

    Google Scholar 

  10. Muhammed T, Mehmood R, Albeshri A, Alzahrani A (2020) HCDSR: A hierarchical clustered fault tolerant routing technique for IoT-based smart societies. In Smart Infrastructure and Appl. ( 609–628). Springer, Cham.

  11. Rawat P, Chauhan S (2021) Particle swarm optimization-based energy efficient clustering protocol in wireless sensor network. Neural Comput Appl 33:14147–14165

    Google Scholar 

  12. Mazumdar N, Nag A, Nandi S (2021) HDDS: Hierarchical Data Dissemination Strategy for energy optimization in dynamic wireless sensor network under harsh environments. Ad Hoc Netw 111:102348

    Google Scholar 

  13. Gupta, P., Tripathi, S., Singh, S.: Energy-efficient routing protocols for cluster-based heterogeneous wireless sensor network (HetWSN)—strategies and challenges: a review. Data Anal. Manag. 853–878 (2021).

  14. Senthil GA, Raaza A, Kumar N (2022) Internet of things energy efficient cluster-based routing using hybrid particle swarm optimization for wireless sensor network. Wirel Pers Commun 122:2603–2619

    Google Scholar 

  15. Prabha R, Senthil GA, Suganthi SU (2023) Cluster head based secure routing using optimized dual-discriminator conditional generative adversarial network in wireless ad-hoc networks. Peer-to-Peer Netw Appl 16:2747–2760

    Google Scholar 

  16. Devika G, Ramesh D, Karegowda AG (2020) Swarm intelligence–based energy‐efficient clustering algorithms for WSN: overview of algorithms, analysis, and applications. Swarm Intell. Optim.: Algorith. Appl. 207–261.

  17. SureshKumar K, Vimala P (2021) Energy efficient routing protocol using exponentially-ant lion whale optimization algorithm in wireless sensor networks. Comput Netw 197:108250

    Google Scholar 

  18. Ullah Z (2020) A survey on hybrid, energy efficient and distributed (HEED) based energy efficient clustering protocols for wireless sensor networks. Wirel Pers Commun 112:2685–2713

    Google Scholar 

  19. Fu X, Pace P, Aloi G, Li W, Fortino G (2021) Toward robust and energy-efficient clustering wireless sensor networks: A double-stage scale-free topology evolution model. Comput Netw 200:108521

    Google Scholar 

  20. Moussa N, Hamidi-Alaoui Z, El Belrhiti El Alaoui A (2021) IACO-ERP: An improved ACO-based energy-efficient routing protocol for fog-based WSNs. Int J Commun. Syst 34:e4743

    Google Scholar 

  21. Dehghani M, Hubalovsky S, Trojovsky P (2022) Tasmanian devil optimization: A new bio-inspired optimization algorithm for solving optimization algorithm. IEEE Access 10:19599–19620

    Google Scholar 

  22. Zhao W, Wang L, Mirjalili S (2022) Artificial Hummingbird Algorithm: A new bio-inspired optimizer with its engineering applications. Comput Methods Appl Mech Eng 388:114194

    MathSciNet  Google Scholar 

  23. Chu Y, Wei Z, Sun G, Zang H, Chen S, Zhou Y (2022) Optimal Home Energy Management Strategy: A reinforcement learning method with actor-critic using Kronecker-factored trust region. Electr Power Syst Res 212:108617

    Google Scholar 

  24. Chaurasia S, Kumar K, Kumar N (2023) MOCRAW: A meta-heuristic optimized cluster head selection based routing algorithm for WSNS. AdHoc Netw 141:103079

    Google Scholar 

  25. Mehra PS (2021) E-FUCA: Enhancement in fuzzy unequal clustering and routing for Sustainable Wireless Sensor Network. Comp Intell Syst 8:393–412

    Google Scholar 

  26. Moussa N, El Belrhiti El Alaoui A (2021) An energy-efficient cluster-based routing protocol using unequal clustering and improved ACO techniques for WSNS. Peer-to-Peer Netw Appl 14:1334–1347

    Google Scholar 

  27. Mansour RF, Alsuhibany SA, Abdel-Khalek S, Alharbi R, Vaiyapuri T, Obaid AJ, Gupta D (2022) Energy aware fault tolerant clustering with routing protocol for improved survivability in wireless sensor networks. Comput Netw 212:109049

    Google Scholar 

  28. Al-Otaibi S, Al-Rasheed A, Mansour RF, Yang E, Joshi GP, Cho W (2021) Hybridization of metaheuristic algorithm for Dynamic Cluster-based routing protocol in wireless sensor networksx. IEEE Access 9:83751–83761

    Google Scholar 

  29. Sahoo BM, Pandey HM, Amgoth T (2021) Gapso-H: A hybrid approach towards optimizing the cluster based routing in Wireless Sensor Network. Swarm Evol Comput 60:100772

    Google Scholar 

  30. Arivubrakan P, Kanagachidambaresan GR (2023) Multi-objective cluster head-based energy-aware routing protocol using hybrid woodpecker and flamingo search optimization algorithm for internet of things environment. Int J Inf Technol Decis Mak 1–20.

  31. Srinivasulu M, Shivamurthy G, Venkataramana B (2023) Quality of Service Aware Energy Efficient Multipath routing protocol for internet of things using hybrid optimization algorithm. Multimed Tools Appl.

  32. Vaiyapuri T, Parvathy VS, Manikandan V, Krishnaraj N, Gupta D, Shankar K (2021) A novel hybrid optimization for cluster-based routing protocol in information-centric wireless sensor networks for IOT based Mobile Edge Computing. Wirel Pers Commun 127:39–62

    Google Scholar 

  33. Guleria K, Verma AK, Goyal N, Sharma AK, Benslimane A, Singh A (2021) An enhanced energy proficient clustering (EEPC) algorithm for relay selection in heterogeneous WSNs. AdHoc Netw 116:102473

    Google Scholar 

  34. Wategaonkar DN, Nagaraj SV, Reshmi TR (2021) Multi-hop energy-efficient reliable cluster-based sectoring scheme using Markov chain model to improve QoS parameters in a WSN. Wirel Pers Commun 119:393–421

    Google Scholar 

  35. Vinitha A, Rukmini MSS (2022) Dhirajsunehra: Secure and energy aware multi-hop routing protocol in WSN using Taylor-based hybrid optimization algorithm. J. King Saud Univ. - Comput. Inf. Sci. 34:1857–1868

    Google Scholar 

  36. Seyyedabbasi A, Dogan G, Kiani F (2020) Heel: A new clustering method to improve wireless sensor network lifetime. IET Wirel Sensor Syst 10:130–136

    Google Scholar 

  37. Sarkar A, Senthil Murugan T (2019) Cluster head selection for energy efficient and delay-less routing in Wireless Sensor Network. Wirel Netw 25:303–320

    Google Scholar 

  38. Seyyedabbasi A, Kiani F (2020) Map-aco: An efficient protocol for multi-agent pathfinding in real-time WSN and decentralized IOT Systems. Microprocess Microsyst 79:103325

    Google Scholar 

  39. Wu M, Li Z, Chen J, Min Q, Lu T (2022) A dual cluster-head energy-efficient routing algorithm based on canopy optimization and K-means for WSN. Sensors 22:9731

    Google Scholar 

  40. Seyyedabbasi A, Kiani F, Allahviranloo T, Fernandez-Gamiz U, Noeiaghdam S (2023) Optimal data transmission and pathfinding for WSN and decentralized IOT systems using I-gwo and ex-GWO algorithms. Alexandria Eng J 63:339–357

    Google Scholar 

  41. Kathiroli P, Selvadurai K (2022) Energy efficient cluster head selection using improved Sparrow Search algorithm in wireless sensor networks. J King Saud Univ Comput Info Sci 34:8564–8575

    Google Scholar 

  42. Jaiswal K, Anand V (2022) FAGWO-H: A hybrid method towards fault-tolerant cluster-based routing in wireless sensor network for IoT applications. J Supercomput 78(8):11195–11227

    Google Scholar 

  43. Jagadeesh, S. and Muthulakshmi, I (2022) A novel oppositional artificial fish swarm based clustering with improved moth flame optimization based routing protocol for wireless sensor networks. Energy Syst 1–21

  44. Muthukkumar R, Garg L, Maharajan K, Jayalakshmi M, Jhanjhi N, Parthiban S, Saritha G (2022) A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks. Peer J Comput Sci 8:e1029

    Google Scholar 

  45. Hossan A, Akter S, Choudhury PK (2022) Distance and energy aware extended LEACH using secondary cluster head for wireless sensor networks. Telemat Info Rep 8:100029

    Google Scholar 

  46. Rami Reddy M, Ravi Chandra ML, Venkatramana P, Dilli R (2023) Energy-efficient cluster head selection in wireless sensor networks using an improved grey wolf optimization algorithm. Comput 12(2):35

    Google Scholar 

  47. Reddy DL, Puttamadappa CG, Suresh HNG (2021) Hybrid optimization algorithm for security aware cluster head selection process to aid hierarchical routing in wireless sensor network. IET Commun 15(12):1561–1575

    Google Scholar 

  48. Arya G, Bagwari A, Chauhan DS (2022) Performance analysis of deep learning-based routing protocol for an efficient data transmission in 5G WSN communication. IEEE Access 10:9340–9356

    Google Scholar 

  49. Dattatraya KN, Rao KR (2022) Hybrid based cluster head selection for maximizing network lifetime and energy efficiency in WSN. J King Saud Univ Comput Inf 34(3):716–726

    Google Scholar 

  50. Roberts MK, Ramasamy P (2022) Optimized hybrid routing protocol for energy-aware cluster head selection in wireless sensor networks. Digit Signal Process 130:103737

    Google Scholar 

  51. Khot PS, Naik U (2021) Particle-water wave optimization for secure routing in wireless sensor network using cluster head selection. Wirel Pers Commun 119:2405–2429

    Google Scholar 

  52. Ajmi N, Helali A, Lorenz P, Mghaieth R (2021) MWCSGA—multi weight chicken swarm based genetic algorithm for energy efficient clustered wireless sensor network. Sensors 21(3):791

    Google Scholar 

  53. Daniel J, Francis SFV, Velliangiri S (2021) Cluster head selection in wireless sensor network using tunicate swarm butterfly optimization algorithm. Wirel Netw 27:5245–5262

    Google Scholar 

Download references

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Karpagam Institute of Technology, Coimbatore, Tamil, Nadu-641021, India

    S. Syed Jamaesha & M. S. Gowtham

  2. Sri Krishna College of Engineering and Technology, Coimbatore, Tamil, Nadu-641008, India

    R. Sarath Kumar

Authors
  1. S. Syed Jamaesha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Sarath Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. S. Gowtham
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All the authors have contributed equally to the work.

Corresponding author

Correspondence to S. Syed Jamaesha.

Ethics declarations

Competing interests

The authors declare no competing interests.

Disclosure of potential conflict of interest

The authors declare that they have no potential conflict of interest.

Statement of animal and human Rights

I. Ethical Approval

All applicable institutional and/or national guidelines for the care and use of animals were followed.

ii. Informed Consent

For this type of analysis formal consent is not needed.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jamaesha, S.S., Kumar, R.S. & Gowtham, M.S. Cluster based hybrid optimization and kronecker gradient factored approximate optimum path curvature network for energy efficiency routing in WSN. Peer-to-Peer Netw. Appl. 17, 1588–1609 (2024). https://doi.org/10.1007/s12083-024-01675-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-024-01675-1

Keywords

Search

Navigation