SpringerLink
Log in

Elliptic curve encryption-based energy-efficient secured ACO routing protocol for wireless sensor networks

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

Abstract

Design of effective algorithm for reliable and energy optimized secure routing protocol (SRP) for wireless sensor networks (WSNs) is a demanding design issue now. To handle this problem, we propose a trust and encryption-based SRP based on trust modelling with intrusion detection, elliptic curve cryptography (ECC), clustering, fuzzy rules and ant colony optimization (ACO)-oriented SRP for WSN routing. In this paper, an extended convolutional neural networks with Schrodinger equation and particle swarm optimization is proposed for develo** and intrusion detection-based trust modelling. Moreover, a new node authentication scheme and an encryption-based secure routing protocol are also proposed in this work for increasing the security. This proposed secure protocol known as trust and ECC encryption-based ACO-SRP (TECC-ACO-SRP) performs authentication and trust analysis on the nodes using intrusion detection, and then, the data are communicated after data encryption using ECC encryption technique. This proposed system combines dominant set clustering with fuzzy rules to make clusters with similar type of nodes as members and then selects cluster heads (CHs) for every cluster. This SRP ensures improved security, reduced delay and energy usage with higher packet delivery ratio than other existing SRPs.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

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

Similar content being viewed by others

References

  1. Elhoseny M, Elminir H, Riad A, Yuan X (2016) A secure data routing schema for WSN using elliptic curve cryptography and homomorphic encryption. J King Saud Univ-Comput Inf Sci 28(3):262–275. https://doi.org/10.1016/j.jksuci.2015.11.001

    Article  Google Scholar 

  2. Mehta K, Pal R (2017) Biogeography based optimization protocol for energy efficient evolutionary algorithm:(BBO: EEEA), In 2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN), IEEE, pp.281–286. https://doi.org/10.1109/IC3TSN.2017.8284492

  3. Al Marshoud MS, Al-Bayatti AH, Kiraz MS (2024) Security, privacy, and decentralized trust management in VANETs: a review of current research and future directions. ACM Comput Surv. https://doi.org/10.1145/3656166

    Article  Google Scholar 

  4. Alqahtani M, Gumaei A, Mathkour H, Maher Ben Ismail M (2019) A genetic-based extreme gradient boosting model for detecting intrusions in wireless sensor networks. Sensors 19(20):1–20. https://doi.org/10.3390/s19204383

    Article  Google Scholar 

  5. Zhang X, Deng H, **ong Z, Liu Y, Rao Y, Lyu Y, Li Y, Hou D, Li Y (2024) Secure routing strategy based on attribute-based trust access control in social-aware networks. J Signal Process Syst. https://doi.org/10.1007/s11265-023-01908-1

    Article  Google Scholar 

  6. Kaur S, Singh M (2020) Hybrid intrusion detection and signature generation using deep recurrent neural networks. Neural Comput Appl 32(12):7859–7877. https://doi.org/10.1007/s00521-019-04187-9

    Article  Google Scholar 

  7. Gautam AK, Kumar R (2021) A comprehensive study on key management, authentication and trust management techniques in wireless sensor networks. SN Appl Sci 3(1):1–27. https://doi.org/10.1007/s42452-020-04089-9

    Article  MathSciNet  Google Scholar 

  8. Vinayakumar R, Alazab M, Soman KP, Poornachandran P, Al-Nemrat A, Venkatraman S (2019) Deep learning approach for intelligent intrusion detection system. IEEE Access 7:41525–41550

    Article  Google Scholar 

  9. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press

    Google Scholar 

  10. Soni S, Chouhan SS, Rathore SS (2023) TextConvoNet: a convolutional neural network based architecture for text classification. Appl Intell 53:14249–14268. https://doi.org/10.1007/s10489-022-04221-9

    Article  Google Scholar 

  11. Schrödinger E (1926) An undulatory theory of the mechanics of atoms and molecules. Phys Rev 28(6):1049–1070. https://doi.org/10.1103/PhysRev.28.1049

    Article  Google Scholar 

  12. Shalini S, Selvi M (2023) Intelligent IDS in wireless sensor networks using deep fuzzy convolutional neural network. Neural Comput Appl 35:15201–15220. https://doi.org/10.1007/s00521-023-08511-2

    Article  Google Scholar 

  13. Karam R (2020) Schrödinger”s original struggles with a complex wave function. Am J Phys 88(6):433–438. https://doi.org/10.1119/10.0000852

    Article  MathSciNet  Google Scholar 

  14. Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks. Vol. IV. pp. 1942–1948. https://ieeexplore.ieee.org/abstract/document/488968

  15. 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 110:1–15. https://doi.org/10.1016/j.adhoc.2020.102317

    Article  Google Scholar 

  16. Dinesh K, Santhosh Kumar SVN (2024) Energy-efficient trust-aware secured neuro-fuzzy clustering with sparrow search optimization in wireless sensor network. Int J Inf Secur 23(1):199–223. https://doi.org/10.1007/s10207-023-00737-4

    Article  Google Scholar 

  17. Yesodha K, Krishnamurthy M, Selvi M, Kannan A (2024) Intrusion detection system extended CNN and artificial bee colony optimization in wireless sensor networks. Peer-to-Peer Netw Appl. https://doi.org/10.1007/s12083-024-01650-w

    Article  Google Scholar 

  18. Verma V, Jha VK (2024) Secure and energy-aware data transmission for IoT-WSNs with the help of cluster-based secure optimal routing. Wirel Pers Commun. https://doi.org/10.1007/s11277-024-10983-x

    Article  Google Scholar 

  19. Batra PK, Kant K (2016) LEACH-MAC: a new cluster head selection algorithm for wireless sensor networks. Wireless Netw 22(1):49–60. https://doi.org/10.1007/s11276-015-0951-y

    Article  Google Scholar 

  20. Hande JY, Sadiwala R (2024) Data security-based routing in MANETs using key management mechanism. SN Comput Sci 5(1):155. https://doi.org/10.1007/s42979-023-02409-5

    Article  Google Scholar 

  21. Colorni.A, Dogrio.M, Maneizzo.V, (1991) Distributed Optimization by Ant Colonies, Proceedings of the First European Conference on Artificial Life, Elsevier Publishing, pp. 134–142.https://www-public.imtbs-tsp.eu/~gibson/Teaching/Teaching-ReadingMaterial/ColorniDorigoManiezzo91.pdf

  22. Russell S, Peter Norvig P (1994) Artificial intelligence: a modern approach. Prentice Hall, New Jersey

    Google Scholar 

  23. Rich E, Knight K, Nair SB (2009) Artificial intelligence, 3rd edn. McGraw Hill, New York

    Google Scholar 

  24. Shalini S, Selvi M (2023) Multi-objective PSO based feature selection for intrusion detection in IoT based wireless sensor networks. Optik 273:1–15. https://doi.org/10.1016/j.ijleo.2022.170419

    Article  Google Scholar 

  25. Dorgio M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst, Man Cybern—Part B 26(1):29–41

    Article  Google Scholar 

  26. Sim KM, Sun WH (2003) Ant colony optimization for routing and load-balancing: survey and new directions. IEEE Trans Syst Man Cybern—Part A: Syst Humans 33(5):560–572

    Article  MathSciNet  Google Scholar 

  27. Stallings W (2017) Cryptography and network security: principles and practices, 7th edn. Pearson Education, London

    Google Scholar 

  28. Tropea M, Spina MG, De Rango F, Gentile AF (2022) Security in wireless sensor networks: a cryptography performance analysis at MAC layer. Future Internet 14(5):1–20. https://doi.org/10.3390/fi14050145

    Article  Google Scholar 

  29. Wang Y, Li Z (2021) Inverse problem of nonlinear Schrodinger equation as learning of convolutional neural network, ar**v preprint ar**v:2107.08593, pp 1–25. https://arxiv.org/abs/2107.08593

  30. Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2016) Inception-v4, inception-ResNet and the impact of residual connections on learning. ar**v:1602.07261. https://ojs.aaai.org/index.php/aaai/article/view/11231

  31. Sharma B, Sharma L, Lal C, Roy S (2024) Explainable artificial intelligence for intrusion detection in IoT networks: a deep learning-based approach. Expert Syst Appl 238:121751. https://doi.org/10.1016/j.eswa.2023.121751

    Article  Google Scholar 

  32. Yasotha B, Sasikala T, Krishnamurthy M (2023) Wrapper based linear discriminant analysis (LDA) for intrusion detection in IIoT. Comput Syst Sci Eng 45(2):1625–1640

    Article  Google Scholar 

  33. Nagendra Babu G, Mehata KM, Krishnamurthy M (2019) A multi-level lightweight method to find malfunctioning nodes in WSN. J Adv Res Dyn Control Syst 11(01):818–823

    Google Scholar 

  34. Misbha DS (2023) Lightweight key distribution for secured and energy efficient communication in wireless sensor network: an optimization assisted model. High-Confid Comput 3(2):100126

    Article  Google Scholar 

  35. Lopez J, Dahab R (2000) An overview of elliptic curve cryptography, Technical report, Technical report IC-00-10, Institute of Computing – UNICAMP

  36. Nancy P, Muthurajkumar S, Ganapathy S, Santhosh Kumar SVN, Selvi M, Arputharaj K (2020) Intrusion detection using dynamic feature selection and fuzzy temporal decision tree classification for wireless sensor networks. IET Commun 14(5):888–895. https://doi.org/10.1049/iet-com.2019.0172

    Article  Google Scholar 

  37. Duan Y, Chen N, Chang L, Ni Y, Santhosh Kumar SVN, Zhang P (2022) CAPSO: chaos adaptive particle swarm optimization algorithm. IEEE Access 10:29393–29405

    Article  Google Scholar 

  38. Sun Q, Huang X, Kibalya G, Kumar N, Kumar SS, Zhang P, **e D (2021) Security enhanced sentence similarity computing model based on convolutional neural network. IEEE access 9:104183–104196

    Article  Google Scholar 

  39. Patil AR, Borkar GM (2023) Node authentication and encrypted data transmission in mobile ad hoc network using the swarm intelligence-based secure ad-hoc on-demand distance vector algorithm. IET Wirel Sens Syst 13(6):201–215. https://doi.org/10.1049/wss2.12068

    Article  Google Scholar 

  40. Luo Y (2021) Research on network security intrusion detection system based on machine learning. Int J Netw Secur 23(3):490–495. https://doi.org/10.6633/IJNS.202105

    Article  Google Scholar 

  41. Mahmoud MM, Lin X, Shen X (2013) Secure and reliable routing protocols for heterogeneous multihop wireless networks. IEEE Trans Parallel Distrib Syst 26(4):1140–1153. https://doi.org/10.1109/TPDS.2013.138

    Article  Google Scholar 

  42. Thakkar A, Lohiya R (2020) Role of swarm and evolutionary algorithms for intrusion detection system: a survey. Swarm Evol Comput 53:1–34. https://doi.org/10.1016/j.swevo.2019.100631

    Article  Google Scholar 

  43. Mezrag F, Bitam S, Mellouk A (2022) An efficient and lightweight identity-based scheme for secure communication in clustered wireless sensor networks. J Netw Comput Appl 200:1–13. https://doi.org/10.1016/j.jnca.2021.103282

    Article  Google Scholar 

  44. Fotohi R, Firoozi Bari S, Yusefi M (2020) Securing wireless sensor networks against denial-of-sleep attacks using ECC cryptography algorithm and interlock protocol. Int J Commun Syst 33(4):1–30. https://doi.org/10.1002/dac.4234

    Article  Google Scholar 

  45. Ren J, Guo J, Qian W, Yuan H, Hao X, **g**g H (2019) Building an effective intrusion detection system by using hybrid data optimization based on machine learning algorithms. Secur Commun Netw. https://doi.org/10.1155/2019/7130868

    Article  Google Scholar 

  46. Raman MG, Somu N, Kirthivasan K, Liscano R, Sriram VS (2017) An efficient intrusion detection system based on hypergraph-Genetic algorithm for parameter optimization and feature selection in support vector machine. Knowl-Based Syst 134:1–12. https://doi.org/10.1016/j.knosys.2017.07.005

    Article  Google Scholar 

  47. Chan L, Gomez Chavez K, RudolphH HA (2020) Hierarchical routing protocols for wireless sensor network: a compressive survey. Wireless Netw 26(5):3291–3314. https://doi.org/10.1007/s11276-020-02260-z

    Article  Google Scholar 

  48. Wang F, Wang Q, Nie F, Li Z, Yu W, Ren F (2020) A linear multivariate binary decision tree classifier based on K-means splitting. Pattern Recogn 107:1–13. https://doi.org/10.1016/j.patcog.2020.107521

    Article  Google Scholar 

  49. Khoshkangini R, Zaboli S, Conti M, (2014) Efficient routing protocol via ant colony optimization (ACO) and breadth first search (BFS),In Proceedings of the IEEE international Conference on Cyber, Physical and Social Computing, September, pp.374–380. https://doi.org/10.1109/iThings.2014.69

  50. 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. https://doi.org/10.1007/s11277-021-08088-w

    Article  Google Scholar 

  51. Selvi M, Thangaramya K, Ganapathy S, Kulothungan K, Khannah Nehemiah H, Kannan A (2019) An energy aware trust based secure routing algorithm for effective communication in wireless sensor networks. Wireless Pers Commun 105(4):1475–1490. https://doi.org/10.1007/s11277-019-06155-x

    Article  Google Scholar 

  52. Asaad MS, Croock MS (2021) Adaptive security approach for wireless sensor network using ECC algorithm. Indones J Electr Eng Comput Sci 22(1):361–368. https://doi.org/10.11591/ijeecs.v22.i1

    Article  Google Scholar 

  53. Chang L, Li F, Niu X, Zhu J (2022) On an improved clustering algorithm based on node density for WSN routing protocol. Cluster Comput. https://doi.org/10.1007/s10586-022-03544-z

    Article  Google Scholar 

  54. Hasan NA, Farhan AK (2019) Security improve in ZigBee protocol based on ECC public algorithm in WSN. Eng Technol J 37(3):67–73. https://doi.org/10.30684/etj.37.3B.1

    Article  Google Scholar 

  55. Moraboena S, Ketepalli G, Ragam P (2020) A deep learning approach to network intrusion detection using deep autoencoder. Revis Intell Artif 34(4):457–463. https://doi.org/10.18280/ria.340410

    Article  Google Scholar 

  56. Salman T, Bhamare D, Erbad A, Jain R, Samaka M, (2017) June. Machine learning for anomaly detection and categorization in multi-cloud environments, In 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) pp. 97–103. IEEE. https://doi.org/10.1109/CSCloud.2017.15

  57. Selvi M, Santhosh Kumar SVN, Ganapathy S, Ayyanar A, Khanna Nehemiah H, Kannan A (2021) An energy efficient clustered gravitational and fuzzy based routing algorithm in WSNs. Wireless Pers Commun 116(1):61–90. https://doi.org/10.1007/s11277-020-07705-4

    Article  Google Scholar 

  58. Shalini S, Selvi M, Santhosh Kumar SVN, Kannan A (2021) Energy efficient clustering routing protocol and ACO algorithm in WSN, In International Conference on Advances in Computing and Data Sciences, Springer, pp.68–80. https://doi.org/10.1007/978-3-030-81462-5_7

  59. Rakesh R, Santhosh Kumar SVN, Yogesh P, Kannan A (2019) Detection of DoS attacks in cloud networks using intelligent rule-based classification system. Cluster Comput 22(Suppl 1):423–434. https://doi.org/10.1007/s10586-018-2181-4

    Article  Google Scholar 

  60. Selvi M, Nandhini C, Thangaramya K, Kulothungan K, Kannan A (2017) HBO based clustering and energy optimized routing algorithm for WSN, Eighth International Conference on Advanced Computing (ICoAC), Chennai, pp. 89–92. https://ieeexplore.ieee.org/abstract/document/7951751

  61. Munuswamy S, Saravanakumar MJ, Sannasi G, Harichandran KN, Arputharaj K (2018) Virtual force-based intelligent clustering for energy-efficient routing in mobile wireless sensor networks. Turk J Electr Eng Comput Sci 26(3):1444–1452

    Google Scholar 

  62. Selvi M, Logambigai R, Ganapathy S, Sai Ramesh L, Khanna Nehemiah H, Kannan A, (2016) fuzzy temporal approach for energy efficient routing in WSN, Proceedings of International Conference on Informatics and Analytics, ACM, pp. 117:1–117:5. https://doi.org/10.1145/2980258.2982109

  63. Logambigai R, Kannan A (2018) Energy conservation routing algorithm for wireless sensor networks using hybrid optimisation approach. Int J Commun Netw Distr Syst 20(3):352–371. https://doi.org/10.1504/IJCNDS.2018.091059

    Article  Google Scholar 

  64. Biswas K, Muthukkumarasamy V, Chowdhury MJM, Wu XW, Singh K (2023) A multipath routing protocol for secure energy efficient communication in wireless sensor networks. Comput Netw 232:109842. https://doi.org/10.1016/j.comnet.2023.109842

    Article  Google Scholar 

  65. Thangaramya K, Logambigai R, Kulothungan K, Ganapathy S, Kannan A (2020) QoS aware trust based routing algorithm for wireless sensor networks. Wirel Pers Commun 110(4):1637–1658. https://doi.org/10.1007/s11277-019-06788-y

    Article  Google Scholar 

  66. Pedditi RB, Debasis K (2023) Energy efficient routing protocol for an IoT-based WSN system to detect forest fires. Appl Sci 13(5):3026. https://doi.org/10.3390/app13053026

    Article  Google Scholar 

  67. Mohajerani A, Gharavian D (2016) An ant colony optimization-based routing algorithm for extending network lifetime in wireless sensor networks. Wirel Netw 22(8):2637–2647. https://doi.org/10.1007/s11276-015-1061-6

    Article  Google Scholar 

  68. Rathee M, Kumar S, Gandomi AH, Dilip K, Balusamy B, Patan R (2019) Ant colony optimization-based quality of service aware energy balancing secure routing algorithm for wireless sensor networks. IEEE Trans Eng Manag 68(1):170–182

    Article  Google Scholar 

  69. 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 60:1–42. https://doi.org/10.1016/j.swevo.2020.100772

    Article  Google Scholar 

  70. Zheng A, Zhang Z, Liu W, Liu J, **ao Y, Li C (2023) Dual cluster head optimization of wireless sensor networks based on multi-objective particle swarm optimization. Sensors 23(1):1–19. https://doi.org/10.3390/s23010231

    Article  Google Scholar 

  71. Younis O, Fahmy S (2004) HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans Mobile Comput 3(4):366–379

    Article  Google Scholar 

  72. Wei D, ** Y, Vural S, Moessner K, Tafazolli R (2011) An energy-efficient clustering solution for wireless sensor networks. IEEE Trans Wirel Commun 10(11):3973–3983

    Article  Google Scholar 

  73. Wu W, **ong N, Wu C (2017) Improved clustering algorithm based on energy consumption in wireless sensor networks. IET Networks 6(3):47–53. https://doi.org/10.1049/iet-net.2016.0115

    Article  Google Scholar 

  74. Thangaramya K, Kulothungan K, Logambigai R, Selvi M, Ganapathy S, Kannan A (2019) Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT. Comput Netw 151:211–223. https://doi.org/10.1016/j.comnet.2019.01.024

    Article  Google Scholar 

  75. Santhosh Kumar SVN, Selvi M, Kannan A (2023) A comprehensive survey on machine learning-based intrusion detection systems for secure communication in internet of things. Comput Intell Neurosci 2023:8981988

    Article  Google Scholar 

  76. Heinzelman W, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocols for wireless microsensor networks, Proceedings in Hawaaian international Conference on Systems Science, pp. 1–10. https://ieeexplore.ieee.org/abstract/document/926982

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

    Article  Google Scholar 

  78. Haider W, Hu J, Slay J, Turnbull BP, **e Y (2017) Generating realistic intrusion detection system dataset based on fuzzy qualitative modelling. J Netw Comput Appl 87:185–192

    Article  Google Scholar 

  79. Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) A detailed analysis of the KDD CUP 99 data set, Proceedings of the IEEE Symposium on Computational Intelligence in Security and Defense Applications, pp 1–6. https://ieeexplore.ieee.org/abstract/document/5356528

  80. Mishra P, Varadharajan V, Tupakula U, Pilli ES (2018) A detailed investigation and analysis of using machine learning techniques for Intrusion Detection. IEEE Commun Surv Tutor 21(1):686–728. https://doi.org/10.1109/COMST.2018.2847722

    Article  Google Scholar 

  81. Elmasry W, Akbulut A, Zaim AH (2020) Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic. Comput Netw 168:1–21. https://doi.org/10.1016/j.comnet.2019.107042

    Article  Google Scholar 

  82. Hibraj F, Vascon S, Stadelmann T, Pelillo M (2018) Speaker clustering using dominant sets. In 2018 24th International Conference on Pattern Recognition (ICPR), pp. 3549–3554. IEEE. https://ieeexplore.ieee.org/abstract/document/8546067

  83. Pavan M, Pelillo M (2006) Dominant sets and pairwise clustering. IEEE Trans Pattern Analys Machine Intell 29(1):167–172

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, KCG College of Technology, Chennai, Tamil Nadu, India

    K. Yesodha & M. Krishnamurthy

  2. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    K. Thangaramya & A. Kannan

Authors
  1. K. Yesodha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Krishnamurthy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. Thangaramya
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. Kannan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

K. Yesodha designed clustering algorithm and carried out the full system implementation, produced results and tested the system. She also prepared the manuscript. Dr. M. Krishnamurthy carried out literature survey, developed the ACO-based routing algorithm and also worked with clustering. Dr. K. Thangaramya developed the authentication model and trust modelling, and Dr. A. Kannan developed the ECC encryption, designed the overall system architecture and the integration of all the components.

Corresponding author

Correspondence to K. Yesodha.

Ethics declarations

Competing interests

The authors declare no competing interests.

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

Yesodha, K., Krishnamurthy, M., Thangaramya, K. et al. Elliptic curve encryption-based energy-efficient secured ACO routing protocol for wireless sensor networks. J Supercomput (2024). https://doi.org/10.1007/s11227-024-06235-1

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11227-024-06235-1

Keywords

Search

Navigation