TARU PUBLICATIONS
Journal of Discrete Mathematical Sciences and Cryptography cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0065·ISSN (Print): 0972-0529

Monthly Journal: Publishes theoretical and applied research in all areas of Discrete Mathematical Sciences, Cryptography, Combinatorics, Elliptic Curves and Information Security.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
submissions@tarupublications.com
Open Access Research Article

Authentication schemes using combinatorial key distribution

, * , , , ,

* Corresponding author · click or hover a name for details

pp. 4333–4345Vol. 29Issue 10-BOctober 2026DOI: 10.47974/JDMSC-3055 Crossmark XML
Received:
01 Apr 2026
Published Online:
31 Oct 2026
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-3055
Pages:
4333–4345

Abstract

As the Internet of Things (IoT) and wireless sensor networks (WSNs increase, the issue of security and efficient authentication of these networks is one of the burning questions. The classical authentication systems are either more prone to the use of centralized systems or computationally-intensive cryptographic systems which is not suitable in large scale and resource-constrained systems. The current key distribution schemes also have the problems of poor scalability, probabilistic connectivity along with sensitive to attacks such as node capture and man in the middle attacks. In this paper, a novel authentication framework is suggested as a framework that is based on combinatorial key distribution to provide deterministic, scalable and lightweight security. The aim is to come up with some practical authentication mechanism that can partially guarantee the credible exchanges of keys and reduce the cost of computation and storage. The algorithm employs combinatorial designs like Balanced Incomplete Block Designs (BIBDs) to compute structured key pools, and deterministic key rings to nodes. A key based authentication protocol that uses a shared key and a nonce based verification that uses mutual authentication is developed using a lightweight protocol and a secure session key is generated. The performance of the scheme measured on the basis of measures like the authentication success rate, cost of computation and storage overhead and connectivity demonstrate that the proposed scheme has an authentication success rate of more than 95 percent and low (1218 ms) computation time and sensible (812 KB) storage overhead.

Keywords

Subject Classifications

94A6205D40

References

[1] Z. Ali, S. Mahmood, K. Mansoor, A. Daud, R. Alharbey, and A. Bukhari, “A lightweight and secure authentication scheme for remote monitoring of patients in IoMT,” IEEE Access, vol. 12, pp. 73004–73020 (2024).

[2] Q. Gao, W. Ma, and W. Luo, “Key predistribution schemes for wireless sensor networks based on combinations of orthogonal arrays,” Ad Hoc Networks, vol. 73, pp. 40–50 (2018).

[3] C. Goswami, A. Adhikari, S. K. Sahoo, and P. Sarkar, “Authenticated key agreement for IoT network using HECC and CRT four co-primes,” Peer-to-Peer Networking and Applications, vol. 17, no. 4, pp. 2397–2414 (2024).

[4] B. B. Gupta, A. Gaurav, K. T. Chui, and C.-H. Hsu, “Identity-based authentication technique for IoT devices,” in Proc. IEEE International Conference on Consumer Electronics (ICCE), pp. 1–4 (2022).

[5] S. U. Jan, S. Ali, I. A. Abbasi, M. A. Mosleh, A. Alsanad, and H. Khattak, “Secure patient authentication framework in the healthcare system using wireless medical sensor networks,” Journal of Healthcare Engineering, vol. 2021, Art no. 9954089 (2021).

[6] J. Kim, J. Moon, J. Jung, and D. Won, “Security analysis and improvements of session key establishment for clustered sensor networks,” Journal of Sensors, vol. 2016, Art no. 4393721 (2016).

[7] K. Kim, J. Ryu, H. Lee, Y. Lee, and D. Won, “Distributed and federated authentication schemes based on updatable smart contracts,” Electronics, vol. 12, no. 1217 (2023).

[8] H. J. Lee, S. Kook, K. Kim, J. Ryu, Y. Lee, and D. Won, “LAMT: Lightweight and anonymous authentication scheme for medical Internet of Things services,” Sensors, vol. 25, no. 821 (2025).

[9] P. Khobragade, B. Thakran, P. K. Dhankar, P. Dasarwar, C. Vaidya, and K. Dhote, “Improving Intrusion Detection Systems Using Machine Learning Algorithms for Real-Time Network Security Monitoring,” 2025 1st International Conference on Data Science and Intelligent Network Computing (ICDSINC), Raipur, India, pp. 160-165 (2025), doi: 10.1109/ICDSINC66221.2025.11448255.

[10] S. Nambiar, M. Tatiya, K. S. Diwate, D. Sharma, S. N. Ajani, and Y. Nagargoje, “Privacy-preserving machine learning techniques ensuring data confidentiality and model accuracy in federated learning environments,” Journal of Information and Optimization Sciences, vol. 47, no. 5-B, pp. 1887–1895 (2026), doi: 10.47974/JIOS-2281.

[11] P. Khobragade, G. Gandal, A. Somatkar, H. M. Panchabudhe, P. Ashok, and B. Madaminov, “Heat transfer modeling using fractional differential equations,” Journal of Interdisciplinary Mathematics, vol. 29, no. 3, pp. 749–756 (2026), doi: 10.47974/JIM-2511.

[12] H. A. Al-Alshaikh, R. C. Poonia, A. K. J. Saudagar, and K. N. Tripathi, “A hybrid deep learning and quantum computing approach for optimized encryption algorithms in secure communications,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 7, pp. 2101–2110 (2024), doi: 10.47974/JDMSC-2083.

[13] M. Wazid, A. K. Das, V. Odelu, N. Kumar, and W. Susilo, “Secure remote user authenticated key establishment protocol for smart home environment,” IEEE Transactions on Dependable and Secure Computing, vol. 17, no. 2, pp. 391–406 (2020).

[14] S. Gupta, S. Sakshi, S. Kolay, M. Chulawala, P. Karekar, A. Shrivastava, and S. Sharma, “Urbanshield: Integrated Vehicle Security and Road Safety System For Metropolitan City,” International Journal on Advanced Computer Theory and Engineering, vol. 14, no. 1, pp. 107–118 (2025), doi: 10.65521/ijacte.v14i1.339.

Views: 6Downloads: 4Citations: 0