Secure Blockchain-based Routing with Narwhal Optimization for WSNs

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Author(s)

Ranjeet Yadav 1,* N. Manimegalai 2 Mercy Beulah E. 3 Mohammed Al-Farouni 4

1. Electronics and Communication Engineering, Government Polytechnic Daman, UT Administration of Dadra and Nagar Haveli and Daman and Diu 396210, India

2. Department of Artificial Intelligence and Data Science, Velammal Engineering College, Chennai – 66, Tamil Nadu, India

3. Department of Computer Science and Engineering, Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College, Tamil Nadu, India

4. Department of computers Techniques engineering, College of technical engineering, The Islamic University, Najaf, Iraq

* Corresponding author.

DOI: https://doi.org/10.5815/ijcnis.2026.01.03

Received: 27 Aug. 2025 / Revised: 25 Sep. 2025 / Accepted: 20 Oct. 2025 / Published: 8 Feb. 2026

Index Terms

WSN, Routing Attacks, Blokchain Integration, Routing Protocol, Attack Detetction, Optimization, Packet Loss

Abstract

Wireless Sensor Networks (WSNs) play a crucial role in various domains, such as environmental monitoring, health, and military applications. These applications necessitate the establishment of secure and efficient communication. This network encounters a major issue since routing attacks along with data tampering are highly prevalent in such networks due to their decentralized architecture and limited resources for computation that make the networks susceptible to a wide range of security threats. The existing techniques-WSN-Block, CEMT, TSRP, ORASWSN, POA-DL, AI-WSN, and EOSR are extensively used for routing but experience inefficiencies in optimizing paths that increases energy consumption drastically and also allows packet loss. In addition, the existing blockchain models for WSN security are not scalable; have high overheads of computation. To address these limitations, we propose the Secure Blockchain-Based Routing with Narwhal Optimization for WSNs (OpNa-SGCDN). Our approach employs Optimized Narwhal-Based Metaheuristic for guaranteed shortest-path communication and minimum energy consumption in optimum routing. Moreover, we provide Scalable Permissionless Blockchain Consensus Model (SP-BlockCM) features enhanced to yield a decentralized solution that is tamper-proof but with improved scalability. The attack detection function is designed by making use of a Stacked Bi-Tier Convolutional Deep Network (SBT-CDN), which is optimized by the Snow Geese Evolutionary Algorithm (SGEA). Experimental results demonstrate that our method improves energy efficiency with 94.7% as well as achieves higher detection accuracy for 96.3% and packet loss for 95.5%, both security and performance, and thus it is better than the available methods. The framework given here is thus obviously comprehensive as well as scalable for secure, energy-efficient WSN communication.

Cite This Paper

Ranjeet Yadav, N. Manimegalai, Mercy Beulah E., Mohammed Al-Farouni, "Secure Blockchain-based Routing with Narwhal Optimization for WSNs", International Journal of Computer Network and Information Security(IJCNIS), Vol.18, No.1, pp.33-50, 2026. DOI:10.5815/ijcnis.2026.01.03

Reference

[1]D. Paganraj, A. Tharun, and C. Mala, “Dair-mlt: Detection and avoidance of IoT routing attacks using machine learning techniques,” Int. J. Inf. Technol., vol. 16, no. 5, pp. 3255–3263, 2024. DOI: 10.1007/s41870-024-01794-1
[2]M. Ezhilarasi, L. Gnanaprasanambikai, A. Kousalya, and M. Shanmugapriya, “A novel implementation of routing attack detection scheme by using fuzzy and feed-forward neural networks,” Soft Comput., vol. 27, no. 7, pp. 4157–4168, 2023. DOI: 10.1007/s00500-022-06915-1
[3]P. Aruchamy, S. Gnanaselvi, D. Sowndarya, and P. Naveenkumar, “An artificial intelligence approach for energy‐aware intrusion detection and secure routing in internet of things‐enabled wireless sensor networks,” Concurr. Comput., vol. 35, no. 23, p. e7818, 2023. DOI: 10.1002/cpe.7818
[4]A. R. A. Moundounga, H. Satori, Y. Boutazart, and E. Abderrahim, “Malicious attack detection based on continuous Hidden Markov Models in Wireless sensor networks,” Microprocess. Microsyst., vol. 101, p. 104888, 2023. DOI: 10.1016/j.micpro.2023.104888
[5]S. Rabhi, T. Abbes, and F. Zarai, “IoT routing attacks detection using machine learning algorithms,” Wirel. Pers. Commun., vol. 128, no. 3, pp. 1839–1857, 2023. DOI: 10.1007/s11277-022-10022-7
[6]D. Bhanu and R. Santhosh, “Fuzzy enhanced location aware secure multicast routing protocol for balancing energy and security in wireless sensor network,” Wirel. Netw., vol. •••, pp. 1–20, 2023.
[7]S. Bagga, D. K. Sharma, K. K. Singh, and A. Singh, “Clustering based routing protocol for wireless sensor networks using the concept of zonal division of network field,” J. Signal Process. Syst. Signal Image Video Technol., vol. 95, no. 2, pp. 115–127, 2023. DOI: 10.1007/s11265-022-01743-w
[8]H. Satori, “Machine learning attack detection based-on stochastic classifier methods for enhancing of routing security in wireless sensor networks,” Ad Hoc Netw., vol. •••, p. 103581, 2024.
[9]R. Batool, N. Bibi, S. Alhazmi, and N. Muhammad, “Secure Cooperative Routing in Wireless Sensor Networks,” Appl. Sci. (Basel), vol. 14, no. 12, p. 5220, 2024. DOI: 10.3390/app14125220
[10]S. S. Vellela and R. Balamanigandan, “An efficient attack detection and prevention approach for secure WSN mobile cloud environment,” Soft Comput., vol. 28, no. 19, pp. 1–15, 2024. DOI: 10.1007/s00500-024-09891-w
[11]I. A. Abd El-Moghith and S. M. Darwish, “Towards designing a trusted routing scheme in wireless sensor networks: A new deep blockchain approach,” IEEE Access, vol. 9,103822–103834, 2021. DOI: 10.1109/ACCESS.2021.3098933
[12]S. Awan, N. Javaid, S. Ullah, A. U. Khan, A. M. Qamar, and J. G. Choi, “Blockchain based secure routing and trust management in wireless sensor networks,” Sensors (Basel), vol. 22, no. 2, p. 411, Jan. 6 2022. DOI: 10.3390/s22020411
[13]L. K. Ramasamy, “F.K. KP, A.L. Imoize, J.O. Ogbebor, S. Kadry, and S. Rho, “Blockchain-based wireless sensor networks for malicious node detection: A survey,”,” IEEE Access, vol. 9,128765–128785, 2021. DOI: 10.1109/ACCESS.2021.3111923
[14]J. Shahid, Z. Muhammad, Z. Iqbal, A. S. Almadhor, and A. R. Javed, “Cellular automata trust-based energy drainage attack detection and prevention in wireless sensor networks,” Comput. Commun., vol. 191, pp. 360–367, 2022. DOI: 10.1016/j.comcom.2022.05.011
[15]K. Yesodha, M. Krishnamurthy, K. Thangaramya, and A. Kannan, “Elliptic curve encryption-based energy-efficient secured ACO routing protocol for wireless sensor networks,” J. Supercomput., vol. 80, no. 13, pp. 1–34, 2024. DOI: 10.1007/s11227-024-06235-1
[16]M. A. Almaiah, “A new scheme for detecting malicious attacks in wireless sensor networks based on blockchain technology,” in Artificial intelligence and blockchain for future cybersecurity applications. Cham: Springer International Publishing, 2021, pp. 217–234. DOI: 10.1007/978-3-030-74575-2_12
[17]A. B. Feroz Khan and G. Anandharaj, “A cognitive energy efficient and trusted routing model for the security of wireless sensor networks: CEMT,” Wirel. Pers. Commun., vol. 119, no. 4, pp. 3149–3159, 2021. DOI: 10.1007/s11277-021-08391-6
[18]H. Hu, Y. Han, H. Wang, M. Yao, and C. Wang, “Trust‐aware secure routing protocol for wireless sensor networks,” ETRI J., vol. 43, no. 4, pp. 674–683, 2021. DOI: 10.4218/etrij.2020-0147
[19]J. R. Dora and K. Nemoga, “Clone node detection attacks and mitigation mechanisms in static wireless sensor networks,” Journal of Cybersecurity and Privacy, vol. 1, no. 4, pp. 553–579, 2021. DOI: 10.3390/jcp1040028
[20]I. A. Abd El-Moghith and S. M. Darwish, “Towards designing a trusted routing scheme in wireless sensor networks: A new deep blockchain approach,” IEEE Access, vol. 9,103822–103834, 2021. DOI: 10.1109/ACCESS.2021.3098933
[21]M. Hanif, H. Ashraf, Z. Jalil, N. Z. Jhanjhi, M. Humayun, S. Saeed, et al., “AI-based wormhole attack detection techniques in wireless sensor networks,” Electronics (Basel), vol. 11, no. 15, p. 2324, 2022. DOI: 10.3390/electronics11152324
[22]Y. Han, H. Hu, and Y. Guo, “Energy-aware and trust-based secure routing protocol for wireless sensor networks using adaptive genetic algorithm,” IEEE Access, vol. 10, pp. 11538–11550, 2022. DOI: 10.1109/ACCESS.2022.3144015
[23]S. A. Medjahed and F. Boukhatem, “Narwhal Optimizer: A Novel Nature-Inspired Metaheuristic Algorithm,” Int. Arab J. Inf. Technol., vol. 21, no. 3, pp. 418–426, 2024. DOI: 10.34028/iajit/21/3/6
[24]Y. Tang, J. Yan, C. Chakraborty, and Y. Sun, “Hedera: A permissionless and scalable hybrid blockchain consensus algorithm in multiaccess edge computing for IoT,” IEEE Internet Things J., vol. 10, no. 24, pp. 21187–21202, 2023. DOI: 10.1109/JIOT.2023.3279108
[25]L. Tojo, V. Maik, and M. Devi, “Image denoising using multi scaling aided double decker convolutional neural network,” Optik (Stuttg.), vol. •••, p. 170350, 2022. DOI: 10.1016/j.ijleo.2022.170350
[26]A. Q. Tian, F. F. Liu, and H. X. Lv, “Snow Geese Algorithm: A novel migration-inspired meta-heuristic algorithm for constrained engineering optimization problems,” Appl. Math. Model., vol. 126, pp. 327–347, 2024. DOI: 10.1016/j.apm.2023.10.045
[27]A. Keerthika and V. Berlin Hency, “Reinforcement-Learning based energy efficient optimized routing protocol for WSN,” Peer-to-Peer Netw. Appl., vol. 15, no. 3, pp. 1685–1704, 2024. DOI: 10.1007/s12083-022-01315-6
[28]A. Keerthika and V. Berlin Hency, “Reinforcement-Learning based energy efficient optimized routing protocol for WSN,” Peer-to-Peer Netw. Appl., vol. 15, no. 3, pp. 1685–1704, 2022. DOI: 10.1007/s12083-022-01315-6
[29]M. J. Rhesa and S. Revathi, “Energy-Efficient Aware and Secure Model for WSN Lifetime Enhancement Using HDASCII-AE and GEK-GRU,” IEEE Access, vol. 12,140235–140252, 2024. DOI: 10.1109/ACCESS.2024.3461794
[30]S. Sivakumar, M. Mercy Theresa, K. Sudha, and K. Sangeethalakshmi, “Secure Wireless Sensor Networks: A Weighted K-NN and RNN-based Approach for Attack Detection and Localization,” J. Inst. Electron. Telecommun. Eng., vol. 71, no. 3, pp. 864–875, 2025. DOI: 10.1080/03772063.2024.2436970