2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 18 , Issue 2 , PP: 20-35, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Rasha Hani Salman 1 * , Hala Bahjat Abdul Wahab 2
Doi: https://doi.org/10.54216/JISIoT.180202
Blockchain technology has recently emerged as a fundamental pillar of decentralized and secure systems. However, many Proof-of-Work (POW) algorithms suffer from some challenges, including their inefficiency in discovering the value of Nonces due to their reliance on random attempts, which consume significant resources, energy, and time, making them difficult to use in lightweight blockchain environments, especially in resource-limited environments such as mobile devices and others. The main goal of this paper is to introduce a smart system that replaces random guessing with a more intelligent and predictive approach using deep learning models like CNN2D, GRU, LSTM, and hybrid models. The intelligent optimization algorithm (GWO) is also used, which has been enhanced with random Lévy jumps, in addition to improved clustering using a genetic algorithm. The results, after applying the system to health data across three difficulty levels (4, 6, and 8), showed that the intelligent neural model was the most stable and accurate, achieving the lowest error values and the highest generalization ability, with a maximum error value of (0.0136) at the highest difficulty level (8). The hybrid GA–KMeans algorithm demonstrated high efficiency in improving clustering accuracy. It achieved the highest similarity index value (0.9980) and the lowest Davis-Bolden index value (0.0000), which plays a significant role in guiding searches efficiently and effectively. The CNN2D model also achieved ideal numerical results, but it is prone to overlearning, while the GRU neural model provided an efficient balance between stability and accuracy. Other hybrid models, such as GRU+CNN, have shown excellent performance, but with varying results. The proposed system proves to be an efficient and intelligent alternative to the low-cost random approach for Nonce discovery in lightweight blockchain environments.
Neural Network , Grey Wolf Algorithm , Levy Flight , Lightweight Blockchain Systems , Ascon Algorithm , Smart Mining , Nonce Value Optimization
[1] S. Nakamoto, "Bitcoin: A peer-to-peer electronic cash system," 2008. [Online]. Available: https://bitcoin.org/bitcoin.pdf.
[2] Z. Zheng, S. Xie, H. Dai, X. Chen, and H. Wang, "An overview of blockchain technology: Architecture, consensus, and future trends," in Proc. 2017 IEEE Int. Congr. Big Data (BigData Congr.), 2017, pp. 557-564.
[3] R. H. Salman and H. B. A. Wahab, "Enhancing blockchain security and decentralization using the ASCON algorithm for lightweight applications," IEEE Access, vol. 12, pp. 48570-48583, 2024.
[4] J. Yli-Huumo, D. Ko, S. Choi, S. Park, and K. Smolander, "Where is current research on blockchain technology?—a systematic review," PLOS ONE, vol. 11, no. 10, p. e0163477, Oct. 2016.
[5] M. A. Alzubi, R. H. Salman, and H. B. A. Wahab, "Using lightweight post-quantum algorithms for enhancing blockchain security," J. Inf. Process. Syst., vol. 19, no. 1, pp. 1-15, Feb. 2024.
[6] W. Wang et al., "A survey on consensus mechanisms and mining strategy management in blockchain networks," IEEE Access, vol. 7, pp. 22328-22370, 2019.
[7] M. Swan, Blockchain: Blueprint for a New Economy. Sebastopol, CA, USA: O'Reilly Media, 2015.
[8] K. Christidis and M. Devetsikiotis, "Blockchains and smart contracts for the internet of things," IEEE Access, vol. 4, pp. 2292-2303, 2016.
[9] A. Reyna, C. Martín, J. Chen, E. Soler, and M. Díaz, "On blockchain and its integration with IoT: Challenges and opportunities," Future Gener. Comput. Syst., vol. 88, pp. 173-190, Nov. 2018.
[10] A. de Vries, "Bitcoin's growing energy problem," Joule, vol. 2, no. 5, pp. 801-805, May 2018.
[11] I. Eyal and E. G. Sirer, "Majority is not enough: Bitcoin mining is vulnerable," in Proc. Int. Conf. Financ. Cryptogr. Data Secur., 2014, pp. 436-454.
[12] G. T. Nguyen and K. Kim, "A survey about consensus algorithms used in blockchain," J. Inf. Process. Syst., vol. 14, no. 1, pp. 101-128, Feb. 2018.
[13] X. Li, P. Jiang, T. Chen, X. Luo, and Q. Wen, "A survey on the security of blockchain systems," Future Gener. Comput. Syst., vol. 107, pp. 841-853, Jun. 2020.
[14] M. J. Dworkin, "SHA-3 standard: Permutation-based hash and extendable-output functions," Nat. Inst. Stand. Technol., Gaithersburg, MD, USA, NIST FIPS 202, Aug. 2015.
[15] [15] M. Conti, E. S. Kumar, C. Lal, and S. Ruj, "A survey on security and privacy issues of bitcoin," IEEE Commun. Surv. Tutor, vol. 20, no. 4, pp. 3416-3452, 4th Quart. 2018.
[16] S. Mirjalili, S. M. Mirjalili, and A. Lewis, "Grey wolf optimizer," Adv. Eng. Softw., vol. 69, pp. 46-61, Mar. 2014.
[17] X.-S. Yang, Nature-Inspired Metaheuristic Algorithms, 2nd ed. Luniver Press, 2010.
[18] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, "Empirical evaluation of gated recurrent neural networks on sequence modeling," arXiv:1412.3555, Dec. 2014.
[19] N. Y. Ahn and D. H. Lee, "Secure vehicle communications using proof-of-nonce blockchain," arXiv: 2011.07846, Nov. 2020.
[20] R. Li and W. Ying, "Improved particle swarm optimization based on Lévy flights," J. Syst. Simul., vol. 29, no. 8, pp. 1685-1692, 2020.
[21] D. C. Nguyen et al., "Intelligent blockchain-based edge computing via deep reinforcement learning: Solutions and challenges," IEEE Netw., vol. 36, no. 6, pp. 12-19, Nov./Dec. 2022.
[22] A. Baykasoğlu and M. E. Şenol, "WSAR with Levy flight for constrained optimization," in Proc. 7th Int. Conf. Harmony Search, Soft Comput. Appl. (ICHSA 2022), 2022, pp. 217-225.
[23] K. S. Kumar et al., "A secure and efficient blockchain and DLT-based optimal resource management in digital twin beyond 5G networks using hybrid energy valley and Lévy flight distributor optimization algorithm," IEEE Access, vol. 12, pp. 39902-39921, 2024.
[24] N. A. Saqib and S. T. AL-Talla, "Scaling up security and efficiency in financial transactions and blockchain systems," J. Sens. Actuator Netw., vol. 12, no. 2, p. 31, Apr. 2023.
[25] F. M. Alserhani, "Integrating deep learning and metaheuristics algorithms for blockchain-based reassurance data management in the detection of malicious IoT nodes," Peer-to-Peer Netw. Appl., vol. 17, no. 6, pp. 3856-3882, Nov. 2024.
[26] P. A. Raj, M. Rajakumaran, S. P. Murugan, and S. Senthilkumar, "Hybridization of metaheuristic algorithms for resource scheduling in distributed robotic control system," Discov. Appl. Sci., vol. 7, no. 5, p. 210, May 2025.
[27] V. M. Eskov, V. V. Eskov, Y. V. Vochmina, D. V. Gorbunov, and L. K. Ilyashenko, "Shannon entropy in the research on stationary regimes and the evolution of complexity," Mosc. Univ. Phys. Bull., vol. 72, no. 3, pp. 309-317, 2017.
[28] J. E. McLaughlin, G. W. McLaughlin, J. S. McLaughlin, and C. Y. White, "Using Simpson’s diversity index to examine multidimensional models of diversity in health professions education," Int. J. Med. Educ., vol. 7, pp. 1-6, Jan. 2016.
[29] O. A. Hassen et al., "Towards a secure signature scheme based on multimodal biometric technology: Application for IoT blockchain network," Symmetry, vol. 12, no. 10, p. 1699, Oct. 2020.
[30] A. H. Alzubi, R. H. Salman, and H. B. A. Wahab, "Enhancing blockchain security using lightweight post-quantum algorithms," J. Inf. Process. Syst., vol. 19, no. 1, pp. 1-15, Feb. 2024.
