{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T15:30:14Z","timestamp":1773070214578,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T00:00:00Z","timestamp":1772928000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009967","name":"Bingtuan Major Science and Technology Project","doi-asserted-by":"publisher","award":["2023AA001"],"award-info":[{"award-number":["2023AA001"]}],"id":[{"id":"10.13039\/501100009967","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004317","name":"Shihezi Financial Science and Technology Project","doi-asserted-by":"publisher","award":["2024GY08"],"award-info":[{"award-number":["2024GY08"]}],"id":[{"id":"10.13039\/501100004317","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009967","name":"Bingtuan Science and Technology Program","doi-asserted-by":"publisher","award":["2023ZD045"],"award-info":[{"award-number":["2023ZD045"]}],"id":[{"id":"10.13039\/501100009967","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Bingtuan Key Areas Science and Technology Research Project","award":["2024AB080"],"award-info":[{"award-number":["2024AB080"]}]},{"name":"Bingtuan Science and Technology Innovation Talent Program","award":["2023CB005"],"award-info":[{"award-number":["2023CB005"]}]},{"name":"Bingtuan Science and Technology Innovation Talent Program","award":["2023ZD066"],"award-info":[{"award-number":["2023ZD066"]}]},{"name":"Bingtuan Science and Technology Innovation Talent Program","award":["2022CB002-08"],"award-info":[{"award-number":["2022CB002-08"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Byzantine fault tolerance (BFT) protocols serve as the cornerstone of data consistency in permissioned blockchains; however, their scalability is inherently constrained by stochastic leader-centric bottlenecks and rigid, non-adaptive timeout mechanisms. Existing rule-based heuristics often fail to capture high-entropy and time-varying network latency, leading to frequent view changes and severe performance degradation under network volatility. To mitigate this epistemic uncertainty, this paper proposes TimesNet-BFT, a novel entropy-aware optimization framework. By leveraging TimesNet\u2019s transformation of one-dimensional time series into two-dimensional tensors for multi-periodicity analysis, the framework accurately characterizes stochastic nodal latency patterns to facilitate entropy-minimized dynamic leader election and adaptive timeout strategies. Extensive evaluations conducted on simulated and real-world trace-driven Internet of Vehicles (IoV) scenarios validate the proposed approach, achieving a prediction MAPE below 5% alongside robust zero-shot generalization. Notably, under high-entropy network conditions, the framework demonstrates up to a 191.9% increase in throughput and mitigates latency variance by 73.3%, effectively neutralizing the structural bottlenecks inherent to traditional information-agnostic protocols. Crucially, by mathematically decoupling consensus safety from AI prediction errors, the system introduces an aggressive liveness paradigm that maintains minimal control plane overhead while significantly enhancing the entropic stability of the consensus process.<\/jats:p>","DOI":"10.3390\/e28030302","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T10:16:12Z","timestamp":1773051372000},"page":"302","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TimesNet-BFT: Mitigating Network State Uncertainty in Byzantine Consensus via Deep Temporal Modeling"],"prefix":"10.3390","volume":"28","author":[{"given":"Haolong","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Shihezi University, Shihezi 832000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haijun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Shihezi University, Shihezi 832000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yahui","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Shihezi University, Shihezi 832000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongliang","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Shihezi University, Shihezi 832000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7781-3005","authenticated-orcid":false,"given":"Pan","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Shihezi University, Shihezi 832000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Enaya, A., Fernando, X., and Kashef, R. (2025). Survey of blockchain-based applications for IoT. Appl. Sci., 15.","DOI":"10.3390\/app15084562"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1080\/0952813X.2024.2391778","article-title":"Byzantine fault tolerance in distributed machine learning: A survey","volume":"37","author":"Bouhata","year":"2025","journal-title":"J. Exp. Theor. Artif. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wiseman, Y. (2023). Adapting the H.264 standard to the Internet of Vehicles. Technologies, 11.","DOI":"10.3390\/technologies11040103"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5547","DOI":"10.1007\/s10586-023-04257-7","article-title":"Scalability of blockchain: A comprehensive review and future research direction","volume":"27","author":"Rao","year":"2024","journal-title":"Clust. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yuan, F., Huang, X., Zheng, L., Wang, L., Wang, Y., Yan, X., Gu, S., and Peng, Y. (2025). The evolution and optimization strategies of a PBFT consensus algorithm for consortium blockchains. Information, 16.","DOI":"10.3390\/info16040268"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yin, M., Malkhi, D., Reiter, M.K., Gueta, G.G., and Abraham, I. (2019). HotStuff: BFT consensus with linearity and responsiveness. Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing, ACM.","DOI":"10.1145\/3293611.3331591"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Delladetsimas, A.P., Papangelou, S., Iosif, E., and Giaglis, G. (2025). Leadership uniformity in timeout-based quorum Byzantine fault tolerance (QBFT) consensus. Big Data Cogn. Comput., 9.","DOI":"10.3390\/bdcc9080196"},{"key":"ref_8","unstructured":"Wu, C., Qin, H., Amiri, M.J., Loo, B.T., Malkhi, D., and Marcus, R. (2025). BFTBrain: Adaptive BFT consensus with reinforcement learning. Proceedings of the 22nd USENIX Symposium on Networked Systems Design and Implementation (NSDI 25), USENIX Association."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hussain, M., Mehmood, A., Khan, M.A., Khan, R., and Lloret, J. (2025). Reputation-based leader selection consensus algorithm with rewards for blockchain technology. Computers, 14.","DOI":"10.3390\/computers14010020"},{"key":"ref_10","unstructured":"Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., and Long, M. (2023, January 1\u20135). TimesNet: Temporal 2d-variation modeling for general time series analysis. Proceedings of the Eleventh International Conference on Learning Representations (ICLR), Kigali, Rwanda."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"108032","DOI":"10.1016\/j.comcom.2024.108032","article-title":"An efficient sharding consensus protocol for improving blockchain scalability","volume":"231","author":"Lu","year":"2025","journal-title":"Comput. Commun."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1109\/TNSM.2024.3498594","article-title":"USMN-SCA: A blockchain sharding consensus algorithm with tolerance for an unlimited scale of malicious nodes","volume":"22","author":"Wu","year":"2024","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e2300","DOI":"10.1002\/nem.2300","article-title":"Duo-H: An effectual consensus algorithm using two-tier shard consortium blockchain mechanism for enhanced privacy protection","volume":"34","author":"Devi","year":"2024","journal-title":"Int. J. Netw. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chen, R., Luo, H., Sun, G., Liu, X., and Yu, H. (2025). DRDST: Low-latency DAG consensus through robust dynamic sharding and tree-broadcasting for IoV. Proceedings of the 2025 IEEE International Conference on Communications (ICC), IEEE.","DOI":"10.1109\/ICC52391.2025.11161306"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111275","DOI":"10.1016\/j.comnet.2025.111275","article-title":"FDSS: Flight data sharing scheme based on blockchain with dynamic, secure and efficient consensus algorithm","volume":"265","author":"Xu","year":"2025","journal-title":"Comput. Netw."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6300","DOI":"10.1109\/TII.2023.3342473","article-title":"Efficient and secure blockchain consensus algorithm for heterogeneous industrial Internet of Things nodes based on double-DAG","volume":"20","author":"Chen","year":"2024","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"104003","DOI":"10.1016\/j.jnca.2024.104003","article-title":"Zebra: A cluster-aware blockchain consensus algorithm","volume":"232","author":"Wan","year":"2024","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_18","unstructured":"Puthal, D., Mohanty, S.P., Yanambaka, V.P., and Kougianos, E. (2020). PoAh: A novel consensus algorithm for fast scalable private blockchain for large-scale IoT frameworks. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lu, D. (2024). A state-function-driven consensus protocol for blockchain networks. 2024 IEEE International Conference on Blockchain (Blockchain), IEEE.","DOI":"10.1109\/Blockchain62396.2024.00081"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"262","DOI":"10.26599\/TST.2023.9010160","article-title":"Jamming-resilient consensus for wireless blockchain networks","volume":"30","author":"Zou","year":"2024","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_21","unstructured":"Lin, J., Li, H., Xing, H., Huang, R., Huang, W., Deng, S., Zhang, Y., Zeng, W., Lu, P., and Wang, X. (2024). Q-PnV: A quantum consensus mechanism for security consortium blockchains. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kim, H., Kim, W., Kang, Y., Kim, H., and Seo, H. (2024). Post-quantum delegated proof of luck for blockchain consensus algorithm. Appl. Sci., 14.","DOI":"10.3390\/app14188394"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ding, J., Wu, X., Tian, J., and Li, Y. (2025). RE-BPFT: An improved PBFT consensus algorithm for consortium blockchain based on node credibility and ID3-based classification. Appl. Sci., 15.","DOI":"10.3390\/app15137591"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, J., Sun, Y., Zhang, Z., Ren, W., and Luo, L. (2024). A reputation-aware randomization consensus algorithm for performanceoptimization in blockchain systems. Proceedings of the 2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD), IEEE.","DOI":"10.1109\/CSCWD61410.2024.10580101"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"15434","DOI":"10.1109\/JIOT.2023.3347232","article-title":"TP-PBFT: A scalable PBFT based on threshold proxy signature for IoT-blockchain applications","volume":"11","author":"Tang","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6936","DOI":"10.1109\/TCE.2024.3408227","article-title":"Louvain-based committee formation and reputation-driven leadership for hybrid blockchain consensus","volume":"70","author":"Wadhwa","year":"2024","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2558","DOI":"10.1109\/TSC.2024.3399653","article-title":"Long-term proof-of-contribution: An incentivized consensus algorithm for blockchain-enabled federated learning","volume":"17","author":"Zhao","year":"2024","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s10723-024-09743-9","article-title":"AI-driven task scheduling strategy with blockchain integration for edge computing","volume":"22","author":"Sinha","year":"2024","journal-title":"J. Grid Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/TSG.2024.3445659","article-title":"Power line monitoring-based consensus algorithm for performance enhancement of energy blockchain applications in Smart Grid 2.0","volume":"16","author":"Yapa","year":"2025","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"32850","DOI":"10.1038\/s41598-025-00951-1","article-title":"Optimizing IoV cloud trust with adaptive blockchain and reinforcement learning","volume":"15","author":"Gopi","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4001","DOI":"10.1109\/JBHI.2023.3327485","article-title":"Generative AI-based data completeness augmentation algorithm for data-driven smart healthcare","volume":"29","author":"Lan","year":"2025","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"110213","DOI":"10.1016\/j.compeleceng.2025.110213","article-title":"AI-driven dynamic trust management and blockchain-based security in industrial IoT","volume":"123","author":"Kumar","year":"2025","journal-title":"Comput. Electr. Eng."},{"key":"ref_33","unstructured":"Sayeed, S., and Marco-Gisbert, H. (2025). SoK: Security and privacy of AI agents for blockchain. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4024","DOI":"10.1109\/TNSM.2024.3419151","article-title":"Meta-governance: Blockchain-driven metaverse platform for mitigating misbehavior using smart contract and AI","volume":"21","author":"Rahaman","year":"2024","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1089\/big.2020.0159","article-title":"Deep learning for time series forecasting: A survey","volume":"9","author":"Torres","year":"2021","journal-title":"Big Data"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1145\/52325.52356","article-title":"Congestion avoidance and control","volume":"18","author":"Jacobson","year":"1988","journal-title":"ACM SIGCOMM Comput. Commun. Rev."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Van Der Heijden, R.W., Lukaseder, T., and Kargl, F. (2018). Veremi: A dataset for comparable evaluation of misbehavior detection in vanets. Proceedings of the International Conference on Security and Privacy in Communication Systems, Springer.","DOI":"10.1007\/978-3-030-01701-9_18"},{"key":"ref_38","unstructured":"Box, G.E., Jenkins, G.M., Reinsel, G.C., and Ljung, G.M. (2015). Time Series Analysis: Forecasting and Control, John Wiley & Sons."},{"key":"ref_39","first-page":"321","article-title":"Multivariable functional interpolation and adaptive networks","volume":"2","author":"Broomhead","year":"1988","journal-title":"Complex Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Graves, A. (2012). Long short-term memory. Supervised Sequence Labelling with Recurrent Neural Networks, Springer.","DOI":"10.1007\/978-3-642-24797-2"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/3\/302\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T10:25:24Z","timestamp":1773051924000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/3\/302"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,8]]},"references-count":40,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["e28030302"],"URL":"https:\/\/doi.org\/10.3390\/e28030302","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,8]]}}}