{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T08:19:59Z","timestamp":1768724399614,"version":"3.49.0"},"reference-count":32,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T00:00:00Z","timestamp":1692316800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deputyship for Research &amp; Innovation, Ministry of Education in Saudi Arabia","award":["S-1443-0183"],"award-info":[{"award-number":["S-1443-0183"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this article, we utilize Digital Twins (DT) with edge networks using blockchain technology for reliable real-time data processing and provide a secure, scalable solution to bridge the gap between physical edge networks and digital systems. Then, we suggest a Federated Learning (FL) framework for collaborative computing that runs on a blockchain and is powered by the DT edge network. This framework increases data privacy while enhancing system security and reliability. The provision of sustainable Resource Allocation (RA) and ensure real-time data-processing interaction between Internet of Things (IoT) devices and edge servers depends on a balance between system latency and Energy Consumption (EC) based on the proposed DT-empowered Deep Reinforcement Learning (Deep-RL) agent. The Deep-RL agent evaluates the performance action based on RA actions in DT to distribute its bandwidth resources to IoT devices based on iteration and the actions taken to generate the best policy and enhance learning efficiency at every step. The simulation results show that the proposed Deep-RL-agent-based DT is able to exploit the best policy, select 47.5% of computing activities that are to be carried out locally with 1 MHz bandwidth and minimize the weighted cost of the transmission policy of edge-computing strategies.<\/jats:p>","DOI":"10.3390\/s23167262","type":"journal-article","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T10:28:48Z","timestamp":1692354528000},"page":"7262","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Sustainable Resource Allocation and Reduce Latency Based on Federated-Learning-Enabled Digital Twin in IoT Devices"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5955-8864","authenticated-orcid":false,"given":"Mohammed A.","family":"Alhartomi","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, University of Tabuk, Tabuk 71491, Saudi Arabia"}]},{"given":"Adeeb","family":"Salh","sequence":"additional","affiliation":[{"name":"Faculty of Information and Communication Technology, University Tunku Abdul Rahman (UTAR), Kampar 31900, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0958-4474","authenticated-orcid":false,"given":"Lukman","family":"Audah","sequence":"additional","affiliation":[{"name":"Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Batu Pahat 86400, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3325-857X","authenticated-orcid":false,"given":"Saeed","family":"Alzahrani","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Tabuk, Tabuk 71491, Saudi Arabia"}]},{"given":"Ahmed","family":"Alzahmi","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Tabuk, Tabuk 71491, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2566-2356","authenticated-orcid":false,"given":"Mohammad R.","family":"Altimania","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Tabuk, Tabuk 71491, Saudi Arabia"}]},{"given":"Abdulaziz","family":"Alotaibi","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, University of Tabuk, Tabuk 71491, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0031-6497","authenticated-orcid":false,"given":"Ruwaybih","family":"Alsulami","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Umm Al-Qura University Makkah, Mecca 24382, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5148-627X","authenticated-orcid":false,"given":"Omar","family":"Al-Hartomy","sequence":"additional","affiliation":[{"name":"Department of Physics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1884","DOI":"10.1109\/TII.2022.3183465","article-title":"Optimizing federated learning with deep reinforcement learning for digital twin empowered industrial IoT","volume":"19","author":"Yang","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"55098","DOI":"10.1109\/ACCESS.2021.3069707","article-title":"A survey on deep learning for ultra-reliable and low-latency communications challenges on 6G wireless systems","volume":"9","author":"Salh","year":"2021","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"13789","DOI":"10.1109\/JIOT.2021.3079510","article-title":"Digital twin networks: A survey","volume":"8","author":"Wu","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1722","DOI":"10.1109\/TII.2018.2804917","article-title":"Experimentable digital twins\u2014Streamlining simulation-based systems engineering for industry 4.0","volume":"14","author":"Schluse","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/MNET.001.1800526","article-title":"Edge intelligence and blockchain empowered 5G beyond for the industrial Internet of Things","volume":"33","author":"Zhang","year":"2019","journal-title":"IEEE Netw."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, P., Xu, N., Sun, W., Wang, G., and Zhang, Y. (April, January 29). Distributed incentives and digital twin for resource allocation in air-assisted internet of vehicles. Proceedings of the 2021 IEEE Wireless Communications and Networking Conference (WCNC), Nanjing, China.","DOI":"10.1109\/WCNC49053.2021.9417521"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4312","DOI":"10.1109\/TVT.2020.2973705","article-title":"Deep reinforcement learning and permissioned blockchain for content caching in vehicular edge computing and networks","volume":"69","author":"Dai","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"21980","DOI":"10.1109\/ACCESS.2020.2970143","article-title":"Digital twin: Values, challenges and enablers from a modeling perspective","volume":"8","author":"Rasheed","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhang, G., and Yan, Q. (2018, January 27\u201329). Dynamic resource allocation optimization for digital twin-driven smart shopfloor. Proceedings of the 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), Zhuhai, China.","DOI":"10.1109\/ICNSC.2018.8361283"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5839","DOI":"10.1109\/JIOT.2021.3058213","article-title":"Dynamic digital twin and distributed incentives for resource allocation in aerial-assisted internet of vehicles","volume":"9","author":"Sun","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/TCSS.2021.3068369","article-title":"Digital twin empowered content caching in social-aware vehicular edge networks","volume":"9","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8017","DOI":"10.1007\/s00521-021-06754-5","article-title":"Deep ClassRooms: A deep learning based digital twin framework for on-campus class rooms","volume":"35","author":"Razzaq","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/MNET.2019.1800376","article-title":"Blockchain and deep reinforcement learning empowered intelligent 5G beyond","volume":"33","author":"Dai","year":"2019","journal-title":"IEEE Netw."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7702","DOI":"10.1109\/JIOT.2019.2901840","article-title":"Privacy-preserving support vector machine training over blockchain-based encrypted IoT data in smart cities","volume":"6","author":"Shen","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1109\/MNET.011.1900598","article-title":"Blockchain and federated learning for 5G beyond","volume":"35","author":"Lu","year":"2020","journal-title":"IEEE Netw."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1109\/COMST.2020.2986024","article-title":"Federated learning in mobile edge networks: A comprehensive survey","volume":"22","author":"Lim","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2276","DOI":"10.1109\/JIOT.2020.3015772","article-title":"Communication-efficient federated learning and permissioned blockchain for digital twin edge networks","volume":"8","author":"Lu","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6549","DOI":"10.1109\/JIOT.2022.3162714","article-title":"Dual-driven resource management for sustainable computing in the blockchain-supported digital twin IoT","volume":"10","author":"Wang","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5709","DOI":"10.1109\/TII.2020.3010798","article-title":"Communication-efficient federated learning for digital twin edge networks in industrial IoT","volume":"17","author":"Lu","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5098","DOI":"10.1109\/TII.2020.3017668","article-title":"Low-latency federated learning and blockchain for edge association in digital twin empowered 6G networks","volume":"17","author":"Lu","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, Z., Song, M., Zhang, Z., Song, Y., Wang, Q., and Qi, H. (May, January 29). Beyond inferring class representatives: User-level privacy leakage from federated learning. Proceedings of the IEEE INFOCOM 2019-IEEE Conference on Computer Communications, Paris, France.","DOI":"10.1109\/INFOCOM.2019.8737416"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Triastcyn, A., and Faltings, B. (2019, January 9\u201312). Federated learning with bayesian differential privacy. Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA.","DOI":"10.1109\/BigData47090.2019.9005465"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4968","DOI":"10.1109\/TII.2020.3016320","article-title":"Deep reinforcement learning for stochastic computation offloading in digital twin networks","volume":"17","author":"Dai","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tran, N.H., Bao, W., Zomaya, A., Nguyen, M.N., and Hong, C.S. (May, January 29). Federated learning over wireless networks: Optimization model design and analysis. Proceedings of the IEEE INFOCOM 2019-IEEE Conference on Computer Communications, Paris, France.","DOI":"10.1109\/INFOCOM.2019.8737464"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"16353","DOI":"10.1109\/ACCESS.2023.3244099","article-title":"Energy-Efficient Federated Learning with Resource Allocation for Green IoT Edge Intelligence in B5G","volume":"11","author":"Salh","year":"2023","journal-title":"IEEE Access"},{"key":"ref_26","first-page":"1","article-title":"Atomo: Communication-efficient learning via atomic sparsification","volume":"31","author":"Wang","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_27","unstructured":"Caldas, S., Kone\u010dny, J., McMahan, H.B., and Talwalkar, A. (2018). Expanding the reach of federated learning by reducing client resource requirements. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yuan, N., He, W., Shen, J., Qiu, X., Guo, S., and Li, W. (2020, January 20\u201324). Delay-aware NFV resource allocation with deep reinforcement learning. Proceedings of the NOMS 2020\u20132020 IEEE\/IFIP Network Operations and Management Symposium, Budapest, Hungary.","DOI":"10.1109\/NOMS47738.2020.9110377"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1109\/TNET.2020.3035770","article-title":"Federated learning over wireless networks: Convergence analysis and resource allocation","volume":"29","author":"Dinh","year":"2020","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"9294","DOI":"10.1109\/JIOT.2021.3057594","article-title":"Resource management for secure computation offloading in softwarized cyber\u2013physical systems","volume":"8","author":"Wang","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_32","unstructured":"Xiao, H., Rasul, K., and Vollgraf, R. (2017). Fashion-mnist: A novel image dataset for benchmarking machine learning algorithms. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7262\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:37:10Z","timestamp":1760128630000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7262"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,18]]},"references-count":32,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["s23167262"],"URL":"https:\/\/doi.org\/10.3390\/s23167262","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,18]]}}}