{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T02:01:28Z","timestamp":1784772088171,"version":"3.55.0"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T00:00:00Z","timestamp":1779840000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T00:00:00Z","timestamp":1784764800000},"content-version":"vor","delay-in-days":57,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Manipal University Jaipur"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Internet Things"],"DOI":"10.1007\/s43926-026-00367-x","type":"journal-article","created":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T07:34:02Z","timestamp":1779867242000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A machine learning enabled IoT framework for intelligent resource management in 6\u00a0G enabled smart cities"],"prefix":"10.1007","volume":"6","author":[{"given":"Sanjay","family":"Agal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruchika","family":"Katariya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,27]]},"reference":[{"key":"367_CR1","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/JIOT.2014.2306328","volume":"1","author":"A Zanella","year":"2014","unstructured":"Zanella A, Bui N, Castellani A, Vangelista L, Zorzi M. Internet of things for smart cities. IEEE Internet Things J. 2014;1:22\u201332. https:\/\/doi.org\/10.1109\/JIOT.2014.2306328.","journal-title":"IEEE Internet Things J"},{"key":"367_CR2","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1016\/j.measurement.2018.07.067","volume":"129","author":"AH Alavi","year":"2018","unstructured":"Alavi AH, Jiao P, Buttlar WG, Lajnef N. Internet of Things-enabled smart cities: state-of-the-art and future trends. Measurement. 2018;129:589\u2013606. https:\/\/doi.org\/10.1016\/j.measurement.2018.07.067.","journal-title":"Measurement"},{"key":"367_CR3","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.dcan.2017.10.002","volume":"4","author":"MS Mahdavinejad","year":"2017","unstructured":"Mahdavinejad MS, et al. Machine learning for internet of things data analysis: a survey. Digit Commun Netw. 2017;4:161\u201375. https:\/\/doi.org\/10.1016\/j.dcan.2017.10.002.","journal-title":"Digit Commun Netw"},{"key":"367_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-020-2955-6","author":"X You","year":"2020","unstructured":"You X, et al. Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts. Sci China Inf Sci. 2020. https:\/\/doi.org\/10.1007\/s11432-020-2955-6.","journal-title":"Sci China Inf Sci"},{"key":"367_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2023.110085","volume":"237","author":"AT Jawad","year":"2023","unstructured":"Jawad AT, Maaloul R, Chaari L. A comprehensive survey on 6G and beyond: enabling technologies, opportunities of machine learning and challenges. Comput Netw. 2023;237:110085. https:\/\/doi.org\/10.1016\/j.comnet.2023.110085.","journal-title":"Comput Netw"},{"key":"367_CR6","doi-asserted-by":"publisher","first-page":"113741","DOI":"10.1109\/ACCESS.2024.3444313","volume":"12","author":"SS Sefati","year":"2024","unstructured":"Sefati SS, et al. A comprehensive survey on resource management in 6g network based on internet of things. IEEE Access. 2024;12:113741\u201384. https:\/\/doi.org\/10.1109\/ACCESS.2024.3444313.","journal-title":"IEEE Access"},{"key":"367_CR7","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1109\/mcom.001.2400213","volume":"63","author":"H Wang","year":"2024","unstructured":"Wang H, et al. Digital Twin Channel for 6G: concepts, architectures and potential applications. IEEE Commun Mag. 2024;63:24\u201330. https:\/\/doi.org\/10.1109\/mcom.001.2400213.","journal-title":"IEEE Commun Mag"},{"key":"367_CR8","doi-asserted-by":"publisher","first-page":"5539","DOI":"10.1016\/j.egyr.2025.11.097","volume":"14","author":"M Yessef","year":"2025","unstructured":"Yessef M, Hakam Y, Tabaa M, Alammar MM, Elbarbary ZM. Digital twin technology in smart cities: a step toward intelligent urban management. Energy Rep. 2025;14:5539\u201357. https:\/\/doi.org\/10.1016\/j.egyr.2025.11.097.","journal-title":"Energy Rep"},{"key":"367_CR9","doi-asserted-by":"publisher","DOI":"10.1145\/3326066","author":"C-H Hong","year":"2019","unstructured":"Hong C-H, Varghese B. Resource management in fog\/edge computing: a survey on architectures, infrastructure, and algorithms. ACM Comput Surv. 2019. https:\/\/doi.org\/10.1145\/3326066.","journal-title":"ACM Comput Surv"},{"key":"367_CR10","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1109\/JIOT.2016.2579198","volume":"3","author":"W Shi","year":"2016","unstructured":"Shi W, Cao J, Zhang Q, Li Y, Xu L. Edge computing: vision and challenges. IEEE Internet Things J. 2016;3:637\u201346. https:\/\/doi.org\/10.1109\/JIOT.2016.2579198.","journal-title":"IEEE Internet Things J"},{"key":"367_CR11","doi-asserted-by":"publisher","first-page":"1463","DOI":"10.32604\/cmc.2023.038417","volume":"76","author":"I Abdullaev","year":"2023","unstructured":"Abdullaev I, et al. Task offloading and resource allocation in IoT based mobile edge computing using deep learning. Comput Mater Cont Comput Mater Cont (Print). 2023;76:1463\u201377. https:\/\/doi.org\/10.32604\/cmc.2023.038417.","journal-title":"Comput Mater Cont Comput Mater Cont (Print)"},{"key":"367_CR12","doi-asserted-by":"publisher","first-page":"65205","DOI":"10.1109\/ACCESS.2023.3288698","volume":"11","author":"RM Sohaib","year":"2023","unstructured":"Sohaib RM, et al. Intelligent resource management for embb and urllc in 5g and beyond wireless networks. IEEE Access. 2023;11:65205\u201321. https:\/\/doi.org\/10.1109\/ACCESS.2023.3288698.","journal-title":"IEEE Access"},{"key":"367_CR13","doi-asserted-by":"publisher","first-page":"2416","DOI":"10.1109\/TNSE.2020.2978856","volume":"7","author":"H Peng","year":"2020","unstructured":"Peng H, Shen X. Deep reinforcement learning based resource management for multi-access edge computing in vehicular networks. IEEE Trans Netw Sci Eng. 2020;7:2416\u201328. https:\/\/doi.org\/10.1109\/TNSE.2020.2978856.","journal-title":"IEEE Trans Netw Sci Eng"},{"key":"367_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2023.109827","volume":"231","author":"DJ Birabwa","year":"2023","unstructured":"Birabwa DJ, Ramotsoela D, Ventura N. Multi-agent deep reinforcement learning for user association and resource allocation in integrated terrestrial and non-terrestrial networks. Comput Netw. 2023;231:109827. https:\/\/doi.org\/10.1016\/j.comnet.2023.109827.","journal-title":"Comput Netw"},{"key":"367_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2024.101251","volume":"27","author":"MH Alsharif","year":"2024","unstructured":"Alsharif MH, Kannadasan R, Wei W, Nisar KS, Abdel-Aty A-H. A contemporary survey of recent advances in federated learning: taxonomies, applications, and challenges. Internet of Things. 2024;27:101251. https:\/\/doi.org\/10.1016\/j.iot.2024.101251.","journal-title":"Internet of Things"},{"key":"367_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.hcc.2021.100008","volume":"1","author":"Q Xia","year":"2021","unstructured":"Xia Q, Ye W, Tao Z, Wu J, Li Q. A survey of federated learning for edge computing: research problems and solutions. High-Confidence Comput. 2021;1:100008. https:\/\/doi.org\/10.1016\/j.hcc.2021.100008.","journal-title":"High-Confidence Comput"},{"key":"367_CR17","doi-asserted-by":"publisher","first-page":"108952","DOI":"10.1109\/ACCESS.2020.2998358","volume":"8","author":"A Fuller","year":"2020","unstructured":"Fuller A, Fan Z, Day C, Barlow C. Digital twin: enabling technologies, challenges and open research. IEEE Access. 2020;8:108952\u201371. https:\/\/doi.org\/10.1109\/ACCESS.2020.2998358.","journal-title":"IEEE Access"},{"key":"367_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2024.110350","volume":"244","author":"A Hakiri","year":"2024","unstructured":"Hakiri A, Gokhale A, Yahia SB, Mellouli N. A comprehensive survey on digital twin for future networks and emerging Internet of Things industry. Comput Netw. 2024;244:110350. https:\/\/doi.org\/10.1016\/j.comnet.2024.110350.","journal-title":"Comput Netw"},{"key":"367_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2022.100565","volume":"19","author":"A Rejeb","year":"2022","unstructured":"Rejeb A, Rejeb K, Simske S, Treiblmaier H, Zailani S. The big picture on the internet of things and the smart city: a review of what we know and what we need to know. Internet of Things. 2022;19:100565. https:\/\/doi.org\/10.1016\/j.iot.2022.100565.","journal-title":"Internet of Things"},{"key":"367_CR20","doi-asserted-by":"publisher","first-page":"167653","DOI":"10.1109\/ACCESS.2019.2953499","volume":"7","author":"BR Barricelli","year":"2019","unstructured":"Barricelli BR, Casiraghi E, Fogli D. A survey on digital twin: definitions, characteristics, applications, and design implications. IEEE Access. 2019;7:167653\u201371. https:\/\/doi.org\/10.1109\/ACCESS.2019.2953499.","journal-title":"IEEE Access"},{"key":"367_CR21","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.iotcps.2023.02.004","volume":"3","author":"R Singh","year":"2023","unstructured":"Singh R, Gill SS. Edge AI: a survey. Internet Things Cyber-Phys Syst. 2023;3:71\u201392. https:\/\/doi.org\/10.1016\/j.iotcps.2023.02.004.","journal-title":"Internet Things Cyber-Phys Syst"},{"key":"367_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2025.111064","volume":"259","author":"P Vidhya","year":"2025","unstructured":"Vidhya P, Subashini K, Sathishkannan R, Gayathri S. Dynamic network slicing based resource management and service aware Virtual Network Function (VNF) migration in 5G networks. Comput Netw. 2025;259:111064. https:\/\/doi.org\/10.1016\/j.comnet.2025.111064.","journal-title":"Comput Netw"},{"key":"367_CR23","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1109\/MCOM.2017.1600267CM","volume":"55","author":"K Zhang","year":"2017","unstructured":"Zhang K, et al. Security and privacy in smart city applications: challenges and solutions. IEEE Commun Mag. 2017;55:122\u20139. https:\/\/doi.org\/10.1109\/MCOM.2017.1600267CM.","journal-title":"IEEE Commun Mag"},{"key":"367_CR24","doi-asserted-by":"publisher","DOI":"10.1049\/ntw2.70002","author":"AAA Ari","year":"2025","unstructured":"Ari AAA, et al. IoT-5G and B5G\/6G resource allocation and network slicing orchestration using learning algorithms. IET Networks. 2025. https:\/\/doi.org\/10.1049\/ntw2.70002.","journal-title":"IET Networks"},{"key":"367_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2020.102360","volume":"61","author":"B Bhushan","year":"2020","unstructured":"Bhushan B, et al. Blockchain for smart cities: a review of architectures, integration trends and future research directions. Sustain Cities Soc. 2020;61:102360. https:\/\/doi.org\/10.1016\/j.scs.2020.102360.","journal-title":"Sustain Cities Soc"},{"key":"367_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.bcra.2024.100193","volume":"5","author":"AMS Saleh","year":"2024","unstructured":"Saleh AMS. Blockchain for secure and decentralized artificial intelligence in cybersecurity: a comprehensive review. Blockch Res Appl. 2024;5:100193. https:\/\/doi.org\/10.1016\/j.bcra.2024.100193.","journal-title":"Blockch Res Appl"},{"key":"367_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2024.103941","volume":"229","author":"X Zhao","year":"2024","unstructured":"Zhao X, Wu Y, Zhao T, Wang F, Li M. Federated deep reinforcement learning for task offloading and resource allocation in mobile edge computing-assisted vehicular networks. J Netw Comput Appl. 2024;229:103941. https:\/\/doi.org\/10.1016\/j.jnca.2024.103941.","journal-title":"J Netw Comput Appl"},{"key":"367_CR28","doi-asserted-by":"publisher","first-page":"20941","DOI":"10.1038\/s41598-025-05338-w","volume":"15","author":"A Alqahtani","year":"2025","unstructured":"Alqahtani A, Taneja N, Taneja A, Alqahtani N. Energy aware resource management in 6G IoT networks using STAR RIS. Sci Rep. 2025;15:20941. https:\/\/doi.org\/10.1038\/s41598-025-05338-w.","journal-title":"Sci Rep"},{"key":"367_CR29","doi-asserted-by":"publisher","unstructured":"Preethi, et al. Green IOT: AI-Powered solutions for sustainable energy management in smart Devices. Procedia Comput Sci. 2025;258:2312\u201322. https:\/\/doi.org\/10.1016\/j.procs.2025.04.486.","DOI":"10.1016\/j.procs.2025.04.486"},{"key":"367_CR30","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1016\/j.comnet.2017.06.013","volume":"129","author":"E Ahmed","year":"2017","unstructured":"Ahmed E, et al. The role of big data analytics in Internet of Things. Comput Netw. 2017;129:459\u201371. https:\/\/doi.org\/10.1016\/j.comnet.2017.06.013.","journal-title":"Comput Netw"},{"key":"367_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2025.101770","volume":"34","author":"E Dritsas","year":"2025","unstructured":"Dritsas E, Trigka M. Big data and Internet of Things applications in smart cities: recent advances, challenges, and critical issues. Internet of Things. 2025;34:101770. https:\/\/doi.org\/10.1016\/j.iot.2025.101770.","journal-title":"Internet of Things"},{"key":"367_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.sftr.2021.100047","volume":"3","author":"SE Bibri","year":"2021","unstructured":"Bibri SE. Data-driven smart sustainable cities of the future: an evidence synthesis approach to a comprehensive state-of-the-art literature review. Sustain Futures. 2021;3:100047. https:\/\/doi.org\/10.1016\/j.sftr.2021.100047.","journal-title":"Sustain Futures"},{"key":"367_CR33","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.icte.2022.07.002","volume":"9","author":"M Shahjalal","year":"2022","unstructured":"Shahjalal M, et al. Enabling technologies for AI empowered 6G massive radio access networks. ICT Express. 2022;9:341\u201355. https:\/\/doi.org\/10.1016\/j.icte.2022.07.002.","journal-title":"ICT Express"},{"key":"367_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijin.2025.06.001","author":"BR Das","year":"2025","unstructured":"Das BR, Hasan SR, Sabuj SR, Hossain MA, Ray SK. A comprehensive survey on emerging AI technologies for 6G communications: research direction, trends, challenges, and opportunities. Int J Intell Netw. 2025. https:\/\/doi.org\/10.1016\/j.ijin.2025.06.001.","journal-title":"Int J Intell Netw"},{"key":"367_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2024.107696","volume":"166","author":"O Almurshed","year":"2024","unstructured":"Almurshed O, et al. Enhancing performance of machine learning tasks on edge-cloud infrastructures: a cross-domain Internet of Things based framework. Futur Gener Comput Syst. 2024;166:107696. https:\/\/doi.org\/10.1016\/j.future.2024.107696.","journal-title":"Futur Gener Comput Syst"},{"key":"367_CR36","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1016\/j.dcan.2020.12.002","volume":"7","author":"TJ Saleem","year":"2020","unstructured":"Saleem TJ, Chishti MA. Deep learning for the internet of things: potential benefits and use-cases. Digit Commun Netw. 2020;7:526\u201342. https:\/\/doi.org\/10.1016\/j.dcan.2020.12.002.","journal-title":"Digit Commun Netw"},{"key":"367_CR37","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1109\/IOTM.004.2100182","volume":"5","author":"T Zhang","year":"2022","unstructured":"Zhang T, et al. Federated learning for the internet of things: applications, challenges, and opportunities. IEEE Internet of Things Magazine. 2022;5:24\u20139. https:\/\/doi.org\/10.1109\/IOTM.004.2100182.","journal-title":"IEEE Internet of Things Magazine"},{"key":"367_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.hcc.2025.100340","volume":"5","author":"MR Kabir","year":"2025","unstructured":"Kabir MR, Shishir FS, Shomaji S, Ray S. Digital twins in healthcare IoT: a systematic review. High-Confid Comput. 2025;5:100340. https:\/\/doi.org\/10.1016\/j.hcc.2025.100340.","journal-title":"High-Confid Comput"},{"key":"367_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2022.103362","volume":"203","author":"M Liyanage","year":"2022","unstructured":"Liyanage M, et al. A survey on Zero touch network and Service Management (ZSM) for 5G and beyond networks. J Netw Comput Appl. 2022;203:103362. https:\/\/doi.org\/10.1016\/j.jnca.2022.103362.","journal-title":"J Netw Comput Appl"},{"key":"367_CR40","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1109\/MNET.011.2000392","volume":"35","author":"A Boudi","year":"2021","unstructured":"Boudi A, Bagaa M, P\u00f6yh\u00f6nen P, Taleb T, Flinck H. Ai-based resource management in beyond 5g cloud native environment. IEEE Network. 2021;35:128\u201335. https:\/\/doi.org\/10.1109\/MNET.011.2000392.","journal-title":"IEEE Network"},{"key":"367_CR41","unstructured":"3GPP. 3rd generation partnership project; technical specification group radio access network; nr; physical layer procedures for data (release 16). typeTech. Rep. numberTS 38.214, institution3GPP 2020. noteVersion 16.2.0."},{"key":"367_CR42","unstructured":"3GPP. 3rd generation partnership project; technical specification group radio access network; study on channel model for frequencies from 0.5 to 100 ghz (release 16). typeTech. Rep. numberTR 38.901, institution3GPP 2020. noteVersion 16.1.0."},{"key":"367_CR43","doi-asserted-by":"publisher","first-page":"732","DOI":"10.1109\/COMST.2015.2481183","volume":"18","author":"M Dayarathna","year":"2016","unstructured":"Dayarathna M, Wen Y, Fan R. Data center energy consumption modeling: a survey. IEEE Commun Surveys Tutor. 2016;18:732\u201394. https:\/\/doi.org\/10.1109\/COMST.2015.2481183.","journal-title":"IEEE Commun Surveys Tutor"},{"key":"367_CR44","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1016\/j.future.2011.04.017","volume":"28","author":"A Beloglazov","year":"2011","unstructured":"Beloglazov A, Abawajy J, Buyya R. Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing. Futur Gener Comput Syst. 2011;28:755\u201368. https:\/\/doi.org\/10.1016\/j.future.2011.04.017.","journal-title":"Futur Gener Comput Syst"},{"key":"367_CR45","doi-asserted-by":"publisher","unstructured":"Song T, Lopez D, Meo M, Piovesan N, Renga D. High altitude platform stations: the new network energy efficiency enabler in the 6g era. In 2024 IEEE Wireless Communications and Networking Conference (WCNC), 2024. pp. 1\u20136. https:\/\/doi.org\/10.1109\/WCNC57260.2024.10571153.","DOI":"10.1109\/WCNC57260.2024.10571153"},{"key":"367_CR46","unstructured":"3GPP. 3rd generation partnership project; technical specification group services and system aspects; service requirements for the 5g system (release 16). typeTech. Rep. numberTS 22.261, institution3GPP 2020. noteVersion 16.5.0."},{"key":"367_CR47","unstructured":"3GPP. 3rd generation partnership project; technical specification group radio access network; study on scenarios and requirements for next generation access technologies (release 15). typeTech. Rep. numberTR 38.913, institution3GPP 2018. noteVersion 15.0.0."},{"key":"367_CR48","doi-asserted-by":"publisher","first-page":"3133","DOI":"10.1109\/COMST.2019.2916583","volume":"21","author":"NC Luong","year":"2019","unstructured":"Luong NC, et al. Applications of deep reinforcement learning in communications and networking: a survey. IEEE Commun Surveys Tutor. 2019;21:3133\u201374. https:\/\/doi.org\/10.1109\/COMST.2019.2916583.","journal-title":"IEEE Commun Surveys Tutor"},{"key":"367_CR49","doi-asserted-by":"publisher","unstructured":"Zhang X. Multi-Objective Resource Allocation in Edge Computing Using Improved Genetic Algorithm with Knowledge-Based Crossover and Segmentation Mutation. Informatica. 2025. https:\/\/doi.org\/10.31449\/inf.v49i25.7965","DOI":"10.31449\/inf.v49i25.7965"},{"key":"367_CR50","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih V, et al. Human-level control through deep reinforcement learning. Nature. 2015;518:529\u201333. https:\/\/doi.org\/10.1038\/nature14236.","journal-title":"Nature"},{"key":"367_CR51","doi-asserted-by":"publisher","unstructured":"Xiang J, Li Q, Dong X. Ren Z. Continuous control with deep reinforcement learning for mobile robot navigation. In 2019 Chinese Automation Congress (CAC). 2019. pp. 1501\u20131506. https:\/\/doi.org\/10.1109\/CAC48633.2019.8996652.","DOI":"10.1109\/CAC48633.2019.8996652"}],"container-title":["Discover Internet of Things"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s43926-026-00367-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43926-026-00367-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43926-026-00367-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T01:15:50Z","timestamp":1784769350000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s43926-026-00367-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,27]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["367"],"URL":"https:\/\/doi.org\/10.1007\/s43926-026-00367-x","relation":{},"ISSN":["2730-7239"],"issn-type":[{"value":"2730-7239","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,27]]},"assertion":[{"value":"20 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This research is entirely computational and simulation-based. It does not involve human participants, human data, animal subjects, or biological materials. The experimental validation was conducted using a co-simulation environment comprising synthetic traffic models, publicly available smart city datasets (e.g., Madrid Air Quality Network) and standard network simulation tools. Primary data collection was not performed from human subjects. Therefore, approval from an ethics committee was not required for this study.","order":1,"name":"Ethics","label":"Ethics approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This research did not involve any human participants, human data, or biological materials. No informed consent was required as the study is purely computational and simulation-based.","order":2,"name":"Ethics","label":"Consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":3,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"98"}}