{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T21:28:12Z","timestamp":1769203692090,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819978717","type":"print"},{"value":"9789819978724","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T00:00:00Z","timestamp":1699401600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T00:00:00Z","timestamp":1699401600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-7872-4_14","type":"book-chapter","created":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T14:02:42Z","timestamp":1699365762000},"page":"241-262","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Delay Optimization for\u00a0Consensus Communication in\u00a0Blockchain-Based End-Edge-Cloud Network"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1060-1208","authenticated-orcid":false,"given":"Shengcheng","family":"Ma","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6135-3614","authenticated-orcid":false,"given":"Shuai","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Wei-Tek","family":"Tsai","sequence":"additional","affiliation":[]},{"given":"Yaowei","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,8]]},"reference":[{"issue":"4","key":"14_CR1","doi-asserted-by":"publisher","first-page":"4298","DOI":"10.1109\/TVT.2020.2973651","volume":"69","author":"Y Lu","year":"2020","unstructured":"Lu, Y., Huang, X., Zhang, K., Maharjan, S., Zhang, Y.: Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles. IEEE Trans. Veh. Technol. 69(4), 4298\u20134311 (2020)","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"9","key":"14_CR2","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1109\/MCOM.001.2000175","volume":"58","author":"T Maksymyuk","year":"2021","unstructured":"Maksymyuk, T., et al.: Blockchain-empowered framework for decentralized network management in 6G. IEEE Commun. Mag. 58(9), 86\u201392 (2021)","journal-title":"IEEE Commun. Mag."},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Shubhani, A., Neeraj, K., Sudeep, T.: Blockchain-envisioned UAV communication using 6G networks: open issues, use cases, and future directions. IEEE Internet Things J. 8(7) (2021)","DOI":"10.1109\/JIOT.2020.3020819"},{"issue":"3","key":"14_CR4","doi-asserted-by":"publisher","first-page":"2459","DOI":"10.1109\/TVT.2022.3143828","volume":"71","author":"M Jiang","year":"2022","unstructured":"Jiang, M., Wu, T., Wang, Z., Gong, Y., Zhang, L., Liu, R.P.: A multi-intersection vehicular cooperative control based on end-edge-cloud computing. IEEE Trans. Veh. Technol. 71(3), 2459\u20132471 (2022)","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"1","key":"14_CR5","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1109\/COMST.2022.3218527","volume":"25","author":"S Duan","year":"2023","unstructured":"Duan, S., et al.: Distributed artificial intelligence empowered by end-edge-cloud computing: a survey. IEEE Commun. Surv. Tutor. 25(1), 591\u2013624 (2023)","journal-title":"IEEE Commun. Surv. Tutor."},{"issue":"2","key":"14_CR6","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1109\/MWC.010.2100491","volume":"29","author":"S Zhang","year":"2022","unstructured":"Zhang, S., Wang, Z., Zhou, Z., Wang, Y., Zhang, H., et al.: Blockchain and federated deep reinforcement learning based secure cloud-edge-end collaboration in power IoT. IEEE Wirel. Commun. 29(2), 84\u201391 (2022)","journal-title":"IEEE Wirel. Commun."},{"issue":"3","key":"14_CR7","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1109\/MCOM.001.2000857","volume":"59","author":"B Mafakheri","year":"2021","unstructured":"Mafakheri, B., Heider-Aviet, A., Riggio, R., Goratti, L.: Smart contracts in the 5G roaming architecture: the fusion of blockchain with 5G networks. IEEE Commun. Mag. 59(3), 77\u201383 (2021)","journal-title":"IEEE Commun. Mag."},{"issue":"6","key":"14_CR8","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1109\/MNET.021.1900629","volume":"34","author":"W Li","year":"2020","unstructured":"Li, W., Su, Z., Li, R., Zhang, K., Wang, Y.: Blockchain-based data security for artificial intelligence applications in 6G networks. IEEE Netw. 34(6), 31\u201337 (2020)","journal-title":"IEEE Netw."},{"issue":"12","key":"14_CR9","doi-asserted-by":"publisher","first-page":"3325","DOI":"10.1109\/JSAC.2022.3213323","volume":"40","author":"X Wang","year":"2022","unstructured":"Wang, X., Zhao, Y., Qiu, C., Liu, Z., Nie, J., Leung, V.C.M.: InFEDge: a blockchain-based incentive mechanism in hierarchical federated learning for end-edge-cloud communications. IEEE J. Sel. Areas Commun. 40(12), 3325\u20133342 (2022)","journal-title":"IEEE J. Sel. Areas Commun."},{"issue":"6","key":"14_CR10","doi-asserted-by":"publisher","first-page":"1503","DOI":"10.1109\/TPDS.2021.3112604","volume":"33","author":"Y Ding","year":"2022","unstructured":"Ding, Y., Li, K., Liu, C., Li, K.: InFEDGe: a blockchain-based incentive mechanism in hierarchical federated learning for end-edge-cloud communications. IEEE Trans. Parallel Distrib. Syst. 33(6), 1503\u20131519 (2022)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"issue":"6","key":"14_CR11","doi-asserted-by":"publisher","first-page":"4321","DOI":"10.1109\/TWC.2020.2982627","volume":"19","author":"J Feng","year":"2020","unstructured":"Feng, J., Yu, F.R., Pei, Q., Du, J., Zhu, L.: Joint optimization of radio and computational resources allocation in blockchain-enabled mobile edge computing systems. IEEE Trans. Wirel. Commun. 19(6), 4321\u20134334 (2020)","journal-title":"IEEE Trans. Wirel. Commun."},{"issue":"15","key":"14_CR12","doi-asserted-by":"publisher","first-page":"13195","DOI":"10.1109\/JIOT.2022.3140811","volume":"9","author":"X Zhang","year":"2022","unstructured":"Zhang, X., Peng, M., Yan, S., Sun, Y.: Joint communication and computation resource allocation in fog-based vehicular networks. IEEE Internet Things J. 9(15), 13195\u201313208 (2022)","journal-title":"IEEE Internet Things J."},{"issue":"23","key":"14_CR13","doi-asserted-by":"publisher","first-page":"16779","DOI":"10.1109\/JIOT.2021.3052778","volume":"8","author":"Z Yang","year":"2021","unstructured":"Yang, Z., Liang, B., Ji, W.: An intelligent end-edge-cloud architecture for visual IoT-assisted healthcare systems. IEEE Internet Things J. 8(23), 16779\u201316786 (2021)","journal-title":"IEEE Internet Things J."},{"issue":"11","key":"14_CR14","doi-asserted-by":"publisher","first-page":"8047","DOI":"10.1109\/TII.2022.3164395","volume":"18","author":"H Liao","year":"2022","unstructured":"Liao, H., Jia, Z., Zhou, Z., Wang, Y., Zhang, H., et al.: Cloud-edge-end collaboration in air-ground integrated power IoT: a semi-distributed learning approach. IEEE Trans. Ind. Inform. 18(11), 8047\u20138057 (2022)","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"10","key":"14_CR15","doi-asserted-by":"publisher","first-page":"9399","DOI":"10.1109\/JIOT.2020.3007869","volume":"7","author":"M Li","year":"2020","unstructured":"Li, M., Yu, F.R., Si, P., Wu, W., Zhang, Y.: Resource optimization for delay-tolerant data in blockchain-enabled IoT with edge computing: a deep reinforcement learning approach. IEEE Internet Things J. 7(10), 9399\u20139412 (2020)","journal-title":"IEEE Internet Things J."},{"issue":"6","key":"14_CR16","doi-asserted-by":"publisher","first-page":"3559","DOI":"10.1109\/TII.2019.2897805","volume":"15","author":"M Liu","year":"2019","unstructured":"Liu, M., Yu, F.R., Teng, Y., Leung, V.C.M., Song, M.: Performance optimization for blockchain-enabled industrial internet of things (IIoT) systems: a deep reinforcement learning approach. IEEE Trans. Ind. Inform. 15(6), 3559\u20133570 (2019)","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"5","key":"14_CR17","doi-asserted-by":"publisher","first-page":"3572","DOI":"10.1109\/TII.2021.3117481","volume":"18","author":"G Qu","year":"2022","unstructured":"Qu, G., Cui, N., Wu, H., Li, R., Ding, Y.: ChainFL: a simulation platform for joint federated learning and blockchain in edge\/cloud computing environments. IEEE Trans. Ind. Inform. 18(5), 3572\u20133581 (2022)","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"7","key":"14_CR18","doi-asserted-by":"publisher","first-page":"5098","DOI":"10.1109\/TII.2020.3017668","volume":"17","author":"Y Lu","year":"2021","unstructured":"Lu, Y., Huang, X., Zhang, K., Maharjan, S., Zhang, Y.: Low-latency federated learning and blockchain for edge association in digital twin empowered 6G networks. IEEE Trans. Ind. Inform. 17(7), 5098\u20135107 (2021)","journal-title":"IEEE Trans. Ind. Inform."},{"key":"14_CR19","unstructured":"Castro, M., Liskov, B.: Practical Byzantine fault tolerance. In: Proceedings of the Third Symposium on Operating Systems Design and Implementation, vol. 17, no. 7, pp. 173\u2013186 (1999)"},{"issue":"5","key":"14_CR20","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1109\/MNET.011.1900536","volume":"34","author":"B Cao","year":"2020","unstructured":"Cao, B., Wang, X., Zhang, W., Song, H., Lv, Z.: A many-objective optimization model of industrial internet of things based on private blockchain. IEEE Netw. 34(5), 78\u201383 (2020)","journal-title":"IEEE Netw."},{"issue":"4","key":"14_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102602","volume":"58","author":"L Chunlin","year":"2021","unstructured":"Chunlin, L., Jing, Z., Xianmin, Y., Luo, Y.: Lightweight blockchain consensus mechanism and storage optimization for resource constrained IoT devices. Inf. Process. Manag. 58(4), 102602 (2021)","journal-title":"Inf. Process. Manag."},{"key":"14_CR22","unstructured":"Ryan, L., Yi, W., Aviv, T., Jean, H., Pieter, A., Igor, M.: Multi-agent actor-critic for mixed cooperative-competitive environments. In: 31st International Conference on Neural Information Processing Systems (NIPS 2017). Curran Associates Inc., Red Hook (2017)"},{"key":"14_CR23","unstructured":"Lillicrap, T.P., et al.: Continuous control with deep reinforcement learning. In: 4th International Conference on Learning Representations, ICLR 2016 (2016)"},{"issue":"5","key":"14_CR24","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TSMC.1983.6313077","volume":"13","author":"AG Barto","year":"1983","unstructured":"Barto, A.G., Sutton, R.S., Anderson, C.W.: Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Trans. Syst. Man Cybern. 13(5), 834\u2013846 (1983)","journal-title":"IEEE Trans. Syst. Man Cybern."}],"container-title":["Lecture Notes in Computer Science","Advanced Parallel Processing Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-7872-4_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T14:07:48Z","timestamp":1699366068000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-7872-4_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,8]]},"ISBN":["9789819978717","9789819978724"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-7872-4_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,8]]},"assertion":[{"value":"8 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APPT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Advanced Parallel Processing Technologies","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanchang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"appt2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conf.ccf.org.cn\/CCFSys2023","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"49","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"23","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"47% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.8","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}