{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T17:41:10Z","timestamp":1772041270463,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,7,27]],"date-time":"2018-07-27T00:00:00Z","timestamp":1532649600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772286"],"award-info":[{"award-number":["61772286"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Jiangsu Province of China","award":["BK20160910"],"award-info":[{"award-number":["BK20160910"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The adaptive coordination of trust services can provide highly dependable and personalized solutions for industrial requirements in the service-oriented industrial internet of things (IIoT) architecture to achieve efficient utilization of service resources. Although great progress has been made, trust service coordination still faces challenging problems such as trustless industry service, poor coordination, and quality of service (QoS) personalized demand. In this paper, we propose a QoS-driven and adaptive trust service coordination method to implement Pareto-efficient allocation of limited industrial service resources in the background of the IIoT. First, we established a Pareto-effective and adaptive industrial IoT trust service coordination model and introduced a blockchain-based adaptive trust evaluation mechanism to achieve trust evaluation of industrial services. Then, taking advantage of a large and complex search space for solution efficiency, we introduced and compared multi-objective gray-wolf algorithms with the particle swarm optimization (PSO) and dragonfly algorithms. The experimental results showed that by judging and blacklisting malicious raters quickly and accurately, our model can efficiently realize self-adaptive, personalized, and intelligent trust service coordination under the given constraints, improving not only the response time, but also the success rate in coordination.<\/jats:p>","DOI":"10.3390\/s18082449","type":"journal-article","created":{"date-parts":[[2018,7,27]],"date-time":"2018-07-27T12:20:03Z","timestamp":1532694003000},"page":"2449","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["QoS-Driven Adaptive Trust Service Coordination in the Industrial Internet of Things"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5846-4674","authenticated-orcid":false,"given":"Jin","family":"Qi","sequence":"first","affiliation":[{"name":"College of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"},{"name":"Jiangsu Engineer Research Center of Communication and Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zian","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"},{"name":"Jiangsu Engineer Research Center of Communication and Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengfei","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zian","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0085-1545","authenticated-orcid":false,"given":"Yanfei","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s11276-015-1133-7","article-title":"A review of industrial wireless networks in the context of Industry 4.0","volume":"23","author":"Li","year":"2017","journal-title":"Wirel. Netw."},{"key":"ref_2","unstructured":"Kracke, A. (2012, January 18\u201321). Overview of the Advanced Manufacturing Partnership. Proceedings of the 23rd Advanced Aerospace Materials and Processes (AeroMat) Conference and Exposition, Charlotte, NC, USA."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, B., and Wu, K. (2017). The Price of Environmental Sustainability: Empirical Evidence from Stock Market Performance in China. Sustainability, 9.","DOI":"10.3390\/su9081452"},{"key":"ref_4","unstructured":"China Electronics Standardization Institute (2017). White Paper on the Industrial Internet of Things, China Electronics Standardization Institute."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2233","DOI":"10.1109\/TII.2014.2300753","article-title":"Internet of Things in Industries: A Survey","volume":"10","author":"Xu","year":"2014","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.comnet.2016.01.009","article-title":"Cloud-assisted Industrial Internet of Things (IIoT)\u2014Enabled framework for health monitoring","volume":"101","author":"Hossain","year":"2016","journal-title":"Comput. Netw."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/MIS.2017.9","article-title":"An Open Semantic Framework for the Industrial Internet of Things","volume":"32","author":"Mayer","year":"2017","journal-title":"IEEE Intell. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/MIE.2017.2649104","article-title":"The Future of Industrial Communication: Automation Networks in the Era of the Internet of Things and Industry 4.0","volume":"11","author":"Wollschlaeger","year":"2017","journal-title":"IEEE Ind. Electron. Mag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1109\/JIOT.2016.2577889","article-title":"Multiobjective Topology Optimization Based on Mapping Matrix and NSGA-II for Switched Industrial Internet of Things","volume":"3","author":"Li","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1109\/TII.2017.2684807","article-title":"Incremental CFS Algorithm for Clustering Large Data in Industrial Internet of Things","volume":"13","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jeschke, S., Brecher, C., Meisen, T., and \u00d6zdemir, D. (2017). Industrial Internet of Things and Cyber Manufacturing Systems, Springer.","DOI":"10.1007\/978-3-319-42559-7"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/MCOM.2016.1600399CM","article-title":"Green industrial Internet of Things architecture: An energy-efficient perspective","volume":"54","author":"Wang","year":"2016","journal-title":"IEEE Commun. Mag."},{"key":"ref_13","first-page":"1","article-title":"Green resource allocation based on deep reinforcement learning in content-centric IoT","volume":"1","author":"He","year":"2018","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1109\/MCOM.2018.1700231","article-title":"QoE-driven big data architecture for smart city","volume":"56","author":"He","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/TSC.2011.21","article-title":"QoS-Driven Service Composition with Reconfigurable Services","volume":"6","author":"Ma","year":"2013","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MWC.2016.7721739","article-title":"Toward trustworthy crowdsourcing in social internet of things","volume":"30","author":"Wang","year":"2016","journal-title":"IEEE Wirel. Commun."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1109\/TPDS.2014.2320748","article-title":"Toward Better Quality of Service Composition Based on a Global Social Service Network","volume":"26","author":"Chen","year":"2015","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1007\/s12652-016-0445-5","article-title":"Knowledge based differential evolution for cloud computing service composition","volume":"9","author":"Qi","year":"2018","journal-title":"J. Ambient Intell. Hum. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.jnca.2015.05.003","article-title":"Manufacturing service composition method based on networked collaboration mode","volume":"59","author":"Xue","year":"2016","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s00170-015-7731-9","article-title":"Task-driven manufacturing cloud service proactive discovery and optimal configuration method","volume":"84","author":"Zhang","year":"2016","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1109\/TPDS.2015.2408613","article-title":"CloudArmor: Supporting Reputation-Based Trust Management for Cloud Services","volume":"27","author":"Noor","year":"2016","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1109\/TSC.2014.2365797","article-title":"Trust Management for SOA-Based IoT and its Application to Service Composition","volume":"9","author":"Chen","year":"2016","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_23","unstructured":"Fu, F., and Fang, L. (2016, January 14\u201317). Blockchain-based trusted computing in social network. Proceedings of the 2016 2nd IEEE International Conference on Computer and Communications (ICCC), Chengdu, China."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Huang, Z., Su, X., Zhang, Y., Shi, C., Zhang, H., and Xie, L. (2017, January 13\u201316). A decentralized solution for IoT data trusted exchange based-on blockchain. Proceedings of the 2017 3rd IEEE International Conference on Computer and Communications (ICCC), Chengdu, China.","DOI":"10.1109\/CompComm.2017.8322729"},{"key":"ref_25","unstructured":"Fitzgerald, J., and Griffin, C. (2017). Pareto Optimal Decision Making in a Distributed Opportunistic Sensing Problem. IEEE Trans. Cybern., 1\u20137."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Abdulgader, M., Lakshminarayanan, S., and Kaur, D. (2017, January 14\u201317). Efficient energy management for smart homes with grey wolf optimizer. Proceedings of the 2017 IEEE International Conference on Electro Information Technology (EIT), Lincoln, NE, USA.","DOI":"10.1109\/EIT.2017.8053392"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Rodriguez, L., Castillo, O., Garcia, M., Soria, J., Valdez, F., and Melin, P. (2017, January 9\u201312). Dynamic simultaneous adaptation of parameters in the grey wolf optimizer using fuzzy logic. Proceedings of the 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Naples, Italy.","DOI":"10.1109\/FUZZ-IEEE.2017.8015523"},{"key":"ref_28","unstructured":"Mustafa, A.S., and Kumaraswamy, Y.S. (2014, January 27\u201329). Data mining algorithms for Web-services classification. Proceedings of the 2014 International Conference on Contemporary Computing and Informatics (IC3I), Mysore, India."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1007\/s00521-015-1920-1","article-title":"Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems","volume":"27","author":"Mirjalili","year":"2016","journal-title":"Neural Comput. Appl."},{"key":"ref_30","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the ICNN\u201995\u2014International Conference on Neural Networks, Perth, WA, Australia."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/8\/2449\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:14:54Z","timestamp":1760195694000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/8\/2449"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,27]]},"references-count":30,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2018,8]]}},"alternative-id":["s18082449"],"URL":"https:\/\/doi.org\/10.3390\/s18082449","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,27]]}}}