{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T18:56:49Z","timestamp":1743101809152,"version":"3.37.3"},"reference-count":46,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,14]],"date-time":"2018-05-14T00:00:00Z","timestamp":1526256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"With the rapid development of cyber-physical systems (CPS), building cyber-physical systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedure of building Cyber-physical systems, it has been found that a large number of functionally equivalent services exist, so it becomes an urgent task to recommend suitable services from the large number of services available in CPS. However, since it is time-consuming, and even impractical, for a single user to invoke all of the services in CPS to experience their QoS, a robust QoS prediction method is needed to predict unknown QoS values. A commonly used method in QoS prediction is collaborative filtering, however, it is hard to deal with the data sparsity and cold start problem, and meanwhile most of the existing methods ignore the data credibility issue. Thence, in order to solve both of these challenging problems, in this paper, we design a framework of QoS prediction for CPS services, and propose a personalized QoS prediction approach based on reputation and location-aware collaborative filtering. Our approach first calculates the reputation of users by using the Dirichlet probability distribution, so as to identify untrusted users and process their unreliable data, and then it digs out the geographic neighborhood in three levels to improve the similarity calculation of users and services. Finally, the data from geographical neighbors of users and services are fused to predict the unknown QoS values. The experiments using real datasets show that our proposed approach outperforms other existing methods in terms of accuracy, efficiency, and robustness.<\/jats:p>","DOI":"10.3390\/s18051556","type":"journal-article","created":{"date-parts":[[2018,5,15]],"date-time":"2018-05-15T07:29:34Z","timestamp":1526369374000},"page":"1556","source":"Crossref","is-referenced-by-count":57,"title":["A Personalized QoS Prediction Approach for CPS Service Recommendation Based on Reputation and Location-Aware Collaborative Filtering"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4975-034X","authenticated-orcid":false,"given":"Li","family":"Kuang","sequence":"first","affiliation":[{"name":"School of Software, Central South University, Changsha 410075, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3327-7455","authenticated-orcid":false,"given":"Long","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Software, Central South University, Changsha 410075, China"}]},{"given":"Lan","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Software, Central South University, Changsha 410075, China"}]},{"given":"Yin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Software, Central South University, Changsha 410075, China"}]},{"given":"Pengju","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Software, Central South University, Changsha 410075, China"}]},{"given":"Chuanbin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Software, Central South University, Changsha 410075, China"}]},{"given":"Yujia","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Software, Central South University, Changsha 410075, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xia, F., Ma, L., Dong, J., and Sun, Y. (2008, January 29\u201331). Network QoS management in cyber-physical systems. Proceedings of the 2008 IEEE International Conference on Embedded Software and Systems Symposia, ICESS Symposia\u201908, Sichuan, China.","DOI":"10.1109\/ICESS.Symposia.2008.84"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dillon, T., Potdar, V., Singh, J., and Talevski, A. (June, January 31). Cyber-physical systems: Providing Quality of Service (QoS) in a heterogeneous systems-of-systems environment. Proceedings of the 2011 5th IEEE International Conference on Digital Ecosystems and Technologies Conference (DEST), Daejeon, Korea.","DOI":"10.1109\/DEST.2011.5936595"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/JAS.2017.7510349","article-title":"Review on cyber-physical systems","volume":"4","author":"Liu","year":"2017","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mohammed, A.W., Xu, Y., Hu, H., and Agyemang, B. (2016). Markov task network: A framework for service composition under uncertainty in cyber-physical systems. Sensors, 16.","DOI":"10.3390\/s16091542"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2899006","article-title":"Research directions for cyber physical systems in wireless and mobile healthcare","volume":"1","author":"Stankovic","year":"2017","journal-title":"ACM Trans. Cyber Phys. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yin, Y., Yu, F., Xu, Y., Yu, L., and Mu, J. (2017). Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems. Sensors, 17.","DOI":"10.3390\/s17092059"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3593","DOI":"10.3233\/IFS-162104","article-title":"Web service QoS prediction by neighbor information combined non-negative matrix factorization","volume":"30","author":"Su","year":"2016","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.knosys.2013.03.012","article-title":"Recommender systems survey","volume":"46","author":"Bobadilla","year":"2013","journal-title":"Knowl. Based Syst."},{"key":"ref_9","unstructured":"Tang, M., Jiang, Y., Liu, J., and Liu, X. (2012, January 24\u201329). Location-aware collaborative filtering for QoS-based service recommendation. Proceedings of the 2012 19th IEEE International Conference on Web Services (ICWS), Honolulu, HI, USA."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1109\/TSMCA.2012.2210409","article-title":"Predicting quality of service for selection by neighborhood-based collaborative filtering","volume":"43","author":"Wu","year":"2013","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Shao, L., Zhang, J., Wei, Y., Zhao, J., Xie, B., and Mei, H. (2007, January 9\u201313). Personalized qos prediction forweb services via collaborative filtering. Proceedings of the 2007 IEEE International Conference on Web Services, ICWS 2007, Salt Lake City, UT, USA.","DOI":"10.1109\/ICWS.2007.140"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s11280-012-0186-0","article-title":"QoS-aware service selection via collaborative QoS evaluation","volume":"17","author":"Yu","year":"2014","journal-title":"World Wide Web"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10462-012-9364-9","article-title":"Shilling attacks against recommender systems: A comprehensive survey","volume":"42","author":"Gunes","year":"2014","journal-title":"Artif. Intell. Rev."},{"key":"ref_14","unstructured":"Qiu, W., Zheng, Z., Wang, X., Yang, X., and Lyu, M.R. (July, January 28). Reputation-Aware QoS Value Prediction of Web Services. Proceedings of the IEEE International Conference on Services Computing, Santa Clara, CA, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/TR.2015.2464075","article-title":"Web Service Personalized Quality of Service Prediction via Reputation-Based Matrix Factorization","volume":"65","author":"Xu","year":"2016","journal-title":"IEEE Trans. Reliab."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wu, C., Qiu, W., Zheng, Z., Wang, X., and Yang, X. (July, January 27). Qos prediction of web services based on two-phase k-means clustering. Proceedings of the 2015 IEEE International Conference on Web Services (ICWS), New York, NY, USA.","DOI":"10.1109\/ICWS.2015.31"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.knosys.2016.09.033","article-title":"TAP: A personalized trust-aware QoS prediction approach for web service recommendation","volume":"115","author":"Su","year":"2017","journal-title":"Knowl. Based Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.future.2016.09.022","article-title":"Exploiting Web service geographical neighborhood for collaborative QoS prediction","volume":"68","author":"Chen","year":"2017","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xu, Y., Yin, J., Lo, W., and Wu, Z. (2013). Personalized Location-Aware QoS Prediction for Web Services Using Probabilistic Matrix Factorization. Web Information Systems Engineering, Springer.","DOI":"10.1007\/978-3-642-41230-1_20"},{"key":"ref_20","unstructured":"He, P., Zhu, J., Zheng, Z., Xu, J., and Lyu, M.R. (July, January 27). Location-Based Hierarchical Matrix Factorization for Web Service Recommendation. Proceedings of the IEEE International Conference on Web Services, Anchorage, AK, USA."},{"key":"ref_21","unstructured":"Luo, X., Zhou, M., Xia, Y., and Zhu, Q. (2014, January 9\u201310). Predicting web service QoS via matrix-factorization-based collaborative filtering under non-negativity constraint. Proceedings of the Wireless and Optical Communication Conference (WOCC), Newark, NJ, USA."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zheng, Z., and Lyu, M.R. (December, January 29). WSPred: A time-aware personalized QoS prediction framework for Web services. Proceedings of the 2011 22nd IEEE International Symposium on Software Reliability Engineering (ISSRE), Hiroshima, Japan.","DOI":"10.1109\/ISSRE.2011.17"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Xie, Q., Zhao, S., Zheng, Z., Zhu, J., and Lyu, M.R. (July, January 27). Asymmetric correlation regularized matrix factorization for web service recommendation. In Proceeding of the 2016 IEEE International Conference on Web Services (ICWS), San Francisco, CA, USA.","DOI":"10.1109\/ICWS.2016.34"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2065","DOI":"10.1016\/j.eswa.2013.09.005","article-title":"Facing the cold start problem in recommender systems","volume":"41","author":"Lika","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ardagna, D., and Pernici, B. (2007). Adaptive service composition in flexible processes. IEEE Trans. Softw. Eng., 33.","DOI":"10.1109\/TSE.2007.1011"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"22706","DOI":"10.3390\/s141222706","article-title":"Analyzing comprehensive QoS with security constraints for services composition applications in wireless sensor networks","volume":"14","author":"Xiong","year":"2014","journal-title":"Sensors"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1145\/1232722.1232728","article-title":"Efficient algorithms for Web services selection with end-to-end QoS constraints","volume":"1","author":"Yu","year":"2007","journal-title":"ACM Trans. Web (TWEB)"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Huang, Y., Huang, J., Cheng, B., He, S., and Chen, J. (2017). Time-Aware Service Ranking Prediction in the Internet of Things Environment. Sensors, 17.","DOI":"10.3390\/s17050974"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Abdullah, A., and Li, X. (July, January 27). An integrated-model qos-based graph for web service recommendation. Proceedings of the 2015 IEEE International Conference on Web Services (ICWS), New York, NY, USA.","DOI":"10.1109\/ICWS.2015.62"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.knosys.2014.10.002","article-title":"Service organization and recommendation using multi-granularity approach","volume":"73","author":"Liu","year":"2015","journal-title":"Knowl. Based Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hu, Y., Peng, Q., Hu, X., and Yang, R. (July, January 27). Web service recommendation based on time series forecasting and collaborative filtering. Proceedings of the 2015 IEEE International Conference on Web Services (ICWS), New York, NY, USA.","DOI":"10.1109\/ICWS.2015.40"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1016\/j.sigpro.2015.01.013","article-title":"Multimedia services quality prediction based on the association mining between context and QoS properties","volume":"120","author":"Kuang","year":"2016","journal-title":"Signal Process."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Ma, H., Lyu, M.R., and King, I. (2009, January 6\u201310). WSRec: A Collaborative Filtering Based Web Service Recommender System. Proceedings of the IEEE International Conference on Web Services, Los Angeles, CA, USA.","DOI":"10.1109\/ICWS.2009.30"},{"key":"ref_34","unstructured":"Chen, M., and Ma, Y. (2015). A Hybrid Approach to Web Service Recommendation Based on QoS-Aware Rating and Ranking. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chen, M., Ma, Y., Hu, B., and Zhang, L.J. (July, January 27). A ranking-oriented hybrid approach to qos-aware web service recommendation. Proceedings of the 2015 IEEE International Conference on Services Computing (SCC), New York, NY, USA.","DOI":"10.1109\/SCC.2015.84"},{"key":"ref_36","unstructured":"Chen, F., Yuan, S., and Mu, B. (July, January 27). User-QoS-Based Web Service Clustering for QoS Prediction. Proceedings of the IEEE International Conference on Web Services, New York, NY, USA."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lo, W., Yin, J., Deng, S., Li, Y., and Wu, Z. (2012, January 24\u201329). An extended matrix factorization approach for qos prediction in service selection. Proceedings of the 2012 IEEE Ninth International Conference on Services Computing (SCC), Honolulu, HI, USA.","DOI":"10.1109\/SCC.2012.36"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1016\/j.ins.2014.09.042","article-title":"Automated intelligent system for sound signalling device quality assurance","volume":"294","author":"Maniak","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1109\/TSC.2015.2407877","article-title":"A highly accurate prediction algorithm for unknown web service QoS values","volume":"9","author":"Ma","year":"2015","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1504\/IJWGS.2015.067156","article-title":"Personalised QoS\u2013based web service recommendation with service neighbourhood\u2013enhanced matrix factorisation","volume":"11","author":"Yin","year":"2015","journal-title":"Int. J. Web Grid Serv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, G., and Zhang, G. (2007, January 15\u201317). Agent selection and P2P overlay construction using global locality knowledge. Proceedings of the IEEE International Conference on Networking, Sensing and Control (ICNSC 07), London, UK.","DOI":"10.1109\/ICNSC.2007.372832"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/TSC.2012.34","article-title":"Investigating QoS of real-world web services","volume":"7","author":"Zheng","year":"2014","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Zhang, Y., and Lyu, M.R. (2010, January 5\u201310). Distributed qos evaluation for real-world web services. Proceedings of the 2010 IEEE International Conference on Web Services (ICWS), Miami, FL, USA.","DOI":"10.1109\/ICWS.2010.10"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zheng, Z., and Lyu, M.R. (2011, January 4\u20137). Exploring latent features for memory-based QoS prediction in cloud computing. Proceedings of the 2011 30th IEEE Symposium on Reliable Distributed Systems (SRDS), Madrid, Spain.","DOI":"10.1109\/SRDS.2011.10"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J. (1994, January 22\u201326). GroupLens: An open architecture for collaborative filtering of netnews. Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, Chapel Hill, NC, USA.","DOI":"10.1145\/192844.192905"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. (2001, January 1\u20135). Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th ACM International Conference on World Wide Web, Hong Kong, China.","DOI":"10.1145\/371920.372071"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/5\/1556\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T21:35:13Z","timestamp":1718055313000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/5\/1556"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,14]]},"references-count":46,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2018,5]]}},"alternative-id":["s18051556"],"URL":"https:\/\/doi.org\/10.3390\/s18051556","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2018,5,14]]}}}