{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T02:16:12Z","timestamp":1771467372842,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,3,18]],"date-time":"2022-03-18T00:00:00Z","timestamp":1647561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Development program of ETU &quot;LETI&quot; within the framework of the program of strategic academic leadership&quot; Priority-2030&quot;.","award":["No.075-15-2021-1318 on 29 sept 2021"],"award-info":[{"award-number":["No.075-15-2021-1318 on 29 sept 2021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Increase in technologies around the world requires adding intelligence to the objects, and making it a smart object in an environment leads to the Social Internet of Things (SIoT). These social objects are uniquely identifiable, transferable and share information from user-to-objects and objects-to objects through interactions in a smart environment such as smart homes, smart cities and many more applications. SIoT faces certain challenges such as handling of heterogeneous objects, selection of generated data in objects, missing values in data. Therefore, the discovery and communication of meaningful patterns in data are more important for every application. Thus, the analysis of data is essential in smarter decisions and qualifies performance of data for various applications. In a smart environment, social networks of intelligent objects are increasing services and decreasing the relationship in a reliable and efficient way of sharing resources and services. Hence, this work proposed the feature selection method based on proposed semantic rules and established the relationships to classify the services using relationship artificial neural networks (R-ANN). R-ANN is an inversely proportional relationship to the objects based on certain rules and conditions between the objects to objects and users to objects. It provides the service oriented knowledge model to make decisions in the proposed R-ANN model that produces service to the users. The proposed R-ANN provides an accuracy of 89.62% for various services namely weather, air quality, parking, light status, and people presence respectively in the SIoT environment compared to the existing model.<\/jats:p>","DOI":"10.3390\/bdcc6010032","type":"journal-article","created":{"date-parts":[[2022,3,20]],"date-time":"2022-03-20T21:25:00Z","timestamp":1647811500000},"page":"32","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Service Oriented R-ANN Knowledge Model for Social Internet of Things"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2286-7695","authenticated-orcid":false,"given":"Mohana","family":"S. D.","sequence":"first","affiliation":[{"name":"Department of Information Science and Engineering, JSS Science and Technology University, Mysuru 570006, Karnataka, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3490-6292","authenticated-orcid":false,"given":"S. P. Shiva","family":"Prakash","sequence":"additional","affiliation":[{"name":"Department of Information Science and Engineering, JSS Science and Technology University, Mysuru 570006, Karnataka, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5949-7830","authenticated-orcid":false,"given":"Kirill","family":"Krinkin","sequence":"additional","affiliation":[{"name":"Department of Software Engineering and Computer Applications, Saint Petersburg Electrotechnical University \u201cLETI\u201d, Saint Petersburg 197022, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tsai, S.C., and Chen, C.H. (2022). Exploring the Innovation Diffusion of Big Data Robo-Advisor. Appl. Syst. Innov., 5.","DOI":"10.3390\/asi5010015"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hussain, M., and Beg, M.M. (2019). Fog computing for internet of things (IoT)-aided smart grid architectures. Big Data Cogn. Comput., 3.","DOI":"10.3390\/bdcc3010008"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Serrano, W. (2019). Intelligent recommender system for big data applications based on the random neural network. Big Data Cogn. Comput., 3.","DOI":"10.3390\/bdcc3010015"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Manheim, D. (2019). Multiparty dynamics and failure modes for machine learning and artificial intelligence. Big Data Cogn. Comput., 3.","DOI":"10.3390\/bdcc3020021"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Waseem, M., Lin, Z., and Yang, L. (2019). Data-driven load forecasting of air conditioners for demand response using levenberg\u2013marquardt algorithm-based ANN. Big Data Cogn. Comput., 3.","DOI":"10.3390\/bdcc3030036"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Stella, M., and Kenett, Y.N. (2019). Viability in multiplex lexical networks and machine learning characterizes human creativity. Big Data Cogn. Comput., 3.","DOI":"10.3390\/bdcc3030045"},{"key":"ref_7","unstructured":"Ganzfried, S. (2017). Optimal number of choices in rating contexts. Imperfect Decision Makers: Admitting Real-World Rationality, PMLR: Centre de Convencions Internacional de Barcelona. Available online: http:\/\/proceedings.mlr.press\/v58\/ganzfried17a.html."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"How, M.L., Cheah, S.M., Khor, A.C., and Chan, Y.J. (2020). Artificial intelligence-enhanced predictive insights for advancing financial inclusion: A human-centric ai-thinking approach. Big Data Cogn. Comput., 4.","DOI":"10.3390\/bdcc4020008"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"107248","DOI":"10.1016\/j.comnet.2020.107248","article-title":"How to exploit the Social Internet of Things: Query Generation Model and Device Profiles\u2019 Dataset","volume":"174","author":"Marche","year":"2020","journal-title":"Comput. Netw."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.gltp.2021.08.032","article-title":"Performance comparison of machine learning algorithms for data aggregation in social internet of things","volume":"2","author":"Meghana","year":"2021","journal-title":"Glob. Transit. Proc."},{"key":"ref_11","first-page":"68","article-title":"A Survey on Recommender Systems for Internet of Things: Techniques, Applications and Future Directions","volume":"35","author":"Altulyan","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Asaithambi, S.P.R., Venkatraman, S., and Venkatraman, R. (2021). Big data and personalisation for non-intrusive smart home automation. Big Data Cogn. Comput., 5.","DOI":"10.3390\/bdcc5010006"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gandomi, A.H., Chen, F., and Abualigah, L. (2022). Machine Learning Technologies for Big Data Analytics. Electronics, 11.","DOI":"10.3390\/electronics11030421"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Guo, K., Lu, Y., Gao, H., and Cao, R. (2018). Artificial intelligence-based semantic internet of things in a user-centric smart city. Sensors, 18.","DOI":"10.3390\/s18051341"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kong, Y., Ma, X., and Wen, C. (2022). A New Method of Deep Convolutional Neural Network Image Classification Based on Knowledge Transfer in Small Label Sample Environment. Sensors, 22.","DOI":"10.3390\/s22030898"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Marinov, M.B., Nikolov, N., Dimitrov, S., Todorov, T., Stoyanova, Y., and Nikolov, G.T. (2022). Linear Interval Approximation for Smart Sensors and IoT Devices. Sensors, 22.","DOI":"10.3390\/s22030949"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Pavi\u0107evi\u0107, M., and Popovi\u0107, T. (2022). Forecasting Day-Ahead Electricity Metrics with Artificial Neural Networks. Sensors, 22.","DOI":"10.3390\/s22031051"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chen, Y.H., Chen, S.W., Chang, P.J., Hua, H.T., Lin, S.Y., and Chen, R.S. (2022). A VLSI Chip for the Abnormal Heart Beat Detection Using Convolutional Neural Network. Sensors, 22.","DOI":"10.3390\/s22030796"},{"key":"ref_19","first-page":"121","article-title":"A Hybrid Deep Learning Model for Predicting Stock Market Trend Prediction","volume":"32","author":"Cheng","year":"2021","journal-title":"Int. J. Inf. Manag. Sci."},{"key":"ref_20","unstructured":"Wang, L., and Sng, D. (2015). Deep learning algorithms with applications to video analytics for a smart city: A survey. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.inffus.2019.05.004","article-title":"A survey of data fusion in smart city applications","volume":"52","author":"Lau","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lin, J., Chen, W.M., Lin, Y., Cohn, J., Gan, C., and Han, S. (2020). Mcunet: Tiny deep learning on iot devices. arXiv.","DOI":"10.1109\/IPCCC50635.2020.9391558"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Le, L.T., Nguyen, H., Dou, J., and Zhou, J. (2019). A comparative study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in estimating the heating load of buildings\u2019 energy efficiency for smart city planning. Appl. Sci., 9.","DOI":"10.3390\/app9132630"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1109\/JIOT.2017.2712560","article-title":"Semisupervised deep reinforcement learning in support of IoT and smart city services","volume":"5","author":"Mohammadi","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/MSSC.2017.2745818","article-title":"Embedded deep neural network processing: Algorithmic and processor techniques bring deep learning to iot and edge devices","volume":"9","author":"Verhelst","year":"2017","journal-title":"IEEE Solid-State Circuits Mag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"30315","DOI":"10.1007\/s11042-018-6919-z","article-title":"Multimedia-oriented action recognition in Smart City-based IoT using multilayer perceptron","volume":"78","author":"Zamil","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_27","unstructured":"Drewil, G.I., and Al-Bahadili, R.J. (2021). Forecast Air Pollution in Smart City Using Deep Learning Techniques: A Review. Multicult. Educ., 7."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sotiriadis, S., Bessis, N., Asimakopoulou, E., and Mustafee, N. (2014, January 13\u201316). Towards simulating the internet of things. Proceedings of the 2014 28th International Conference on Advanced Information Networking and Applications Workshops, Victoria, BC, Canada.","DOI":"10.1109\/WAINA.2014.74"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.sysarc.2016.06.008","article-title":"IOTSim: A simulator for analysing IoT applications","volume":"72","author":"Zeng","year":"2017","journal-title":"J. Syst. Archit."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Osterlind, F., Dunkels, A., Eriksson, J., Finne, N., and Voigt, T. (2006, January 14\u201316). Cross-level sensor network simulation with cooja. Proceedings of the 2006 31st IEEE Conference on Local Computer Networks, Tampa, FL, USA.","DOI":"10.1109\/LCN.2006.322172"},{"key":"ref_31","first-page":"527","article-title":"Network simulations with the ns-3 simulator","volume":"14","author":"Henderson","year":"2008","journal-title":"SIGCOMM Demonstr."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1002\/spe.2509","article-title":"iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments","volume":"47","author":"Gupta","year":"2017","journal-title":"Softw. Pract. Exp."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Han, S.N., Lee, G.M., Crespi, N., Heo, K., Van Luong, N., Brut, M., and Gatellier, P. (2014, January 6\u20138). DPWSim: A simulation toolkit for IoT applications using devices profile for web services. Proceedings of the 2014 IEEE World Forum on Internet of Things (WF-IoT), Seoul, Korea.","DOI":"10.1109\/WF-IoT.2014.6803226"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Defiebre, D., Germanakos, P., and Sacharidis, D. (2020, January 14\u201317). DANOS: A Human-Centered Decentralized Simulator in SIOT. Proceedings of the Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, Genoa, Italy.","DOI":"10.1145\/3386392.3399292"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kasnesis, P., Toumanidis, L., Kogias, D., Patrikakis, C.Z., and Venieris, I.S. (2016, January 12\u201314). Assist: An agent-based siot simulator. Proceedings of the 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), Reston, VA, USA.","DOI":"10.1109\/WF-IoT.2016.7845409"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Jindal, A., Aujla, G.S., Kumar, N., Prodan, R., and Obaidat, M.S. (2018, January 9\u201313). DRUMS: Demand response management in a smart city using deep learning and SVR. Proceedings of the 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/GLOCOM.2018.8647926"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chen, J.F., Chen, W.L., Huang, C.P., Huang, S.H., and Chen, A.P. (2016, January 16\u201318). Financial time-series data analysis using deep convolutional neural networks. Proceedings of the 2016 7th International Conference on Cloud Computing and Big Data (CCBD), Macau, China.","DOI":"10.1109\/CCBD.2016.027"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/TMBMC.2017.2655025","article-title":"Inscribed matter communication: Part I","volume":"2","author":"Rose","year":"2016","journal-title":"IEEE Trans. Mol. Biol. Multi-Scale Commun."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1016\/j.measurement.2019.05.039","article-title":"A bearing data analysis based on kurtogram and deep learning sequence models","volume":"145","author":"Udmale","year":"2019","journal-title":"Measurement"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"100076","DOI":"10.1016\/j.iot.2019.100076","article-title":"A service oriented IoT architecture for disaster preparedness and forecasting system","volume":"14","author":"Pillai","year":"2021","journal-title":"Internet Things"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Akhter, R., and Sofi, S.A. (2021). Precision agriculture using IoT data analytics and machine learning. J. King Saud Univ.-Comput. Inf. Sci., in press.","DOI":"10.1016\/j.jksuci.2021.05.013"},{"key":"ref_42","unstructured":"Bhuiyan, R. (2021, May 30). Examination of Air Pollutant Concentrations in Smart City Helsinki Using Data Exploration and Deep Learning Methods. Available online: https:\/\/urn.fi\/URN:NBN:fi:amk-2021060113276."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Alrahhal, H., Jamous, R., Ramadan, R., Alayba, A.M., and Yadav, K. (2022). Utilising Acknowledge for the Trust in Wireless Sensor Networks. Appl. Sci., 12.","DOI":"10.3390\/app12042045"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Al-Otaiby, N., Alhindi, A., and Kurdi, H. (2022). AntTrust: An Ant-Inspired Trust Management System for Peer-to-Peer Networks. Sensors, 22.","DOI":"10.3390\/s22020533"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ghoneim, O.A., and Manjunatha, B.R. (2017, January 13\u201316). Forecasting of ozone concentration in smart city using deep learning. Proceedings of the 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India.","DOI":"10.1109\/ICACCI.2017.8126024"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.procs.2016.09.068","article-title":"Analysis of eight data mining algorithms for smarter Internet of Things (IoT)","volume":"98","author":"Alam","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"103358","DOI":"10.1016\/j.micpro.2020.103358","article-title":"Object Recommendation based Friendship Selection (ORFS) for navigating smarter social objects in SIoT","volume":"80","author":"Rajendran","year":"2021","journal-title":"Microprocess. Microsyst."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"103083","DOI":"10.1016\/j.scs.2021.103083","article-title":"A Secure, Private, and Explainable IoHT Framework to Support Sustainable Health Monitoring in a Smart City","volume":"72","author":"Rahman","year":"2021","journal-title":"Sustain. Cities Soc."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/6\/1\/32\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:38:59Z","timestamp":1760135939000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/6\/1\/32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,18]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["bdcc6010032"],"URL":"https:\/\/doi.org\/10.3390\/bdcc6010032","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,18]]}}}