{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T13:19:49Z","timestamp":1769519989000,"version":"3.49.0"},"reference-count":49,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T00:00:00Z","timestamp":1588550400000},"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":"<jats:p>The purpose of this paper is to improve the accuracy of solving prediction tasks of the missing IoT data recovery. To achieve this, the authors have developed a new ensemble of neural network tools. It consists of two successive General Regression Neural Network (GRNN) networks and one neural-like structure of the Successive Geometric Transformation Model (SGTM). The principle of ensemble topology construction on two successively connected general regression neural networks, supplemented with an SGTM neural-like structure, is mathematically substantiated, which improves the accuracy of prediction results. The effectiveness of the method is based on the replacement of the summation of the results of the two GRNNs with a weighted summation, which improves the accuracy of the ensemble operation in general. A detailed algorithmic implementation of the ensemble method as well as a flowchart of its operation is presented. The parameters of the ensemble operation are determined by optimization using the brute-force method. Based on the developed ensemble method, the solution of the task of completing the partially missing values in the real monitoring dataset of the air environment collected by the IoT device is presented. By comparing the performance of the developed ensemble with the existing methods, the highest accuracy of its performance (by the parameters of Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) accuracy) among the most similar in this class has been proved.<\/jats:p>","DOI":"10.3390\/s20092625","type":"journal-article","created":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T14:00:43Z","timestamp":1588600843000},"page":"2625","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":89,"title":["An Approach towards Increasing Prediction Accuracy for the Recovery of Missing IoT Data based on the GRNN-SGTM Ensemble"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9802-6799","authenticated-orcid":false,"given":"Roman","family":"Tkachenko","sequence":"first","affiliation":[{"name":"Department of Publishing Information Technologies, Lviv Polytechnic National University, 12 Bandera str., 79000 Lviv, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9761-0096","authenticated-orcid":false,"given":"Ivan","family":"Izonin","sequence":"additional","affiliation":[{"name":"Department of Publishing Information Technologies, Lviv Polytechnic National University, 12 Bandera str., 79000 Lviv, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3678-9229","authenticated-orcid":false,"given":"Natalia","family":"Kryvinska","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Faculty of Management, Comenius University in Bratislava, 82005 Bratislava 25, Slovakia"},{"name":"Department of e-Business, School of Business, Economics and Statistics, University of Vienna, A-1090 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1667-2584","authenticated-orcid":false,"given":"Ivanna","family":"Dronyuk","sequence":"additional","affiliation":[{"name":"Department of Automated Control Systems, Lviv Polytechnic National University, 12 Bandera str., 79000 Lviv, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6476-7305","authenticated-orcid":false,"given":"Khrystyna","family":"Zub","sequence":"additional","affiliation":[{"name":"Center of Information Support, Lviv Polytechnic National University, 12 Bandera str., 79000 Lviv, Ukraine"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,4]]},"reference":[{"key":"ref_1","unstructured":"Barolli, L., Xhafa, F., Khan, Z.A., and Odhabi, H. IoT Device Selection in Opportunistic Networks: A Fuzzy Approach Considering IoT Device Failure Rate. Proceedings of the Advances in Internet, Data and Web Technologies."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5087","DOI":"10.1002\/rnc.4306","article-title":"A game theory approach for cooperative control to improve data quality and false data detection in WSN","volume":"28","author":"Corchado","year":"2018","journal-title":"Int. J. Robust Nonlinear Control"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mary, I.P.S., and Arockiam, L. (2017, January 2\u20133). Imputing the missing data in IoT based on the spatial and temporal correlation. Proceedings of the 2017 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), Bangalore, India.","DOI":"10.1109\/ICCTAC.2017.8249990"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yan, X., Xiong, W., Hu, L., Wang, F., and Zhao, K. (2020, March 21). Missing Value Imputation Based on Gaussian Mixture Model for the Internet of Things. Available online: https:\/\/www.hindawi.com\/journals\/mpe\/2015\/548605\/.","DOI":"10.1155\/2015\/548605"},{"key":"ref_5","unstructured":"Sangaiah, A.K., Sheng, M., and Zhang, Z. (2018). Chapter 6\u2014Aspect Oriented Modeling of Missing Data Imputation for Internet of Things (IoT) Based Healthcare Infrastructure. Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications, Academic Press. Intelligent Data-Centric Systems."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mary, I.P.S. (2017). Imputing the missing values in IoT using ESTCP model. Int. J. Adv. Res. Comput. Sci., 8.","DOI":"10.26483\/ijarcs.v8i9.5145"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.future.2019.02.015","article-title":"Missing data resilient decision-making for healthcare IoT through personalization: A case study on maternal health","volume":"96","author":"Azimi","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_8","unstructured":"(2017). IoT Analytics Challenges\u2014Analytics for the Internet of Things (IoT), Packt Publishing Ltd."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lujic, I., Maio, V.D., and Brandic, I. (2018, January 1\u20133). Adaptive Recovery of Incomplete Datasets for Edge Analytics. Proceedings of the 2018 IEEE 2nd International Conference on Fog and Edge Computing (ICFEC), Washington, DC, USA.","DOI":"10.1109\/CFEC.2018.8358726"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1587\/transinf.2018EDP7257","article-title":"Missing-Value Imputation of Continuous Missing Based on Deep Imputation Network Using Correlations among Multiple IoT Data Streams in a Smart Space","volume":"102","author":"Lee","year":"2019","journal-title":"Ieice Trans. Inf. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ding, Z., Mei, G., Cuomo, S., Li, Y., and Xu, N. (2018). Comparison of Estimating Missing Values in IoT Time Series Data Using Different Interpolation Algorithms. Int. J. Parallel. Prog., 1\u201315.","DOI":"10.1007\/s10766-018-0595-5"},{"key":"ref_12","first-page":"10419","article-title":"Data Recovery by Fountain Codes in IoT Networks","volume":"13","author":"Aishwarya","year":"2018","journal-title":"Int. J. Appl. Eng. Res."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Marcelis, P.J., Rao, V.S., and Prasad, R.V. (2017, January 18\u201321). DaRe: Data Recovery through Application Layer Coding for LoRaWAN. Proceedings of the 2017 IEEE\/ACM Second International Conference on Internet-of-Things Design and Implementation (IoTDI), Pittsburgh, PA, USA.","DOI":"10.1145\/3054977.3054978"},{"key":"ref_14","unstructured":"Zhou, J., and Huang, Z. (2020, March 31). Recover Missing Sensor Data with Iterative Imputing Network. Available online: https:\/\/www.semanticscholar.org\/paper\/Recover-Missing-Sensor-Data-with-Iterative-Imputing-Zhou-Huang\/59813bfb77cda27c2c510c2d5b3bbf23f105a293."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"15582","DOI":"10.3390\/s131115582","article-title":"Data Management for the Internet of Things: Design Primitives and Solution","volume":"13","author":"Hayajneh","year":"2013","journal-title":"Sensors"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Guzel, M., Kok, I., Akay, D., and Ozdemir, S. (2020). ANFIS and Deep Learning based missing sensor data prediction in IoT. Concurr. Comput. Pract. Exp., 32.","DOI":"10.1002\/cpe.5400"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Babichev, S. (2018). An Evaluation of the Information Technology of Gene Expression Profiles Processing Stability for Different Levels of Noise Components. Data, 3.","DOI":"10.3390\/data3040048"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.engappai.2019.09.002","article-title":"Data-driven approach augmented in simulation for robust fault prognosis","volume":"86","author":"Djeziri","year":"2019","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_19","unstructured":"Hu, Z., Petoukhov, S.V., and He, M. (2020). Method of the Data Adequacy Determination of Personal Medical Profiles. Proceedings of the Advances in Intelligent Systems and Computing II, Springer International Publishing."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Szewczyk, R., Zieli\u0144ski, C., and Kaliczy\u0144ska, M. (2017). Methods of Determining Information Support of Web Community User Personal Data Verification System. Proceedings of the Automation 2017, Springer International Publishing.","DOI":"10.1007\/978-3-319-54042-9"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.jss.2018.06.034","article-title":"Missing Data in Surgical Data Sets: A Review of Pertinent Issues and Solutions","volume":"232","author":"Sharath","year":"2018","journal-title":"J. Surg. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"103379","DOI":"10.1016\/j.jbi.2020.103379","article-title":"Multiple predictively equivalent risk models for handling missing data at time of prediction: With an application in severe hypoglycemia risk prediction for type 2 diabetes","volume":"103","author":"Ma","year":"2020","journal-title":"J. Biomed. Inform."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Beretta, L., and Santaniello, A. (2016). Nearest neighbor imputation algorithms: A critical evaluation. BMC Med. Inform. Decis. Mak.","DOI":"10.1186\/s12911-016-0318-z"},{"key":"ref_24","unstructured":"Jonsson, P., and Wohlin, C. (2004, January 11\u201317). An evaluation of k-nearest neighbour imputation using Likert data. Proceedings of the 10th International Symposium on Software Metrics, Chicago, IL, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1080\/08839514.2019.1637138","article-title":"Comparison of Performance of Data Imputation Methods for Numeric Dataset","volume":"33","author":"Jadhav","year":"2019","journal-title":"Appl. Artif. Intell."},{"key":"ref_26","first-page":"153","article-title":"NS-kNN: A modified k-nearest neighbors approach for imputing metabolomics data","volume":"14","author":"Lee","year":"2018","journal-title":"Metab. Off. J. Metab. Soc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3375","DOI":"10.35940\/ijrte.C5024.098319","article-title":"Imputing the Missing Values in IoT using FRBIM","volume":"8","author":"Mary","year":"2019","journal-title":"IJRTE"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lai, X., Liu, X., Zhang, L., Lin, C., Obaidat, M.S., and Hsiao, K.-F. (2019, January 20\u201324). Missing Value Imputations by Rule-Based Incomplete Data Fuzzy Modeling. Proceedings of the ICC 2019\u20142019 IEEE International Conference on Communications (ICC), Shanghai, China.","DOI":"10.1109\/ICC.2019.8761052"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1007\/s00500-011-0774-4","article-title":"Missing data imputation for fuzzy rule-based classification systems","volume":"16","author":"Luengo","year":"2012","journal-title":"Soft Comput."},{"key":"ref_30","unstructured":"Hu, Z., Petoukhov, S., Dychka, I., and He, M. (2020). Missing Data Imputation Through SGTM Neural-Like Structure for Environmental Monitoring Tasks. Proceedings of the Advances in Computer Science for Engineering and Education II, Springer International Publishing."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1007\/978-3-319-91008-6_58","article-title":"Model and Principles for the Implementation of Neural-Like Structures Based on Geometric Data Transformations","volume":"Volume 754","author":"Hu","year":"2019","journal-title":"Advances in Computer Science for Engineering and Education"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1016\/j.procs.2019.11.054","article-title":"Recovery of Incomplete IoT Sensed Data using High-Performance Extended-Input Neural-Like Structure","volume":"160","author":"Izonin","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1109\/TSMC.1971.4308320","article-title":"Polynomial Theory of Complex Systems","volume":"SMC-1","author":"Ivakhnenko","year":"1971","journal-title":"IEEE Trans. Syst. Manand Cybern."},{"key":"ref_34","unstructured":"Barolli, L., Nishino, H., and Miwa, H. (2020). GRNN Approach Towards Missing Data Recovery Between IoT Systems. Proceedings of the Advances in Intelligent Networking and Collaborative Systems, Springer International Publishing."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.knosys.2016.11.003","article-title":"A globally enhanced general regression neural network for on-line multiple emissions prediction of utility boiler","volume":"118","author":"Song","year":"2017","journal-title":"Knowl. Based Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1016\/j.procs.2019.11.044","article-title":"An Extended-Input GRNN and its Application","volume":"160","author":"Izonin","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1007\/s13202-015-0196-4","article-title":"A general regression neural network model offers reliable prediction of CO2 minimum miscibility pressure","volume":"6","author":"Alomair","year":"2016","journal-title":"J. Pet. Explor. Prod. Technol."},{"key":"ref_38","unstructured":"Vagelis, P. (2012). Structural Seismic Design Optimization and Earthquake Engineering: Formulations and Applications: Formulations and Applications, IGI Global."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Huang, D.-S., and Irwin, G.W. (2006). Intelligent Computing in Signal Processing and Pattern Recognition: International Conference on Intelligent Computing, ICIC 2006, Kunming, China, August, 2006, Springer.","DOI":"10.1007\/978-3-540-37258-5"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"55","DOI":"10.3103\/S0146411617010023","article-title":"On-line kernel clustering based on the general regression neural network and T. Kohonen\u2019s self-organizing map","volume":"51","author":"Bodyanskiy","year":"2017","journal-title":"Aut. Control Comp. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Huang, T., Lv, J., Sun, C., and Tuzikov, A.V. (2018). Online GRNN-Based Ensembles for Regression on Evolving Data Streams. Proceedings of the Advances in Neural Networks\u2014ISNN 2018, Springer International Publishing.","DOI":"10.1007\/978-3-319-92537-0"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhou, J., Peng, T., Zhang, C., and Sun, N. (2018). Data Pre-Analysis and Ensemble of Various Artificial Neural Networks for Monthly Streamflow Forecasting. Water, 10.","DOI":"10.3390\/w10050628"},{"key":"ref_43","first-page":"120","article-title":"\u0410\u043d\u0441\u0430\u043c\u0431\u043b\u044c \u043c\u0435\u0440\u0435\u0436 GRNN \u0434\u043b\u044f \u0440o\u0437\u0432\u2019\u044f\u0437\u0430\u043d\u043d\u044f \u0437\u0430\u0434\u0430\u0447 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u0437 \u043f\u0456\u0434\u0432\u0438\u0449\u0435\u043do\u044e \u0442o\u0447\u043d\u0456\u0441\u0442\u044e","volume":"29","author":"Vitynskiy","year":"2019","journal-title":"\u041d\u0430\u0443\u043ao\u0432\u0438\u0439 \u0432\u0456\u0441\u043d\u0438\u043a \u041d\u041b\u0422\u0423 \u0423\u043a\u0440\u0430\u0457\u043d\u0438"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1109\/72.97934","article-title":"A general regression neural network","volume":"2","author":"Specht","year":"1991","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Dronyuk, I., Fedevych, O., and Poplavska, Z. (2017, January 21\u201325). The generalized shift operator and non-harmonic signal analysis. Proceedings of the 2017 14th International Conference The Experience of Designing and Application of CAD Systems in Microelectronics (CADSM), Lviv, Ukraine.","DOI":"10.1109\/CADSM.2017.7916092"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Nazarkevych, M., Lotoshynska, N., Klyujnyk, I., Voznyi, Y., Forostyna, S., and Maslanych, I. (2019, January 2\u20136). Complexity Evaluation of the Ateb-Gabor Filtration Algorithm in Biometric Security Systems. Proceedings of the 2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering (UKRCON), Lviv, Ukraine.","DOI":"10.1109\/UKRCON.2019.8879945"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1016\/j.snb.2007.09.060","article-title":"On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario","volume":"129","author":"Massera","year":"2008","journal-title":"Sens. Actuators B Chem."},{"key":"ref_48","unstructured":"Kotsovsky, V., Geche, F., and Batyuk, A. On the Computational Complexity of Learning Bithreshold Neural Units and Networks. Proceedings of the Lecture Notes in Computational Intelligence and Decision Making."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Teslyuk, T., Tsmots, I., Teslyuk, V., Medykovskyy, M., and Opotyak, Y. (2017). Architecture and Models for System-Level Computer-Aided Design of the Management System of Energy Efficiency of Technological Processes at the Enterprise. Proceedings of the Advances in Intelligent Systems and Computing II, Springer.","DOI":"10.1007\/978-3-319-70581-1_38"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/9\/2625\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:25:20Z","timestamp":1760361920000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/9\/2625"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,4]]},"references-count":49,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["s20092625"],"URL":"https:\/\/doi.org\/10.3390\/s20092625","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,4]]}}}