{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T19:48:42Z","timestamp":1771876122751,"version":"3.50.1"},"reference-count":81,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T00:00:00Z","timestamp":1771804800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T00:00:00Z","timestamp":1771804800000},"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":["Cluster Comput"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s10586-026-05973-6","type":"journal-article","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T18:42:59Z","timestamp":1771872179000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A predictive scheme in fog computing to reduce the volume of data sent to cloud layer"],"prefix":"10.1007","volume":"29","author":[{"given":"Ali Akbar","family":"Sadri","sequence":"first","affiliation":[]},{"given":"Amir Masoud","family":"Rahmani","sequence":"additional","affiliation":[]},{"given":"Morteza","family":"Saberikamarposhti","sequence":"additional","affiliation":[]},{"given":"Mehdi","family":"Hosseinzadeh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,23]]},"reference":[{"issue":"7","key":"5973_CR1","doi-asserted-by":"publisher","first-page":"1645","DOI":"10.1016\/j.future.2013.01.010","volume":"29","author":"J Gubbi","year":"2013","unstructured":"Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of things (IoT): A vision, architectural elements, and future directions. Future Generation Comput. Syst. 29(7), 1645\u20131660 (2013)","journal-title":"Future Generation Comput. Syst."},{"key":"5973_CR2","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1016\/j.future.2017.10.045","volume":"82","author":"G Manogaran","year":"2018","unstructured":"Manogaran, G., Varatharajan, R., Lopez, D., Kumar, P.M.: Revathi Sundarasekar, and Chandu Thota. A new architecture of internet of things and big data ecosystem for secured smart healthcare monitoring and alerting system. Future Generation Comput. Syst. 82, 375\u2013387 (2018)","journal-title":"Future Generation Comput. Syst."},{"issue":"11","key":"5973_CR3","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/s10916-015-0327-y","volume":"39","author":"G Suciu","year":"2015","unstructured":"Suciu, G., Suciu, V., Martian, A., Craciunescu, R., Vulpe, A., Marcu, I.: Simona Halunga, and Octavian Fratu. Big data, internet of things and cloud convergence\u2013an architecture for secure e-health applications. J. Med. Syst. 39(11), 141 (2015)","journal-title":"J. Med. Syst."},{"key":"5973_CR4","doi-asserted-by":"crossref","unstructured":"Manogaran, G., Lopez, D., Thota, C., Abbas, K. M.: Big data analytics in healthcare internet of things. In: Pyne, S., Sundarasekar, R. (eds.) Innovative Healthcare Systems for the 21st Century, pp. 263\u2013284. Springer, Cham, (2017)","DOI":"10.1007\/978-3-319-55774-8_10"},{"key":"5973_CR5","doi-asserted-by":"crossref","unstructured":"Batool, S., Saqib, N.A., Khan, M.A.: Internet of Things data analytics for user authentication and activity recognition. In 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC), pp. 183\u2013187. IEEE, (2017)","DOI":"10.1109\/FMEC.2017.7946428"},{"key":"5973_CR6","doi-asserted-by":"crossref","unstructured":"Pawar, K., Attar, V.: A survey on data analytic platforms for Internet of Things. In 2016 International Conference on Computing, Analytics and Security Trends (CAST), pp. 605\u2013610. IEEE, (2016)","DOI":"10.1109\/CAST.2016.7915039"},{"key":"5973_CR7","first-page":"20817","volume":"OPFRA001","author":"OpenFog Consortium Architecture Working Group","year":"2017","unstructured":"OpenFog Consortium Architecture Working Group: OpenFog Ref. Archit. Fog Comput. OPFRA001, 20817 (2017)","journal-title":"OpenFog Ref. Archit. Fog Comput."},{"key":"5973_CR8","doi-asserted-by":"crossref","unstructured":"Naeem, R., Zojaj, S., Bashir, M.F., Amjad: Haider Abbas, and Hammad Afzal. Fog computing in internet of things: Practical applications and future directions. Peer-to-Peer Networking and Applications: 1\u201327. (2019)","DOI":"10.1007\/s12083-019-00728-0"},{"key":"5973_CR9","doi-asserted-by":"crossref","unstructured":"Sadri, A. A., Rahmani, A. M., Saberikamarposhti, M., Hosseinzadeh, M.: Fog Data Management: A Vision, Challenges, and Future Directions. J. Netw. Comput. Appl. 174, 102882 (2021)","DOI":"10.1016\/j.jnca.2020.102882"},{"key":"5973_CR10","doi-asserted-by":"crossref","unstructured":"Nikoui, T., Samizadeh, A. M., Rahmani, A. M., Tabarsaied, H.: Data management in fog computing. In Fog and Edge Computing: Principles and Paradigms, vol. 35, pp. 171\u2013190 (2019)","DOI":"10.1002\/9781119525080.ch8"},{"key":"5973_CR11","doi-asserted-by":"crossref","unstructured":"Datta, S., Kanti, C., Bonnet, Haerri, J.: Fog computing architecture to enable consumer centric internet of things services. In 2015 International Symposium on Consumer Electronics (ISCE), pp. 1\u20132. IEEE, (2015)","DOI":"10.1109\/ISCE.2015.7177778"},{"key":"5973_CR12","doi-asserted-by":"crossref","unstructured":"Nazmudeen, M.S., Haja, A.T., Wan, Seyed, M., Buhari: Improved throughput for power line communication (plc) for smart meters using fog computing based data aggregation approach. In IEEE International Smart Cities Conference (ISC2), pp. 1\u20134. IEEE, (2016)","DOI":"10.1109\/ISC2.2016.7580841"},{"key":"5973_CR13","doi-asserted-by":"crossref","unstructured":"Hassan, M.A., Xiao, M., Wei, Q., Chen, S.: Help your mobile applications with fog computing. In 2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking-Workshops (SECON Workshops), pp. 1\u20136. IEEE, (2015)","DOI":"10.1109\/SECONW.2015.7328146"},{"issue":"1","key":"5973_CR14","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1109\/COMST.2017.2771153","volume":"20","author":"C Mouradian","year":"2017","unstructured":"Mouradian, C., Naboulsi, D., Yangui, S., Glitho, R.H., Monique, J., Morrow: Polakos. A comprehensive survey on fog computing: State-of-the-art and research challenges. IEEE Commun. Surv. Tutorials. 20(1), 416\u2013464 (2017)","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"5973_CR15","doi-asserted-by":"crossref","unstructured":"Hiriyannaiah, S., Khan, Z., Singh, A., Siddesh, G. M., Srinivasa, K. G.: Data Reduction Techniques in Fog Data Analytics for IoT Applications. In: Buyya, R., Srirama, S. N. (eds.) Fog Data Analytics for IoT Applications, pp. 279\u2013309. Springer, Singapore, (2020)","DOI":"10.1007\/978-981-15-6044-6_12"},{"issue":"2","key":"5973_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJEHMC.2020040101","volume":"11","author":"G Fischer","year":"2020","unstructured":"Fischer, G., Souto: Rodrigo Da Rosa Righi, Vinicius Facco Rodrigues, and Cristiano Andr\u00e9 Da Costa. Use of internet of things with data prediction on healthcare environments: A survey. Int. J. E-Health Med. Commun. (IJEHMC). 11(2), 1\u201319 (2020)","journal-title":"Int. J. E-Health Med. Commun. (IJEHMC)"},{"key":"5973_CR17","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.tele.2019.03.005","volume":"41","author":"T Saheb","year":"2019","unstructured":"Saheb, T.: Paradigm of IoT big data analytics in healthcare industry: A review of scientific literature and mapping of research trends. Telematics Inform. 41, 70\u201385 (2019)","journal-title":"Telematics Inform."},{"key":"5973_CR18","doi-asserted-by":"publisher","first-page":"100177","DOI":"10.1016\/j.iot.2020.100177","volume":"9","author":"AA Alli","year":"2020","unstructured":"Alli, A.A., Muhammad Mahbub, A.: The fog cloud of things: A survey on concepts, architecture, standards, tools, and applications. Internet Things. 9, 100177 (2020)","journal-title":"Internet Things"},{"key":"5973_CR19","doi-asserted-by":"crossref","unstructured":"Arivazhagan, C., Natarajan, V.: A Survey on Fog Computing Paradigms, Challenges and Opportunities in IoT. In 2020 International Conference on Communication and Signal Processing (ICCSP), pp. 0385\u20130389. IEEE, (2020)","DOI":"10.1109\/ICCSP48568.2020.9182229"},{"issue":"4","key":"5973_CR20","doi-asserted-by":"publisher","first-page":"2456","DOI":"10.1109\/COMST.2017.2736886","volume":"19","author":"A Gharaibeh","year":"2017","unstructured":"Gharaibeh, A., Salahuddin, M.A., Hussini, S.J., Khreishah, A., Khalil, I.: Mohsen Guizani, and Ala Al-Fuqaha. Smart cities: A survey on data management, security, and enabling technologies. IEEE Commun. Surv. Tutorials. 19(4), 2456\u20132501 (2017)","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"5973_CR21","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.pmcj.2018.12.007","volume":"52","author":"P Bellavista","year":"2019","unstructured":"Bellavista, P., Berrocal, J., Corradi, A., Das, S.K.: Luca Foschini, and Alessandro Zanni. A Surv. Fog Comput. Internet Things Pervasive Mob. Comput. 52, 71\u201399 (2019)","journal-title":"A Surv. Fog Comput. Internet Things Pervasive Mob. Comput."},{"key":"5973_CR22","doi-asserted-by":"publisher","first-page":"100629","DOI":"10.1016\/j.iot.2022.100629","volume":"20","author":"AA Sadri","year":"2022","unstructured":"Sadri, A. A., Rahmani, A. M., Saberikamarposhti, M., Hosseinzadeh, M.: Data Reduction in Fog Computing and Internet of Things: A Systematic Literature Survey. Internet Things 20, 100629 (2022)","journal-title":"Internet Things"},{"key":"5973_CR23","doi-asserted-by":"crossref","unstructured":"Briki, S., Khabou, N., Ismael Bouassida, R.: A Comparative Analysis of Time Series Prediction Techniques a Systematic Literature Review (SLR). In International Conference on Model and Data Engineering, pp. 3\u201314. Springer, Cham, (2024)","DOI":"10.1007\/978-3-031-49333-1_1"},{"key":"5973_CR24","doi-asserted-by":"crossref","unstructured":"Akaike, H.: Markovian representation of stochastic processes and its application to the analysis of autoregressive moving average processes. In: Krishnaiah, P. R., Kanal, L. N. (eds.) Selected Papers of Hirotugu Akaike, pp. 223\u2013247. Springer, New York, NY, (1998)","DOI":"10.1007\/978-1-4612-1694-0_17"},{"key":"5973_CR25","doi-asserted-by":"crossref","unstructured":"Wu, J., Fang, Q., Xu, Y., Su, J., Ma, F.: Kalman filter based time series prediction of cake factory daily sale. In: 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1\u20137. IEEE (2017)","DOI":"10.1109\/CISP-BMEI.2017.8302108"},{"issue":"1","key":"5973_CR26","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/S0167-9236(00)00086-5","volume":"30","author":"RS Sexton","year":"2000","unstructured":"Sexton, R.S., Robert, E.: Dorsey. Reliable classification using neural networks: A genetic algorithm and backpropagation comparison. Decis. Support Syst. 30(1), 11\u201322 (2000)","journal-title":"Decis. Support Syst."},{"key":"5973_CR27","doi-asserted-by":"crossref","unstructured":"Safarinejadian, B., Tajeddini, M.A., Ramezani, A.: Predict time series using extended, unscented, and cubature Kalman filters based on feed-forward neural network algorithm. In The 3rd International Conference on Control, Instrumentation, and Automation, pp. 159\u2013164. IEEE, (2013)","DOI":"10.1109\/ICCIAutom.2013.6912827"},{"key":"5973_CR28","doi-asserted-by":"crossref","unstructured":"Julier, S.J.: and Jeffrey K. Uhlmann. Unscented filtering and nonlinear estimation. Proceedings of the IEEE 92, no. 3 : 401\u2013422. (2004)","DOI":"10.1109\/JPROC.2003.823141"},{"issue":"6","key":"5973_CR29","doi-asserted-by":"publisher","first-page":"1254","DOI":"10.1109\/TAC.2009.2019800","volume":"54","author":"I Arasaratnam","year":"2009","unstructured":"Arasaratnam, I.: Cubature Kalman filters. IEEE Trans. Autom. Control. 54(6), 1254\u20131269 (2009)","journal-title":"IEEE Trans. Autom. Control"},{"issue":"386","key":"5973_CR30","first-page":"378","volume":"79","author":"G Kitagawa","year":"1984","unstructured":"Kitagawa, G., Gersch, W.: A smoothness priors\u2013state space modeling of time series with trend and seasonality. J. Am. Stat. Assoc. 79(386), 378\u2013389 (1984)","journal-title":"J. Am. Stat. Assoc."},{"key":"5973_CR31","doi-asserted-by":"crossref","unstructured":"Mehdipour, F., Javadi, B.: and Aniket Mahanti. FOG-Engine: Towards big data analytics in the fog. In IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC\/PiCom\/DataCom\/CyberSciTech), pp. 640\u2013646. IEEE, (2016)","DOI":"10.1109\/DASC-PICom-DataCom-CyberSciTec.2016.116"},{"key":"5973_CR32","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1002\/dac.4812","volume":"34","author":"T Moulahi","year":"2021","unstructured":"Moulahi, T., Khediri, S.E., Khan, R.U., Zidi, S.: A fog computing data reduce level to enhance the cloud of things performance. Int. J. Commun Syst. 34, 9 (2021)","journal-title":"Int. J. Commun Syst"},{"key":"5973_CR33","doi-asserted-by":"crossref","unstructured":"Oikawa, H.: and Masaaki Kondo. Density-based data selection and management for edge computing. In 2021 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 1\u201311. IEEE, (2021)","DOI":"10.1109\/PERCOM50583.2021.9439127"},{"key":"5973_CR34","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1016\/j.future.2021.09.044","volume":"128","author":"T Guo","year":"2022","unstructured":"Guo, T., Yu, K., Aloqaily, M., Wan, S.: Constructing a prior-dependent graph for data clustering and dimension reduction in the edge of AIoT. Future Generation Comput. Syst. 128, 381\u2013394 (2022)","journal-title":"Future Generation Comput. Syst."},{"issue":"1","key":"5973_CR35","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1007\/s10922-020-09567-4","volume":"29","author":"AK Idrees","year":"2021","unstructured":"Idrees, A.K., Ali Kadhum, M., Al-Qurabat: Energy-efficient data transmission and aggregation protocol in periodic sensor networks based fog computing. J. Netw. Syst. Manage. 29(1), 4 (2021)","journal-title":"J. Netw. Syst. Manage."},{"issue":"5s","key":"5973_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3126501","volume":"16","author":"I Azimi","year":"2017","unstructured":"Azimi, I., Anzanpour, A., Rahmani, A.M., Pahikkala, T., Levorato, M., Liljeberg, P., Nikil Dutt: HiCH: Hierarchical fog-assisted computing architecture for healthcare IoT. ACM Trans. Embedded Comput. Syst. (TECS). 16(5s), 1\u201320 (2017)","journal-title":"ACM Trans. Embedded Comput. Syst. (TECS)"},{"key":"5973_CR37","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s10586-018-2848-x","volume":"22","author":"AH Rabie","year":"2019","unstructured":"Rabie, A.H., Shereen, H., Ali, Hesham, A., Ali, Ahmed, I.: Saleh. A fog based load forecasting strategy for smart grids using big electrical data. Cluster Comput. 22, 241\u2013270 (2019)","journal-title":"Cluster Comput."},{"key":"5973_CR38","doi-asserted-by":"crossref","unstructured":"Occorso, M., An, M., Olsen, R., Perry, V.: Anomaly Detection as a Data Reduction Approach for Test Event Analysis at the Edge. In 2023 IEEE International Conference on Big Data (BigData), pp. 3863\u20133867. IEEE, (2023)","DOI":"10.1109\/BigData59044.2023.10386215"},{"key":"5973_CR39","doi-asserted-by":"crossref","unstructured":"Alwash, R.F., Idrees, A.K., Al-Obaidi, S.: A Methodological Review on EEG Data Reduction in Edge\/Fog computing-based IoMT networks. In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1\u20137. IEEE, (2023)","DOI":"10.1109\/ICCCNT56998.2023.10307551"},{"key":"5973_CR40","doi-asserted-by":"crossref","unstructured":"Xie, C., Tao, W., Zeng, Z.: and Yuran Dong. Binary-Convolution Data-Reduction Network for Edge\u2013Cloud IIoT Anomaly Detection. Electronics 12, no. 15 : 3229. (2023)","DOI":"10.3390\/electronics12153229"},{"issue":"2","key":"5973_CR41","doi-asserted-by":"publisher","first-page":"836","DOI":"10.1109\/TGCN.2021.3127487","volume":"6","author":"S Bebortta","year":"2021","unstructured":"Bebortta, S., Senapati, D., Panigrahi, C.R., Pati, B.: An adaptive modeling and performance evaluation framework for edge-enabled green IoT systems. IEEE Trans. Green. Commun. Netw. 6(2), 836\u2013844 (2021)","journal-title":"IEEE Trans. Green. Commun. Netw."},{"key":"5973_CR42","first-page":"142","volume":"223","author":"FM Junior","year":"2022","unstructured":"Junior, F.M., Ribeiro, Reinaldo, A.C., Bianchi, R.C., Prati, K., Kolehmainen, J.-P., Soininen, Carlos, A., Kamienski: Data Reduct. Based Mach. Learn. Algorithms Fog Comput. IoT Smart Agric. Biosystems Eng. 223, 142\u2013158 (2022)","journal-title":"Data Reduct. Based Mach. Learn. Algorithms Fog Comput. IoT Smart Agric. Biosystems Eng."},{"key":"5973_CR43","doi-asserted-by":"crossref","unstructured":"Mononen, T., Aref, M.M., Mattila, J.: Filtering scheme for context-aware fog computing in cyber-physical systems. In 2018 14th IEEE\/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), pp. 1\u20137. IEEE, (2018)","DOI":"10.1109\/MESA.2018.8449153"},{"key":"5973_CR44","doi-asserted-by":"crossref","unstructured":"Pflanzner, Tamas, K., Zs Leszko, Kert\u00e9sz, A.: SUMMON: Gathering smart city data to support IoT-Fog-Cloud simulations. In 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC), pp. 71\u201378. IEEE, (2018)","DOI":"10.1109\/FMEC.2018.8364047"},{"key":"5973_CR45","doi-asserted-by":"crossref","unstructured":"Ullah, A., Sehr, I., Muhammad Akbar, and, Ning, H.: FoG assisted secure De-duplicated data dissemination in smart healthcare IoT. In 2018 IEEE International Conference on Smart Internet of Things (SmartIoT), pp. 166\u2013171. IEEE, (2018)","DOI":"10.1109\/SmartIoT.2018.00038"},{"key":"5973_CR46","doi-asserted-by":"crossref","unstructured":"Kim, D., Roh, C.: and Donkyu Baek. Preprocessing at Application Nodes for Reduction of Data Transmission in Edge Computing. In 2023 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), pp. 1\u20134. IEEE, (2023)","DOI":"10.1109\/ICCE-Asia59966.2023.10326355"},{"key":"5973_CR47","doi-asserted-by":"crossref","unstructured":"Zhang, H., Na, J., Zhang, B.: Scenario adaptive edge data reduction. In 2021 IEEE International Conference on Edge Computing (EDGE), pp. 9\u201316. IEEE, (2021)","DOI":"10.1109\/EDGE53862.2021.00011"},{"issue":"1","key":"5973_CR48","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1109\/TNSE.2018.2859307","volume":"7","author":"K Wang","year":"2018","unstructured":"Wang, K., Shao, Y., Xie, L., Wu, J.: Adaptive and fault-tolerant data processing in healthcare IoT based on fog computing. IEEE Trans. Netw. Sci. Eng. 7(1), 263\u2013273 (2018)","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"issue":"2","key":"5973_CR49","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1080\/0952813X.2019.1647563","volume":"32","author":"A Kaur","year":"2020","unstructured":"Kaur, A., Sandeep, K.: Sood. Cloud-Fog based framework for drought prediction and forecasting using artificial neural network and genetic algorithm. J. Exp. Theor. Artif. Intell. 32(2), 273\u2013289 (2020)","journal-title":"J. Exp. Theor. Artif. Intell."},{"issue":"5","key":"5973_CR50","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1080\/24725854.2023.2184884","volume":"56","author":"Y Li","year":"2024","unstructured":"Li, Y., Wang, L., Chen, X., Jin, R.: Distributed data filtering and modeling for fog and networked manufacturing. IISE Trans. 56(5), 485\u2013496 (2024)","journal-title":"IISE Trans."},{"key":"5973_CR51","doi-asserted-by":"crossref","unstructured":"Waheed, S., Peer, A., Shah: Application of Fog and Cloud Computing for Patient\u2019s Data in the Internet of Things. In Advances in Internet, Data and Web Technologies: The 7th International Conference on Emerging Internet, Data and Web Technologies (EIDWT-2019), pp. 425\u2013436. Springer International Publishing, (2019)","DOI":"10.1007\/978-3-030-12839-5_39"},{"issue":"1","key":"5973_CR52","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1186\/s13677-023-00424-8","volume":"12","author":"A Elouali","year":"2023","unstructured":"Elouali, A.: Mora, and Francisco Jos\u00e9 Mora-Gimeno. Data transmission reduction formalization for cloud offloading-based IoT systems. J. Cloud Comput. 12(1), 48 (2023)","journal-title":"J. Cloud Comput."},{"issue":"1","key":"5973_CR53","doi-asserted-by":"publisher","first-page":"138","DOI":"10.3390\/smartcities3010008","volume":"3","author":"S Vergis","year":"2020","unstructured":"Vergis, S., Komianos, V., Tsoumanis, G.: Athanasios Tsipis, and Konstantinos Oikonomou. A low-cost vehicular traffic monitoring system using fog computing. Smart Cities. 3(1), 138\u2013156 (2020)","journal-title":"Smart Cities"},{"key":"5973_CR54","doi-asserted-by":"crossref","unstructured":"Jain, K., Agarwal, A., Kumar, A.: A novel data prediction technique based on correlation for data reduction in sensor networks. In Proceedings of International Conference on Artificial Intelligence and Applications: ICAIA 2020, pp. 595\u2013606. Springer Singapore, (2021)","DOI":"10.1007\/978-981-15-4992-2_56"},{"key":"5973_CR55","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.neucom.2021.02.105","volume":"485","author":"A Fathalla","year":"2022","unstructured":"Fathalla, A., Li, K., Salah, A., Marwa, F.: Mohamed. An LSTM-based distributed scheme for data transmission reduction of IoT systems. Neurocomputing. 485, 166\u2013180 (2022)","journal-title":"Neurocomputing"},{"key":"5973_CR56","doi-asserted-by":"crossref","unstructured":"Salim, C., Mitton, N.: K-predictions based data reduction approach in WSN for smart agriculture. Computing 103, no. 3 : 509\u2013532. (2021)","DOI":"10.1007\/s00607-020-00864-z"},{"key":"5973_CR57","doi-asserted-by":"crossref","unstructured":"Deng, H., Guo, Z., Lin, R., Zou, H.: Fog computing architecture-based data reduction scheme for WSN. In 2019 1st International Conference on Industrial Artificial Intelligence (IAI), pp. 1\u20136. IEEE, (2019)","DOI":"10.1109\/ICIAI.2019.8850817"},{"key":"5973_CR58","doi-asserted-by":"publisher","first-page":"40969","DOI":"10.1109\/ACCESS.2019.2907808","volume":"7","author":"M Taneja","year":"2019","unstructured":"Taneja, M., Jalodia, N., Davy, A.: Distributed decomposed data analytics in fog enabled IoT deployments. IEEE Access. 7, 40969\u201340981 (2019)","journal-title":"IEEE Access."},{"key":"5973_CR59","doi-asserted-by":"crossref","unstructured":"P\u0142aczek, B.: A Multi-Agent Prediction Method for Data Sampling and Transmission Reduction in Internet of Things Sensor Networks. Sensors 23, no. 20 : 8478. (2023)","DOI":"10.3390\/s23208478"},{"key":"5973_CR60","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.future.2023.11.007","volume":"152","author":"B P\u0142aczek","year":"2024","unstructured":"P\u0142aczek, B.: Prediction-based data reduction with dynamic target node selection in IoT sensor networks. Future Generation Comput. Syst. 152, 225\u2013238 (2024)","journal-title":"Future Generation Comput. Syst."},{"issue":"12","key":"5973_CR61","doi-asserted-by":"publisher","first-page":"3309","DOI":"10.1109\/TPDS.2023.3327750","volume":"34","author":"L Yang","year":"2023","unstructured":"Yang, L., Liao, Y., Cheng, X., Xia, M., Xie, G.: Efficient edge data management framework for IIoT via Prediction-Based data reduction. IEEE Trans. Parallel Distrib. Syst. 34(12), 3309\u20133322 (2023)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"5973_CR62","doi-asserted-by":"publisher","first-page":"103556","DOI":"10.1016\/j.jnca.2022.103556","volume":"211","author":"H Liazid","year":"2023","unstructured":"Liazid, H., Lehsaini, M., Liazid, A.: Data transmission reduction using prediction and aggregation techniques in IoT-based wireless sensor networks. J. Netw. Comput. Appl. 211, 103556 (2023)","journal-title":"J. Netw. Comput. Appl."},{"key":"5973_CR63","first-page":"3","volume":"26","author":"P Raja","year":"2023","unstructured":"Raja, P., Deepa, T., Bharathi, V.: Prediction-based Spatial correlation clustering algorithm for efficient data reduction in wireless sensor network. Int. J. Adv. Intell. Paradigms. 26, 3\u20134 (2023)","journal-title":"Int. J. Adv. Intell. Paradigms"},{"key":"5973_CR64","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2025.3537889","author":"Y Liu","year":"2025","unstructured":"Liu, Y., Zhang, S., Li, X., and Jin Hu: Multi-Variable environmental prediction for Chinese solar greenhouse IoT system based on FB-LSTM model. IEEE Internet Things J. (2025). https:\/\/doi.org\/10.1109\/JIOT.2025.3537889","journal-title":"IEEE Internet Things J."},{"key":"5973_CR65","doi-asserted-by":"crossref","unstructured":"Peng, Z., Yin, L.: Nonlinear Prediction Model of Vehicle Network Traffic Management Based on the Internet of Things. Syst. Soft Comput. 20, 254 (2025)","DOI":"10.1016\/j.sasc.2025.200254"},{"issue":"2","key":"5973_CR66","doi-asserted-by":"publisher","first-page":"e5969","DOI":"10.1002\/dac.5969","volume":"38","author":"BB Pawar","year":"2025","unstructured":"Pawar, B.B., Devyani, S.: Jadhav. Hybrid optimization-based topology construction and DRNN-based prediction method for data reduction in IoT. Int. J. Commun Syst. 38(2), e5969 (2025)","journal-title":"Int. J. Commun Syst"},{"key":"5973_CR67","doi-asserted-by":"crossref","unstructured":"Wu, Y., Yang, B., Zhu, D., Chen, C., Guan, X.: Synergy between resource-efficient data transmission and precision-adaptive fault diagnosis for high-frequency signals. IEEE Trans. Ind. Inf. 21(5), 3756\u20133767 (2025)","DOI":"10.1109\/TII.2025.3534403"},{"key":"5973_CR68","doi-asserted-by":"crossref","unstructured":"Jin, P., Du, W., Jin, W.: Efficient productivity prediction model based on edge data compression in smart farms. Smart Agric. Technol. 12, 101242 (2025)","DOI":"10.1016\/j.atech.2025.101242"},{"key":"5973_CR69","doi-asserted-by":"crossref","unstructured":"Pioli, L., Douglas, D. J. De Macedo, D. G. Costa, M. A. R. Dantas: Intelligent Data Reduction for IoT: A Context-Driven Framework. IEEE Access 13, pp. 117500\u2013117520 (2025)","DOI":"10.1109\/ACCESS.2025.3586539"},{"key":"5973_CR70","doi-asserted-by":"crossref","unstructured":"Rezaee, M. R., Abdul Hamid, N. A. W., Hussin, M., Zukarnain, Z. A.: Fog offloading and task management in IoT-fog-cloud environment: Review of algorithms, networks, and SDN application. IEEE Access 12, 39058\u201339080 (2024)","DOI":"10.1109\/ACCESS.2024.3375368"},{"key":"5973_CR71","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1080\/02757259609532305","volume":"13","author":"PR Coppin","year":"1996","unstructured":"Coppin, P.R., Marvin, E.: Bauer. Digital change detection in forest ecosystems with remote sensing imagery. Remote Sens. Reviews. 13, 3\u20134 (1996)","journal-title":"Remote Sens. Reviews"},{"issue":"2","key":"5973_CR72","doi-asserted-by":"publisher","first-page":"151","DOI":"10.32614\/RJ-2014-031","volume":"6","author":"S Korkmaz","year":"2014","unstructured":"Korkmaz, S., Goksuluk, D., Zararsiz, G.: MVN: An R package for assessing multivariate normality. R J. 6(2), 151\u2013162 (2014)","journal-title":"R J."},{"key":"5973_CR73","doi-asserted-by":"crossref","unstructured":"Henderson, A., Ralph: Testing experimental data for univariate normality. Clin. Chim. Acta. 366, 112\u2013129 (2006)","DOI":"10.1016\/j.cca.2005.11.007"},{"issue":"3","key":"5973_CR74","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1093\/biomet\/57.3.519","volume":"57","author":"KV Mardia","year":"1970","unstructured":"Mardia, K.V.: Measures of multivariate skewness and kurtosis with applications. Biometrika. 57(3), 519\u2013530 (1970)","journal-title":"Biometrika"},{"key":"5973_CR75","unstructured":"Hartikainen, J., Solin, A., S\u00e4rkk\u00e4, S.: Optimal filtering with Kalman filters and smoothers. Department of Biomedical Engineering and Computational Sciences, Aalto University School of Science, 16th August 10, no. 1.331: 150 (2011)"},{"key":"5973_CR76","doi-asserted-by":"crossref","unstructured":"Hesar, H. D., Danandeh, A. D.: Adaptive augmented cubature kalman filter\/smoother for ECG denoising. Biomed. Eng. Lett. 14(4), 689\u2013705 (2024)","DOI":"10.1007\/s13534-024-00362-7"},{"issue":"1","key":"5973_CR77","doi-asserted-by":"publisher","first-page":"101807","DOI":"10.1016\/j.jksuci.2023.101807","volume":"36","author":"M Islam","year":"2024","unstructured":"Islam, M., Rezaul: Arif Ahmad, and Mohammad Shahidur Rahman. Bangla text normalization for text-to-speech synthesizer using machine learning algorithms. J. King Saud University-Computer Inform. Sci. 36(1), 101807 (2024)","journal-title":"J. King Saud University-Computer Inform. Sci."},{"key":"5973_CR78","doi-asserted-by":"crossref","unstructured":"Chen, Z., Biggie, H., Ahmed, N., Julier, S., Heckman, C.: Kalman Filter auto-tuning with consistent and robust bayesian optimization. IEEE Trans. Aerosp. Electron. Syst. 60(2), 2236\u20132250 (2024)","DOI":"10.1109\/TAES.2024.3350587"},{"key":"5973_CR79","unstructured":"The MIT-BIH Normal Sinus Rhythm Database, PhysioNet: Cambridge, MA [Online] Available: http:\/\/www.physionet.org\/physiobank\/database\/nsrdb\/"},{"key":"5973_CR80","doi-asserted-by":"crossref","unstructured":"Sameni, R., Shamsollahi, M.B., Jutten, C.: and Massoud Babaie-Zade. Filtering noisy ECG signals using the extended Kalman filter based on a modified dynamic ECG model. In Computers in Cardiology, pp. 1017\u20131020. IEEE, (2005)","DOI":"10.1109\/CIC.2005.1588283"},{"key":"5973_CR81","unstructured":"Sameni, R.: Extraction of Fetal Cardiac Signals from an Array of Maternal Abdominal Recordings. PhD diss., Institut National Polytechnique de Grenoble (INPG); Sharif University of Technology (SUT), (2008)"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-026-05973-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-026-05973-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-026-05973-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T18:43:09Z","timestamp":1771872189000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-026-05973-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,23]]},"references-count":81,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["5973"],"URL":"https:\/\/doi.org\/10.1007\/s10586-026-05973-6","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,23]]},"assertion":[{"value":"25 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"The authors declare that they have no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"194"}}