{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T06:00:40Z","timestamp":1770271240026,"version":"3.49.0"},"reference-count":52,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T00:00:00Z","timestamp":1628640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key R&amp;D Program of China","award":["2016YFC0500105"],"award-info":[{"award-number":["2016YFC0500105"]}]},{"name":"The Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA20060602"],"award-info":[{"award-number":["XDA20060602"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>River basin cyberinfrastructure with the Internet of Things (IoT) as the core has brought watershed data science into the big data era, greatly improving data acquisition and sharing efficiency. However, challenges in analyzing, processing, and applying very large quantities of observational data remain. Given the observational needs in watershed research, we studied the construction of river basin cyberinfrastructure and developed an integrated observational data control system (IODCS). The IODCS is an important platform for processing large quantities of observational data, including automated collection, storage, analysis, processing, and release. This paper presents various aspects of the IODCS in detail, including the system\u2019s overall design, function realization, big data analysis methods, and integrated models. We took the middle reaches of the Heihe River Basin (HRB) as the application research area to show the performance of the developed system. Since the system began operation, it has automatically received, analyzed, and stored more than 1.4 billion observational data records, with an average of more than 14 million observational data records processed per month and up to 21,011 active users. The demonstrated results show that the IODCS can effectively leverage the processing capability of massive observational data and provide a new perspective for facilitating ecological and hydrological scientific research on the HRB.<\/jats:p>","DOI":"10.3390\/s21165429","type":"journal-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T08:35:52Z","timestamp":1628670952000},"page":"5429","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["River Basin Cyberinfrastructure in the Big Data Era: An Integrated Observational Data Control System in the Heihe River Basin"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7144-7163","authenticated-orcid":false,"given":"Jianwen","family":"Guo","sequence":"first","affiliation":[{"name":"Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minghu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Technology, Lanzhou University of Technology, Lanzhou 730050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingsheng","family":"Shang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Lanzhou University of Finance and Economics, Lanzhou 730020, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adan","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2999-9818","authenticated-orcid":false,"given":"Xin","family":"Li","sequence":"additional","affiliation":[{"name":"National Tibetan Plateau Data Center, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,11]]},"reference":[{"key":"ref_1","unstructured":"Toffler, A. (1980). The Third Wave, Bantam Books, Inc.. [1st ed.]."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bertot, J.C., and Choi, H. (2013, January 17\u201320). Big data and e-government: Issues, policies, and recommendations. Proceedings of the 14th Annual International Conference on Digital Government Research, Quebec, QC, Canada.","DOI":"10.1145\/2479724.2479730"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1108\/LHT-06-2017-0131","article-title":"A Big Data smart library recommender system for an educational institution","volume":"36","year":"2018","journal-title":"Libr. Hi Tech"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1007\/s11434-016-1041-y","article-title":"Big Earth Data from space: A new engine for Earth science","volume":"61","author":"Guo","year":"2016","journal-title":"Sci. Bull."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/17538947.2014.1003106","article-title":"Big data analytics for earth sciences: The EarthServer approach","volume":"9","author":"Baumann","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_6","first-page":"379","article-title":"Towards Geo-spatial Information Science in Big Data Era","volume":"45","author":"Li","year":"2016","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1102","DOI":"10.1016\/j.rse.2010.12.015","article-title":"Satellite passive microwave remote sensing for monitoring global land surface phenology","volume":"115","author":"Jones","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1007\/s11430-013-4728-9","article-title":"Characterization, controlling and reduction of uncertainties in the modeling and observation of land-surface systems","volume":"57","author":"Li","year":"2014","journal-title":"Sci. China Earth Sci."},{"key":"ref_9","first-page":"266","article-title":"Environmental monitoring of spatiotemporal change in land use\/land cover and its impact on land surface temperature in El-Fayoum governorate, Egypt","volume":"8","author":"Effat","year":"2017","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1093\/nsr\/nwu017","article-title":"Integrated study of the water-ecosystem-economy in the Heihe River Basin","volume":"1","author":"Cheng","year":"2014","journal-title":"Natl. Sci. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1016\/j.scib.2019.07.004","article-title":"Internet of Things to network smart devices for ecosystem monitoring","volume":"64","author":"Li","year":"2019","journal-title":"Sci. Bull."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7648","DOI":"10.1109\/JIOT.2020.2988249","article-title":"Drone-enabled Internet-of-Things relay for environmental monitoring in remote areas without public networks","volume":"7","author":"Zhang","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1002\/2014EA000044","article-title":"Toward an environmental Internet of Things","volume":"2","author":"Hart","year":"2015","journal-title":"Earth Space Sci."},{"key":"ref_14","first-page":"032029","article-title":"Modeling of information processing in the internet of things at agricultural enterprises","volume":"Volume 315","author":"Lvovich","year":"2019","journal-title":"IOP Conference Series: Earth and Environmental Science"},{"key":"ref_15","first-page":"399","article-title":"Design of Field Observation Data Automatic Assembling System","volume":"28","author":"Guo","year":"2013","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_16","first-page":"1027","article-title":"Improvement and Application of automatic data in Heihe river basin downloading system","volume":"30","author":"Wu","year":"2015","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_17","first-page":"28","article-title":"Design and Operation of Network Management Platform for Forest Ecological Positioning Observation System","volume":"31","author":"Wang","year":"2018","journal-title":"World For. Res."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Khayyat, Z., Ilyas, I.F., Jindal, A., Madden, S., Ouzzani, M., Papotti, P., Quian\u00e9-Ruiz, J.-A., Tang, N., and Yin, S. (2015\u20134, January 31). Bigdansing: A system for big data cleansing. Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, Australia.","DOI":"10.1145\/2723372.2747646"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.future.2019.04.008","article-title":"SWITCH workbench: A novel approach for the development and deployment of time-critical microservice-based cloud-native applications","volume":"99","author":"Cigale","year":"2019","journal-title":"Future Gener. Comput. Syst. Int. J. Escience"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Koulouzis, S., Martin, P., Zhou, H., Hu, Y., Wang, J., Carval, T., Grenier, B., Heikkinen, J., De Laat, C., and Zhao, Z. (2020). Time-critical data management in clouds: Challenges and a Dynamic Real-Time Infrastructure Planner (DRIP) solution. Concurr. Comput. Pract. Exp., 32.","DOI":"10.1002\/cpe.5269"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1109\/TNSE.2020.3017556","article-title":"Intelligent UAVs Trajectory Optimization from Space-Time for Data Collection in Social Networks","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Huang, S., Liu, A., Zhang, S., Wang, T., and Xiong, N. (2020). BD-VTE: A Novel Baseline Data based Verifiable Trust Evaluation Scheme for Smart Network Systems. IEEE Trans. Netw. Sci. Eng.","DOI":"10.1109\/TNSE.2020.3014455"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ren, Y., Wang, T., Zhang, S., and Zhang, J. (2020). An intelligent big data collection technology based on micro mobile data centers for crowdsensing vehicular sensor network. Pers. Ubiquitous Comput.","DOI":"10.1007\/s00779-020-01440-0"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Aftab, M.U., Oluwasanmi, A., Alharbi, A., Sohaib, O., Nie, X., Qin, Z., and Ngo, S.T. (2021). Secure and dynamic access control for the Internet of Things (IoT) based traffic system. Peerj Comput. Sci.","DOI":"10.7717\/peerj-cs.471"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"D\u00edaz, J.J., Mura, I., Franco, J.F., and Akhavan-Tabatabaei, R. (2021). aiRe-A web-based R application for simple, accessible and repeatable analysis of urban air quality data. Environ. Model. Softw., 138.","DOI":"10.1016\/j.envsoft.2021.104976"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/MC.2013.336","article-title":"Cyberinfrastructures: Bridging the Divide between Scientific Research and Software Engineering","volume":"47","author":"Gorton","year":"2014","journal-title":"Computer"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2020","DOI":"10.1109\/LGRS.2014.2319301","article-title":"Data sharing and data set application of watershed allied telemetry experimental research","volume":"11","author":"Wang","year":"2014","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1038\/ajh.2011.146","article-title":"Quantification of the calibration error in the transfer function-derived central aortic blood pressures","volume":"24","author":"Shih","year":"2011","journal-title":"Am. J. Hypertens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3983","DOI":"10.1109\/TIT.2017.2693180","article-title":"The error propagation analysis of the received signal strength-based simultaneous localization and tracking in wireless sensor networks","volume":"63","author":"Zhou","year":"2017","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_30","first-page":"1829","article-title":"An Anomaly Detection Method of Wireless Sensor Network Based on Multi-Modals Data Stream","volume":"40","author":"Fei","year":"2017","journal-title":"Chin. J. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"175192","DOI":"10.1109\/ACCESS.2019.2957602","article-title":"An adaptive outlier detection and processing approach towards time series sensor data","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, M.H., Guo, J.W., Li, X., and Jin, R. (2020). Data-driven anomaly detection approach for time-series streaming data. Sensors, 20.","DOI":"10.3390\/s20195646"},{"key":"ref_33","first-page":"716","article-title":"Automatic data quality control of observations in wireless sensor network","volume":"12","author":"Guo","year":"2014","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Schwichtenberg, H. (2018). Installing Entity Framework Core. Modern Data Access with Entity Framework Core, Apress.","DOI":"10.1007\/978-1-4842-3552-2"},{"key":"ref_35","unstructured":"Albertini, O.R., Bhargov, D., Denissov, A., Guerrero, F., Jayaram, N., Kak, N., Khanna, E., Kislal, O., Kumar, A., and McQuillan, F. (2020, January 14\u201319). Image classification in Greenplum database using deep learning. Proceedings of the International Conference on Management of Data, Portland, OR, USA. ACM SIGMOD Record."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"195385","DOI":"10.1109\/ACCESS.2020.3034466","article-title":"Research on an Application of Shared Architecture for Ecological Monitoring-oriented IoT Streaming Data","volume":"8","author":"Wu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_37","first-page":"1","article-title":"Application Research of 3D Visualization System for Three Poles Scientific Discovery","volume":"43","author":"Wu","year":"2021","journal-title":"J. Glaciol. Geocryol."},{"key":"ref_38","unstructured":"(2021, March 26). OpenLayers API Docs. Available online: https:\/\/openlayers.org\/en\/latest\/apidoc\/."},{"key":"ref_39","unstructured":"(2021, March 26). ECharts Docs. Available online: https:\/\/echarts.apache.org\/en\/api.html#echarts."},{"key":"ref_40","unstructured":"(2021, March 26). WebSocket. Available online: https:\/\/developer.mozilla.org\/en-US\/docs\/Web\/API\/WebSocket."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1175\/BAMS-D-12-00154.1","article-title":"Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific objectives and experimental design","volume":"94","author":"Li","year":"2013","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Li, X., Liu, S., Xiao, Q., Ma, M., Jin, R., Che, T., Wang, W., Hu, X., Xu, Z., and Wen, J. (2017). A multiscale dataset for understanding complex eco-hydrological processes in a heterogeneous oasis system. Sci. Data, 170083.","DOI":"10.1038\/sdata.2017.83"},{"key":"ref_43","first-page":"993","article-title":"Introduction of eco-hydrological wireless sensor network in the Heihe River Basin","volume":"27","author":"Jin","year":"2012","journal-title":"Adv. Earth Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"180072","DOI":"10.2136\/vzj2018.04.0072","article-title":"The Heihe integrated observatory network: A basin-scale land surface processes observatory in China","volume":"17","author":"Liu","year":"2018","journal-title":"Vadose Zone J."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"13140","DOI":"10.1002\/2013JD020260","article-title":"Intercomparison of surface energy flux measurement systems used during the HiWATER-MUSOEXE","volume":"118","author":"Xu","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2015","DOI":"10.1109\/LGRS.2014.2319085","article-title":"A nested eco-hydrological wireless sensor network for capturing the surface heterogeneity in the midstream area of the Heihe River Basin, China","volume":"11","author":"Jin","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_47","first-page":"252","article-title":"Study on quality control approach for Heihe wireless sensor network observation data","volume":"28","author":"Liu","year":"2013","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.envsoft.2013.06.007","article-title":"Parameter sensitivity analysis of crop growth models based on the Extended Fourier Amplitude Sensitivity Test method","volume":"48","author":"Wang","year":"2013","journal-title":"Environ. Model. Softw."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.eja.2013.03.005","article-title":"Estimating near future regional corn yields by integrating multi-source observations into a crop growth model","volume":"49","author":"Wang","year":"2013","journal-title":"Eur. J. Agron."},{"key":"ref_50","first-page":"833","article-title":"Spatial Sampling Design of the Sensor Network for Monitoring the Surface Freeze \/ thaw Cycles over the Heterogeneous Surface in the Heihe River Basin","volume":"29","author":"Jian","year":"2014","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2132","DOI":"10.1109\/LGRS.2017.2754961","article-title":"Understanding the heterogeneity of soil moisture and evapotranspiration using multiscale observations from satellites, airborne sensors, and a ground-based observation matrix","volume":"14","author":"Jin","year":"2017","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_52","unstructured":"(2021, March 26). Fortran 90. Available online: https:\/\/www.fortran90.org\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/16\/5429\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:44:09Z","timestamp":1760165049000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/16\/5429"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,11]]},"references-count":52,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["s21165429"],"URL":"https:\/\/doi.org\/10.3390\/s21165429","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,11]]}}}