{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T13:15:00Z","timestamp":1781615700348,"version":"3.54.5"},"reference-count":88,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,10,26]],"date-time":"2020-10-26T00:00:00Z","timestamp":1603670400000},"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>In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.<\/jats:p>","DOI":"10.3390\/s20216076","type":"journal-article","created":{"date-parts":[[2020,10,26]],"date-time":"2020-10-26T10:38:35Z","timestamp":1603708715000},"page":"6076","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":361,"title":["An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques"],"prefix":"10.3390","volume":"20","author":[{"given":"Rajalakshmi","family":"Krishnamurthi","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida 201309, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2919-6302","authenticated-orcid":false,"given":"Adarsh","family":"Kumar","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dhanalekshmi","family":"Gopinathan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida 201309, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anand","family":"Nayyar","sequence":"additional","affiliation":[{"name":"Graduate School, Duy Tan University, Da Nang 550000, Vietnam"},{"name":"Faculty of Information Technology, Duy Tan University, Da Nang 550000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7389-519X","authenticated-orcid":false,"given":"Basit","family":"Qureshi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Prince Sultan University, Riyadh 11586, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6855","DOI":"10.1109\/JIOT.2020.2970467","article-title":"Missing value imputation for Industrial IoT sensor data with large gaps","volume":"7","author":"Liu","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1198\/tech.2001.s49","article-title":"Wavelet Methods for Time Series Analysis","volume":"43","author":"Chernick","year":"2001","journal-title":"Technometrics"},{"key":"ref_3","unstructured":"(2020, April 10). 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