{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T18:39:11Z","timestamp":1767206351845,"version":"build-2238731810"},"reference-count":32,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,5,9]],"date-time":"2022-05-09T00:00:00Z","timestamp":1652054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004191","name":"Novo Nordisk Fonden","doi-asserted-by":"publisher","award":["NNF20OC0064411"],"award-info":[{"award-number":["NNF20OC0064411"]}],"id":[{"id":"10.13039\/501100004191","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Information"],"abstract":"<jats:p>IoT devices play a fundamental role in the machine learning (ML) application pipeline, as they collect rich data for model training using sensors. However, this process can be affected by uncontrollable variables that introduce errors into the data, resulting in a higher computational cost to eliminate them. Thus, selecting the most suitable algorithm for this pre-processing step on-device can reduce ML model complexity and unnecessary bandwidth usage for cloud processing. Therefore, this work presents a new sensor taxonomy with which to deploy data pre-processing on an IoT device by using a specific filter for each data type that the system handles. We define statistical and functional performance metrics to perform filter selection. Experimental results show that the Butterworth filter is a suitable solution for invariant sampling rates, while the Savi\u2013Golay and medium filters are appropriate choices for variable sampling rates.<\/jats:p>","DOI":"10.3390\/info13050241","type":"journal-article","created":{"date-parts":[[2022,5,9]],"date-time":"2022-05-09T21:23:27Z","timestamp":1652131407000},"page":"241","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A New Data-Preprocessing-Related Taxonomy of Sensors for IoT Applications"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1995-400X","authenticated-orcid":false,"given":"Paul D.","family":"Rosero-Montalvo","sequence":"first","affiliation":[{"name":"Computer Science Department, IT University of Copenhagen, 2300 Copenhagen, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5715-7784","authenticated-orcid":false,"given":"Vivian F.","family":"L\u00f3pez-Batista","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Automatics, University of Salamanca, 37008 Salamanca, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9045-6997","authenticated-orcid":false,"given":"Diego H.","family":"Peluffo-Ord\u00f3\u00f1ez","sequence":"additional","affiliation":[{"name":"Morocco and SDAS Researh Group, Modeling, Simulation and Data Analysis (MSDA) Research Program, Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco"},{"name":"Faculty of Engineering, Corporaci\u00f3n Universitaria Aut\u00f3noma de Nari\u00f1o, Pasto 520001, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Debauche, O., Mahmoudi, S., and Guttadauria, A. (2022). A New Edge Computing Architecture for IoT and Multimedia Data Management. Information, 13.","DOI":"10.3390\/info13020089"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bose, T., Bandyopadhyay, S., Kumar, S., Bhattacharyya, A., and Pal, A. (2016, January 14\u201318). Signal Characteristics on Sensor Data Compression in IoT\u2014An Investigation. Proceedings of the 2016 IEEE International Conference on Sensing, Communication and Networking (SECON Workshops), Sydney, Australia.","DOI":"10.1109\/SECONW.2016.7746810"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Canziani, A., Culurciello, E., and Paszke, A. (2017, January 28\u201331). Evaluation of neural network architectures for embedded systems. Proceedings of the 2017 IEEE International Symposium on Circuits and Systems (ISCAS), Baltimore, MD, USA.","DOI":"10.1109\/ISCAS.2017.8050276"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Komatsu, N., and Nakano, M. (2015). Embedded Systems. Encyclopedia of Biometrics, Springer.","DOI":"10.1007\/978-1-4899-7488-4_287"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Dobrin, A., Stamatescu, G., Dragana, C., and Sgarciu, V. (2016, January 13\u201315). Cloud challenges for networked embedded systems: A review. Proceedings of the 2016 20th International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, Romania.","DOI":"10.1109\/ICSTCC.2016.7790777"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kalantar-zadeh, K. (2013). Sensors. Sensors, Springer.","DOI":"10.1007\/978-1-4614-5052-8"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dasgupta, R., and Dey, S. (2013, January 3\u20135). A comprehensive sensor taxonomy and semantic knowledge representation: Energy meter use case. Proceedings of the 2013 Seventh International Conference on Sensing Technology (ICST), Wellington, New Zealand.","DOI":"10.1109\/ICSensT.2013.6727761"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1109\/LES.2017.2758679","article-title":"A Taxonomy of General Purpose Approximate Computing Techniques","volume":"10","author":"Moreau","year":"2018","journal-title":"IEEE Embed. Syst. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lin, Y.L., Kyung, C.M., Yasuura, H., and Liu, Y. (2015). Smart Sensors and Systems, Springer International Publishing.","DOI":"10.1007\/978-3-319-14711-6"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Raschka, S., Patterson, J., and Nolet, C. (2020). Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence. Information, 11.","DOI":"10.3390\/info11040193"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1584","DOI":"10.1109\/JPROC.2019.2922285","article-title":"Computation Offloading Toward Edge Computing","volume":"107","author":"Lin","year":"2019","journal-title":"Proc. IEEE"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Huuhtanen, T., Ambos, H., and Jung, A. (2019, January 2\u20136). Outlier Detection from Non-Smooth Sensor Data. Proceedings of the 2019 27th European Signal Processing Conference (EUSIPCO), Coru\u00f1a, Spain.","DOI":"10.23919\/EUSIPCO.2019.8903061"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Fran\u00e7a, C.M., Couto, R.S., and Velloso, P.B. (2021). Missing Data Imputation in Internet of Things Gateways. Information, 12.","DOI":"10.3390\/info12100425"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1109\/TII.2014.2299897","article-title":"Distributed Sampled-Data Filtering for Sensor Networks With Nonuniform Sampling Periods","volume":"10","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_15","unstructured":"Britton, R. (2010). Digital Filter Designer\u2019s Handbook, McGraw-Hill. Available online: http:\/\/dsp-book.narod.ru\/DFD\/DFD0.pdf."},{"key":"ref_16","unstructured":"Williams, A. (2022, April 10). Analog Filter and Circuit Design Handbook. Available online: https:\/\/www.amazon.com\/Analog-Filter-Circuit-Design-Handbook\/dp\/0071816712."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Aslam, F., Aimin, W., Li, M., and Ur Rehman, K. (2020). Innovation in the Era of IoT and Industry 5.0: Absolute Innovation Management (AIM) Framework. Information, 11.","DOI":"10.3390\/info11020124"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"MacRuairi, R., Keane, M.T., and Coleman, G. (2008, January 25\u201331). A Wireless Sensor Network Application Requirements Taxonomy. Proceedings of the 2008 Second International Conference on Sensor Technologies and Applications (Sensorcomm 2008), Washington, DC, USA.","DOI":"10.1109\/SENSORCOMM.2008.73"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Fowler, K.R. (2009, January 12\u201314). The future of sensors and sensor networks survey results projecting the next 5 years. Proceedings of the 2009 IEEE Sensors Applications Symposium, Atlanta, GA, USA.","DOI":"10.1109\/SAS.2009.4801766"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tuukkanen, S., and Rajala, S. (2015, January 1\u20134). A survey of printable piezoelectric sensors. Proceedings of the 2015 IEEE SENSORS, Busan, Korea.","DOI":"10.1109\/ICSENS.2015.7370542"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1109\/COMST.2017.2691551","article-title":"Structural Health Monitoring Using Wireless Sensor Networks: A Comprehensive Survey","volume":"19","author":"Noel","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/JSEN.2016.2628346","article-title":"A Survey on Activity Detection and Classification Using Wearable Sensors","volume":"17","author":"Cornacchia","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Quy, V.K., Hau, N.V., Anh, D.V., Quy, N.M., Ban, N.T., Lanza, S., Randazzo, G., and Muzirafuti, A. (2022). IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges. Appl. Sci., 12.","DOI":"10.3390\/app12073396"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Morrison, W., Guerdan, L., Kanugo, J., Trull, T., and Shang, Y. (2018, January 18\u201321). TigerAware: An Innovative Mobile Survey and Sensor Data Collection and Analytics System. Proceedings of the 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC), Guangzhou, China.","DOI":"10.1109\/DSC.2018.00025"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Infanteena, S.D., and Anita, E.M. (2017, January 23\u201324). Survey on compressive data collection techniques for wireless sensor networks. Proceedings of the 2017 International Conference on Information Communication and Embedded Systems (ICICES), Chennai, India.","DOI":"10.1109\/ICICES.2017.8070765"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tiboni, M., Borboni, A., V\u00e9rit\u00e9, F., Bregoli, C., and Amici, C. (2022). Sensors and Actuation Technologies in Exoskeletons: A Review. Sensors, 22.","DOI":"10.3390\/s22030884"},{"key":"ref_27","unstructured":"Bhat, D., Kaur, A., and Singh, S. (2015, January 11\u201313). Wireless sensor network specific low power FIR filter design and implementation on FPGA. Proceedings of the 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Safaei, M., Driss, M., Boulila, W., Sundararajan, E.A., and Safaei, M. (2021). Global Outliers Detection in Wireless Sensor Networks: A Novel Approach Integrating Time-Series Analysis, Entropy, and Random Forest-based Classification. arXiv.","DOI":"10.1002\/spe.3020"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kowalski, P., and Smyk, R. (2018, January 9\u201312). Review and comparison of smoothing algorithms for one-dimensional data noise reduction. Proceedings of the 2018 International Interdisciplinary PhD Workshop (IIPhDW), Swinoujscie, Poland.","DOI":"10.1109\/IIPHDW.2018.8388373"},{"key":"ref_30","unstructured":"Saad, L.B., Beferull-Lozano, B., and Isufi, E. (2020). Quantization Analysis and Robust Design for Distributed Graph Filters. arXiv."},{"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":"Gizlenmistir, Y. (2018, January 2\u20135). Filter based analysis unit design for data acquisition systems. Proceedings of the 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir, Turkey.","DOI":"10.1109\/SIU.2018.8404851"}],"updated-by":[{"DOI":"10.3390\/info16060510","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2022,5,9]],"date-time":"2022-05-09T00:00:00Z","timestamp":1652054400000}}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/5\/241\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T14:10:25Z","timestamp":1754230225000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/5\/241"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,9]]},"references-count":32,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["info13050241"],"URL":"https:\/\/doi.org\/10.3390\/info13050241","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,9]]}}}