{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T21:20:39Z","timestamp":1768425639738,"version":"3.49.0"},"reference-count":32,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T00:00:00Z","timestamp":1748217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011033","name":"Agencia Estatal de Investigaci\u00f3n, Gobierno de Espa\u00f1a","doi-asserted-by":"publisher","award":["PID2023-149777OB-I00"],"award-info":[{"award-number":["PID2023-149777OB-I00"]}],"id":[{"id":"10.13039\/501100011033","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Edge Computing (EC) is one of the proposed solutions to address the problems that the industry is facing when implementing Predictive Maintenance (PdM) implementations that can benefit from Edge Artificial Intelligence (Edge AI) systems. In this work, we have compared six of the most popular no-code Edge AI frameworks in the market. The comparison considers economic cost, the number of features, usability, and performance. We used a combination of the analytic hierarchy process (AHP) and the technique for order performance by similarity to the ideal solution (TOPSIS) to compare the frameworks. We consulted ten independent experts on Edge AI, four employed in industry and the other six in academia. These experts defined the importance of each criterion by deciding the weights of TOPSIS using AHP. We performed two different classification tests on each framework platform using data from a public dataset for PdM on biomedical equipment. Magnetometer data were used for test 1, and accelerometer data were used for test 2. We obtained the F1 score, flash memory, and latency metrics. There was a high level of consensus between the worlds of academia and industry when assigning the weights. Therefore, the overall comparison ranked the analyzed frameworks similarly. NanoEdgeAIStudio ranked first when considering all weights and industry only weights, and Edge Impulse was the first option when using academia only weights. In terms of performance, there is room for improvement in most frameworks, as they did not reach the metrics of the previously developed custom Edge AI solution. We identified some limitations that should be fixed to improve the comparison method in the future, like adding weights to the feature criteria or increasing the number and variety of performance tests.<\/jats:p>","DOI":"10.3390\/bdcc9060145","type":"journal-article","created":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T04:49:52Z","timestamp":1748234992000},"page":"145","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["No-Code Edge Artificial Intelligence Frameworks Comparison Using a Multi-Sensor Predictive Maintenance Dataset"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0983-2386","authenticated-orcid":false,"given":"Juan M.","family":"Montes-S\u00e1nchez","sequence":"first","affiliation":[{"name":"Robotics and Technology of Computers Laboratory, ETSII-EPS, Universidad de Sevilla, 41004 Sevilla, Spain"},{"name":"Smart Computer Systems Research and Engineering Laboratory (SCORE), I3US, Universidad de Sevilla, 41012 Sevilla, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9030-4407","authenticated-orcid":false,"given":"Pl\u00e1cido","family":"Fern\u00e1ndez-Cuevas","sequence":"additional","affiliation":[{"name":"Robotics and Technology of Computers Laboratory, ETSII-EPS, Universidad de Sevilla, 41004 Sevilla, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4352-8759","authenticated-orcid":false,"given":"Francisco","family":"Luna-Perej\u00f3n","sequence":"additional","affiliation":[{"name":"Robotics and Technology of Computers Laboratory, ETSII-EPS, Universidad de Sevilla, 41004 Sevilla, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9466-485X","authenticated-orcid":false,"given":"Saturnino","family":"Vicente-Diaz","sequence":"additional","affiliation":[{"name":"Robotics and Technology of Computers Laboratory, ETSII-EPS, Universidad de Sevilla, 41004 Sevilla, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3061-5922","authenticated-orcid":false,"given":"\u00c1ngel","family":"Jim\u00e9nez-Fern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Robotics and Technology of Computers Laboratory, ETSII-EPS, Universidad de Sevilla, 41004 Sevilla, Spain"},{"name":"Smart Computer Systems Research and Engineering Laboratory (SCORE), I3US, Universidad de Sevilla, 41012 Sevilla, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104555","DOI":"10.1016\/j.respol.2022.104555","article-title":"Artificial intelligence and industrial innovation: Evidence from German firm-level data","volume":"51","author":"Rammer","year":"2022","journal-title":"Res. Policy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.eng.2024.04.002","article-title":"Preventing the Immense Increase in the Life-Cycle Energy and Carbon Footprints of LLM-Powered Intelligent Chatbots","volume":"40","author":"Jiang","year":"2024","journal-title":"Engineering"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/LES.2019.2949620","article-title":"Revisiting Simple and Energy Efficient Embedded Processor Designs Toward the Edge Computing","volume":"12","author":"Saso","year":"2020","journal-title":"IEEE Embed. Syst. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1016\/j.jmsy.2022.01.010","article-title":"Towards Edge Computing in intelligent manufacturing: Past, present and future","volume":"62","author":"Nain","year":"2022","journal-title":"J. Manuf. Syst."},{"key":"ref_5","first-page":"2191","article-title":"Edge-Cloud Computing for Internet of Things Data Analytics: Embedding Intelligence in the Edge With Deep Learning","volume":"17","author":"Ghosh","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_6","unstructured":"Google LLC (2025, May 16). LiteRT for Microcontrollers. Available online: https:\/\/ai.google.dev\/edge\/litert\/microcontrollers\/overview."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"101645","DOI":"10.1016\/j.rineng.2023.101645","article-title":"Improve Predictive Maintenance through the application of artificial intelligence: A systematic review","volume":"21","author":"Scaife","year":"2024","journal-title":"Results Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Guthardt, T., Kosiol, J., and Hohlfeld, O. (2024, January 22\u201327). Low-code vs. the developer: An empirical study on the developer experience and efficiency of a no-code platform. Proceedings of the ACM\/IEEE 27th International Conference on Model Driven Engineering Languages and Systems, Linz, Austria.","DOI":"10.1145\/3652620.3688332"},{"key":"ref_9","unstructured":"Edge AI Foundation (2025, May 16). Edge AI Foundation Webpage. Available online: https:\/\/www.edgeaifoundation.org\/."},{"key":"ref_10","unstructured":"Silva, J.X., Lopes, M., Avelino, G., and Santos, P. (June, January 29). Low-code and No-code Technologies Adoption: A Gray Literature Review. Proceedings of the XIX Brazilian Symposium on Information Systems, Macei\u00f3, Brazil. SBSI \u201923."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Monteiro, M., Branco, B.C., Silvestre, S., Avelino, G., and Valente, M.T. (Softw. Pract. Exp., 2025). NoCodeGPT: A No-Code Interface for Building Web Apps With Language Models, Softw. Pract. Exp., online version.","DOI":"10.1002\/spe.3432"},{"key":"ref_12","first-page":"56","article-title":"Teaching tip: Using no-code AI to teach machine learning in higher education","volume":"35","author":"Sundberg","year":"2024","journal-title":"J. Inf. Syst. Educ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chow, M., and Ng, O. (2025). From technology adopters to creators: Leveraging AI-assisted vibe coding to transform clinical teaching and learning. Med. Teach., 1\u20133.","DOI":"10.1080\/0142159X.2025.2488353"},{"key":"ref_14","unstructured":"Edge Impulse (2025, May 16). Edge Impulse\u2014The Leading Platform for Embedded Machine Learning. Available online: https:\/\/edgeimpulse.com\/."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Okoronkwo, C., Ikerionwu, C., Ramsurrun, V., Seeam, A., Esomonu, N., and Obodoagwu, V. (2024, January 26\u201328). Optimization of Waste Management Disposal Using Edge Impulse Studio on Tiny-Machine Learning (Tiny-ML). Proceedings of the 2024 IEEE 5th International Conference on Electro-Computing Technologies for Humanity (NIGERCON), Ado Ekiti, Nigeria.","DOI":"10.1109\/NIGERCON62786.2024.10927306"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Diab, M.S., and Rodriguez-Villegas, E. (2022, January 11\u201315). Performance evaluation of embedded image classification models using edge impulse for application on medical images. Proceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, UK.","DOI":"10.1109\/EMBC48229.2022.9871108"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hwang, C.L., Yoon, K., Hwang, C.L., and Yoon, K. (1981). Methods for multiple attribute decision making. Multiple Attribute Decision Making: Methods and Applications a State-of-the-Art Survey, Springer.","DOI":"10.1007\/978-3-642-48318-9"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.dss.2015.07.002","article-title":"Multi-criteria analysis for OS-EMR software selection problem: A comparative study","volume":"78","author":"Zaidan","year":"2015","journal-title":"Decis. Support Syst."},{"key":"ref_19","first-page":"7","article-title":"Machine selection by AHP and TOPSIS methods","volume":"4","author":"Karim","year":"2016","journal-title":"Am. J. Ind. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"25","DOI":"10.31181\/dma1120237","article-title":"A comprehensive review of multiple criteria decision-making (MCDM) Methods: Advancements, applications, and future directions","volume":"1","author":"Sahoo","year":"2023","journal-title":"Decis. Mak. Adv."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"109238","DOI":"10.1016\/j.asoc.2022.109238","article-title":"The applications of MCDM methods in COVID-19 pandemic: A state of the art review","volume":"126","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Qin, Y., Qi, Q., Shi, P., Lou, S., Scott, P.J., and Jiang, X. (2023). Multi-attribute decision-making methods in additive manufacturing: The state of the art. Processes, 11.","DOI":"10.3390\/pr11020497"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Canpolat \u015eahin, M., and Koluk\u0131sa Tarhan, A. (2025). Evaluation and Selection of Hardware and AI Models for Edge Applications: A Method and A Case Study on UAVs. Appl. Sci., 15.","DOI":"10.3390\/app15031026"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Aljohani, A. (2025). AI-Driven decision-making for personalized elderly care: A fuzzy MCDM-based framework for enhancing treatment recommendations. BMC Med. Inform. Decis. Mak., 25.","DOI":"10.1186\/s12911-025-02953-5"},{"key":"ref_25","unstructured":"Imagimob (2025, May 16). Imagimob\u2019s DEEPCRAFT\u2122 Studio. Available online: https:\/\/www.imagimob.com\/deepcraft\/."},{"key":"ref_26","unstructured":"STMicroelectronics (2025, May 16). NanoEdge AI Studio\u2014Development Tool. Available online: https:\/\/www.st.com\/en\/development-tools\/nanoedgeaistudio.html."},{"key":"ref_27","unstructured":"Neuton AI (2025, May 16). Neuton AI\u2014Tiny Machine Learning Platform. Available online: https:\/\/neuton.ai."},{"key":"ref_28","unstructured":"TDK Corporation (2025, May 16). TDK Sensei\u2014AutoML for Embedded AI. Available online: https:\/\/sensei.tdk.com\/automl."},{"key":"ref_29","unstructured":"SensiML Corporation (2025, May 16). SensiML\u2014AI Development Tools for Edge Devices. Available online: https:\/\/sensiml.com\/."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Montes-S\u00e1nchez, J., Uwate, Y., Nishio, Y., Jim\u00e9nez-Fern\u00e1ndez, A., and Vicente-D\u00edaz, S. (2024). Peristaltic pump aging detection dataset. idUS (Dep\u00f3sito Investig. Univ. Sevilla).","DOI":"10.12795\/11441\/162880"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Montes-S\u00e1nchez, J.M., Uwate, Y., Nishio, Y., Vicente-D\u00edaz, S., and Jim\u00e9nez-Fern\u00e1ndez, \u00c1. (IEEE Trans. Reliab., 2024). Predictive Maintenance Edge Artificial Intelligence Application Study Using Recurrent Neural Networks for Early Aging Detection in Peristaltic Pumps, IEEE Trans. Reliab., early access.","DOI":"10.1109\/TR.2024.3488963"},{"key":"ref_32","first-page":"1","article-title":"Implementing the analytic hierarchy process as a standard method for multi-criteria decision making in corporate enterprises\u2014A new AHP excel template with multiple inputs","volume":"Volume 2","author":"Goepel","year":"2013","journal-title":"Proceedings of the International Symposium on the Analytic Hierarchy Process"}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/6\/145\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:40:19Z","timestamp":1760031619000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/6\/145"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,26]]},"references-count":32,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["bdcc9060145"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9060145","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,26]]}}}