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All these devices have a limited number of computational resources, just enough to perform the simple operation they are in charge of. The remaining operations are delegated to powerful gateways that manage sensing nodes, but resources are never unlimited, and as more and more devices are deployed on Industry 4.0 platforms, gateways present more problems to handle massive machine-type communications. Although the problems are diverse, those related to security are especially critical. To enable sensing nodes to establish secure communications, several semiconductor companies are currently promoting a new generation of devices based on Physical Unclonable Functions, whose usage grows every year in many real industrial scenarios. Those hardware devices do not consume any computational resource but force the gateway to keep large key-value catalogues for each individual node. In this context, memory usage is not scalable and processing delays increase exponentially with each new node on the platform. In this paper, we address this challenge through predictor-corrector models, representing the key-value catalogues. Models are mathematically complex, but we argue that they consume less computational resources than current approaches. The lightweight models are based on complex functions managed as Laurent series, cubic spline interpolations, and Boolean functions also developed as series. Unknown parameters in these models are predicted, and eventually corrected to calculate the output value for each given key. The initial parameters are based on the Kane Yee formula. An experimental analysis and a performance evaluation are provided in the experimental section, showing that the proposed approach causes a significant reduction in the resource consumption.<\/jats:p>","DOI":"10.3233\/ica-230713","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T11:17:30Z","timestamp":1688123850000},"page":"369-393","source":"Crossref","is-referenced-by-count":5,"title":["Predictor-corrector models for lightweight massive machine-type communications in Industry 4.0"],"prefix":"10.1177","volume":"30","author":[{"given":"Borja","family":"Bordel","sequence":"first","affiliation":[{"name":"Department of Informatic systems, Universidad Polit\u00e9cnica de Madrid, Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ram\u00f3n","family":"Alcarria","sequence":"additional","affiliation":[{"name":"Department of Topographic Engineering and Cartography, Universidad Polit\u00e9cnica de Madrid, Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joaquin","family":"Chung","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory, Lemont, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajkumar","family":"Kettimuthu","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory, Lemont, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/ICA-230713_ref1","doi-asserted-by":"crossref","first-page":"119869","DOI":"10.1016\/j.jclepro.2019.119869","article-title":"Industry 40, digitization, and opportunities for sustainability","volume":"252","author":"Ghobakhloo","year":"2020","journal-title":"Journal of cleaner production."},{"issue":"4","key":"10.3233\/ICA-230713_ref2","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/MC.2017.105","article-title":"Cyber-physical systems","volume":"50","author":"Zanero","year":"2017","journal-title":"Computer."},{"issue":"1","key":"10.3233\/ICA-230713_ref3","doi-asserted-by":"crossref","first-page":"83","DOI":"10.3233\/ICA-210667","article-title":"Recognizing human activities in Industry 40 scenarios through an analysis-modeling-recognition algorithm and context labels","volume":"29","author":"Bordel","year":"2022","journal-title":"Integrated Computer-Aided Engineering"},{"issue":"8","key":"10.3233\/ICA-230713_ref4","doi-asserted-by":"crossref","first-page":"2941","DOI":"10.1080\/00207543.2018.1444806","article-title":"Industry 40: state of the art and future trends","volume":"56","author":"Xu","year":"2018","journal-title":"International Journal of Production Research."},{"issue":"1","key":"10.3233\/ICA-230713_ref5","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1109\/TII.2018.2843169","article-title":"An edge-based architecture to support efficient applications for healthcare industry 40","volume":"15","author":"Pace","year":"2018","journal-title":"IEEE Transactions on Industrial Informatics."},{"issue":"2","key":"10.3233\/ICA-230713_ref6","doi-asserted-by":"crossref","first-page":"153","DOI":"10.3233\/ICA-210673","article-title":"Lightweight encryption for short-range wireless biometric authentication systems in Industry 40","volume":"29","author":"Bordel","year":"2022","journal-title":"Integrated Computer-Aided Engineering."},{"key":"10.3233\/ICA-230713_ref7","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1002\/9781119494492.ch9","article-title":"Massive Machine Type Communication Systems","author":"Haesik","year":"2020","journal-title":"In Design and Optimization for 5G; 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