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Modeling Method to Abstract Collective Behavior of Smart IoT Systems in CPS

This paper presents a new modeling method to abstract the collective behavior of Smart IoT Systems in CPS, based on process algebra and a lattice structure. In general, process algebra is known to be one of the best formal methods to model IoTs, since each IoT can be represented as a process; a latt...

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Detalles Bibliográficos
Autores principales: Song, Junsup, Karagiannis, Dimitris, Lee, Moonkun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269706/
https://www.ncbi.nlm.nih.gov/pubmed/35808556
http://dx.doi.org/10.3390/s22135057
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author Song, Junsup
Karagiannis, Dimitris
Lee, Moonkun
author_facet Song, Junsup
Karagiannis, Dimitris
Lee, Moonkun
author_sort Song, Junsup
collection PubMed
description This paper presents a new modeling method to abstract the collective behavior of Smart IoT Systems in CPS, based on process algebra and a lattice structure. In general, process algebra is known to be one of the best formal methods to model IoTs, since each IoT can be represented as a process; a lattice can also be considered one of the best mathematical structures to abstract the collective behavior of IoTs since it has the hierarchical structure to represent multi-dimensional aspects of the interactions of IoTs. The dual approach using two mathematical structures is very challenging since the process algebra have to provide an expressive power to describe the smart behavior of IoTs, and the lattice has to provide an operational capability to handle the state-explosion problem generated from the interactions of IoTs. For these purposes, this paper presents a process algebra, called dTP-Calculus, which represents the smart behavior of IoTs with non-deterministic choice operation based on probability, and a lattice, called n:2-Lattice, which has special join and meet operations to handle the state explosion problem. The main advantage of the method is that the lattice can represent all the possible behavior of the IoT systems, and the patterns of behavior can be elaborated by finding the traces of the behavior in the lattice. Another main advantage is that the new notion of equivalences can be defined within n:2-Lattice, which can be used to solve the classical problem of exponential and non-deterministic complexity in the equivalences of Norm Chomsky and Robin Milner by abstracting them into polynomial and static complexity in the lattice. In order to prove the concept of the method, two tools are developed based on the ADOxx Meta-Modeling Platform: SAVE for the dTP-Calculus and PRISM for the n:2-Lattice. The method and tools can be considered one of the most challenging research topics in the area of modeling to represent the collective behavior of Smart IoT Systems.
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spelling pubmed-92697062022-07-09 Modeling Method to Abstract Collective Behavior of Smart IoT Systems in CPS Song, Junsup Karagiannis, Dimitris Lee, Moonkun Sensors (Basel) Article This paper presents a new modeling method to abstract the collective behavior of Smart IoT Systems in CPS, based on process algebra and a lattice structure. In general, process algebra is known to be one of the best formal methods to model IoTs, since each IoT can be represented as a process; a lattice can also be considered one of the best mathematical structures to abstract the collective behavior of IoTs since it has the hierarchical structure to represent multi-dimensional aspects of the interactions of IoTs. The dual approach using two mathematical structures is very challenging since the process algebra have to provide an expressive power to describe the smart behavior of IoTs, and the lattice has to provide an operational capability to handle the state-explosion problem generated from the interactions of IoTs. For these purposes, this paper presents a process algebra, called dTP-Calculus, which represents the smart behavior of IoTs with non-deterministic choice operation based on probability, and a lattice, called n:2-Lattice, which has special join and meet operations to handle the state explosion problem. The main advantage of the method is that the lattice can represent all the possible behavior of the IoT systems, and the patterns of behavior can be elaborated by finding the traces of the behavior in the lattice. Another main advantage is that the new notion of equivalences can be defined within n:2-Lattice, which can be used to solve the classical problem of exponential and non-deterministic complexity in the equivalences of Norm Chomsky and Robin Milner by abstracting them into polynomial and static complexity in the lattice. In order to prove the concept of the method, two tools are developed based on the ADOxx Meta-Modeling Platform: SAVE for the dTP-Calculus and PRISM for the n:2-Lattice. The method and tools can be considered one of the most challenging research topics in the area of modeling to represent the collective behavior of Smart IoT Systems. MDPI 2022-07-05 /pmc/articles/PMC9269706/ /pubmed/35808556 http://dx.doi.org/10.3390/s22135057 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Song, Junsup
Karagiannis, Dimitris
Lee, Moonkun
Modeling Method to Abstract Collective Behavior of Smart IoT Systems in CPS
title Modeling Method to Abstract Collective Behavior of Smart IoT Systems in CPS
title_full Modeling Method to Abstract Collective Behavior of Smart IoT Systems in CPS
title_fullStr Modeling Method to Abstract Collective Behavior of Smart IoT Systems in CPS
title_full_unstemmed Modeling Method to Abstract Collective Behavior of Smart IoT Systems in CPS
title_short Modeling Method to Abstract Collective Behavior of Smart IoT Systems in CPS
title_sort modeling method to abstract collective behavior of smart iot systems in cps
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269706/
https://www.ncbi.nlm.nih.gov/pubmed/35808556
http://dx.doi.org/10.3390/s22135057
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