IoT@run-time: a model-based approach to support deployment and self-adaptations in IoT systems

Documento de tesis doctoral acerca del desarrollo de una solución basada en modelado para dar soporte en el diseño, despliegue, y autoadaptación de sistemas IoT multicapa.

Autores:: Alfonso Díaz, Iván David

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2022

Institución:: Universidad de los Andes

Repositorio:: Séneca: repositorio Uniandes

Idioma:: eng

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network_acronym_str	UNIANDES2
network_name_str	Séneca: repositorio Uniandes
repository_id_str
dc.title.none.fl_str_mv	IoT@run-time: a model-based approach to support deployment and self-adaptations in IoT systems
title	IoT@run-time: a model-based approach to support deployment and self-adaptations in IoT systems
spellingShingle	IoT@run-time: a model-based approach to support deployment and self-adaptations in IoT systems Internet of Things Model-Driven Engineering Self-Adaptive System Domain Specific Language Edge Computing Fog Computing. Ingeniería
title_short	IoT@run-time: a model-based approach to support deployment and self-adaptations in IoT systems
title_full	IoT@run-time: a model-based approach to support deployment and self-adaptations in IoT systems
title_fullStr	IoT@run-time: a model-based approach to support deployment and self-adaptations in IoT systems
title_full_unstemmed	IoT@run-time: a model-based approach to support deployment and self-adaptations in IoT systems
title_sort	IoT@run-time: a model-based approach to support deployment and self-adaptations in IoT systems
dc.creator.fl_str_mv	Alfonso Díaz, Iván David
dc.contributor.advisor.none.fl_str_mv	Cabot Sagrera, Jordi Garcés Pernett, Kelly Johany Castro Barrera, Harold Enrique
dc.contributor.author.none.fl_str_mv	Alfonso Díaz, Iván David
dc.contributor.jury.none.fl_str_mv	Capilla Sevilla, Rafael Guilherme, Horta Travassos Lozano Garzón, Carlos Andrés
dc.contributor.researchgroup.es_CO.fl_str_mv	TICSw-Tecnologías de Información y Construcción de Software SOM Research Lab
dc.subject.keyword.none.fl_str_mv	Internet of Things Model-Driven Engineering Self-Adaptive System Domain Specific Language Edge Computing Fog Computing.
topic	Internet of Things Model-Driven Engineering Self-Adaptive System Domain Specific Language Edge Computing Fog Computing. Ingeniería
dc.subject.themes.es_CO.fl_str_mv	Ingeniería
description	Documento de tesis doctoral acerca del desarrollo de una solución basada en modelado para dar soporte en el diseño, despliegue, y autoadaptación de sistemas IoT multicapa.
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-12-14T20:47:45Z
dc.date.available.none.fl_str_mv	2022-12-14T20:47:45Z
dc.date.issued.none.fl_str_mv	2022-12-05
dc.type.es_CO.fl_str_mv	Trabajo de grado - Doctorado
dc.type.driver.none.fl_str_mv	info:eu-repo/semantics/doctoralThesis
dc.type.version.none.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.coar.none.fl_str_mv	http://purl.org/coar/resource_type/c_db06
dc.type.content.es_CO.fl_str_mv	Text
dc.type.redcol.none.fl_str_mv	https://purl.org/redcol/resource_type/TD
format	http://purl.org/coar/resource_type/c_db06
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	http://hdl.handle.net/1992/63561
dc.identifier.doi.none.fl_str_mv	10.57784/1992/63561
dc.identifier.instname.es_CO.fl_str_mv	instname:Universidad de los Andes
dc.identifier.reponame.es_CO.fl_str_mv	reponame:Repositorio Institucional Séneca
dc.identifier.repourl.es_CO.fl_str_mv	repourl:https://repositorio.uniandes.edu.co/
url	http://hdl.handle.net/1992/63561
identifier_str_mv	10.57784/1992/63561 instname:Universidad de los Andes reponame:Repositorio Institucional Séneca repourl:https://repositorio.uniandes.edu.co/
dc.language.iso.es_CO.fl_str_mv	eng
language	eng
dc.relation.references.es_CO.fl_str_mv	F. Ahmadighohandizi and K. Systä. Application development and deployment for iot devices. In European Conference on Service-Oriented and Cloud Computing, pages 74-85. Springer, 2016. A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash. Internet of things: A survey on enabling technologies, protocols, and applications. IEEE communications surveys & tutorials, 17(4):2347-2376, 2015. S. Al-Sarawi, M. Anbar, K. Alieyan, and M. Alzubaidi. Internet of things (iot) communication protocols. In 2017 8th International conference on information technology (ICIT), pages 685-690. IEEE, 2017. M. Alaa, A. A. Zaidan, B. B. Zaidan, M. Talal, and M. L. M. Kiah. A review of smart home applications based on internet of things. Journal of Network and Computer Applications, 97:48-65, 2017. A. H. Alavi, P. Jiao, W. G. Buttlar, and N. Lajnef. Internet of things-enabled smart cities: State-of-the-art and future trends. Measurement, 129:589-606, 2018. I. Alfonso, K. Garcés, H. Castro, and J. Cabot. Self-adaptive architectures in IoT systems: a systematic literature review. Journal of Internet Services and Applications, 12(1):1-28, 2021. I. Alfonso, K. Garcés, H. Castro, and J. Cabot. Modeling self-adaptative IoT architectures. In 2021 ACM/IEEE Int. Conf. on Model Driven Engineering Languages and Systems Companion (MODELS-C), pages 761-766, 2021. I. Alfonso, C. Goméz, K. Garcés, and J. Chavarriaga. Lifetime optimization of wireless sensor networks for gas monitoring in underground coal mining. In 2018 7th International Conference on Computers Communications and Control (ICCCC), pages 224-230. IEEE, 2018. F. Alkhabbas, I. Murturi, R. Spalazzese, P. Davidsson, and S. Dustdar. A goaldriven approach for deploying self-adaptive iot systems. In 2020 IEEE International Conference on Software Architecture (ICSA), pages 146-156. IEEE, 2020. M. Alrowaily and Z. Lu. Secure edge computing in iot systems: Review and case studies. In 2018 IEEE/ACM Symposium on Edge Computing (SEC), pages 440-444. IEEE, 2018. M. Artac, T. Borovak, E. Di Nitto, M. Guerriero, D. Perez-Palacin, and D. A. Tamburri. Infrastructure-as-code for data-intensive architectures: A modeldriven development approach. In 2018 IEEE International Conference on Software Architecture (ICSA), pages 156-15609. IEEE, 2018. K. Ashton et al. That "internet of things" thing. RFID journal, 22(7):97-114, 2009. M.Asif-Ur-Rahman, F. Afsana, M. Mahmud, M. S. Kaiser, M. R. Ahmed, O. Kaiwartya, and A. James-Taylor. Toward a heterogeneous mist, fog, and cloudbased framework for the internet of healthcare things. IEEE Internet of Things Journal, 6(3):4049-4062, 2018. U. Aßmann, S. Götz, J.-M. Jézéquel, B. Morin, and M. Trapp. A reference architecture and roadmap for models@ run. time systems. In Models@ runtime, pages 1-18. Springer, 2014. A. Barii, V. Amaral, and M. Goulão. Usability driven dsl development with use-me. Computer Languages, Systems & Structures, 51:118-157, 2018. J. A. Barriga, P. J. Clemente, E. Sosa-Sánchez, and Á. E. Prieto. Simulateiot: Domain specific language to design, code generation and execute iot simulation environments. IEEE Access, 9:92531-92552, 2021. L. Bass, P. Clements, and R. Kazman. Software architecture in practice. Addison-Wesley Professional, 2003. L. Bass, I. Weber, and L. Zhu. DevOps: A software architect's perspective. Addison-Wesley Professional, 2015. J. Beauquier, B. Bérard, and L. Fribourg. A new rewrite method for proving convergence of self-stabilizing systems. In International Symposium on Distributed Computing, pages 240-255, Bratislava, 1999. Springer. I. Bedhief, L. Foschini, P. Bellavista, M. Kassar, and T. Aguili. Toward selfadaptive software defined fog networking architecture for iiot and industry 4.0. In 2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), pages 1-5. IEEE, 2019. N. Bencomo, R. B. France, B. H. Cheng, and U. Aßmann. Models@ runtime: foundations, applications, and roadmaps, volume 8378. Springer, 2014. N. Bencomo and L. H. G. Paucar. Ram: Causally-connected and requirements aware runtime models using bayesian learning. In 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS), pages 216-226. IEEE, 2019. T. Berger, M. Völter, H. P. Jensen, T. Dangprasert, and J. Siegmund. Efficiency of projectional editing: A controlled experiment. In Proc. of the 24th ACM SIGSOFT Int. Symposium on Foundations of Software Engineering, pages 763-774, 2016. A. Bergmayr, U. Breitenbücher, O. Kopp, M. Wimmer, G. Kappel, and F. Leymann. From architecture modeling to application provisioning for the cloud by combining uml and tosca. In CLOSER (2), pages 97-108, 2016. G. Blair, N. Bencomo, and R. B. France. Models@run.time. Computer, 42(10):22-27, 2009. M. Brambilla, J. Cabot, and M. Wimmer. Model-driven software engineering in practice, 2nd edn. Synthesis Lectures on Software Engineering. Morgan & Claypool Publishers, USA, 2017. M. Breitbach, D. Schäfer, J. Edinger, and C. Becker. Context-aware data and task placement in edge computing environments. In 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom), pages 1-10. IEEE, 2019. D. Bri, M. Fernández-Diego, M. Garcia, F. Ramos, and J. Lloret. How the weather impacts on the performance of an outdoor wlan. IEEE Communications Letters, 16(8):1184-1187, 2012. A. Bucchiarone, A. Cicchetti, F. Ciccozzi, and A. Pierantonio. Domain-specific Languages in Practice: With JetBrains MPS. Springer, 2021. R. Buyya and S. N. Srirama. Fog and edge computing: principles and paradigms. John Wiley & Sons, 2019. A. Cañete, M. Amor, and L. Fuentes. Supporting iot applications deployment on edge-based infrastructures using multi-layer feature models. Journal of Systems and Software, 183:111086, 2022. E. A. Castillo and A. Ahmadinia. Iot-based multi-view machine vision systems. In 2019 IEEE International Conference on Big Data (Big Data), pages 5206-5212. IEEE, 2019. A. Chehri, T. El Ouahmani, and N. Hakem. Mining and iot-based vehicle adhoc network: industry opportunities and innovation. Internet of Things, page 100117, 2019. L. Chen, P. Zhou, L. Gao, and J. Xu. Adaptive fog configuration for the industrial internet of things. IEEE Transactions on Industrial Informatics, 14(10):4656-4664, 2018. W. Chen, C. Liang, Y. Wan, C. Gao, G. Wu, J. Wei, and T. Huang. More: A model-driven operation service for cloud-based it systems. In 2016 IEEE International Conference on Services Computing (SCC), pages 633-640. IEEE, 2016. A. M. K. Cheng. Self-stabilizing real-time rule-based systems. In Proceedings 11th Symposium on Reliable Distributed Systems, pages 172-173, Houston, 1992. IEEE Computer Society. B. Cheng, A. Papageorgiou, F. Cirillo, and E. Kovacs. Geelytics: Geodistributed edge analytics for large scale iot systems based on dynamic topology. In 2015 IEEE 2ndWorld Forum on Internet of Things (WF-IoT), pages 565-570. IEEE, 2015. B. H. Cheng, K. I. Eder, M. Gogolla, L. Grunske, M. Litoiu, H. A. Müller, P. Pelliccione, A. Perini, N. A. Qureshi, B. Rumpe, et al. Using models at runtime to address assurance for self-adaptive systems. In Models@ run. time, pages 101-136. Springer, 2014. L. Cianciaruso, F. di Forenza, E. Di Nitto, M. Miglierina, N. Ferry, and A. Solberg. Using models at runtime to support adaptable monitoring of multiclouds applications. In 2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, pages 401-408. IEEE, 2014. F. Ciccozzi and R. Spalazzese. Mde4iot: supporting the internet of things with model-driven engineering. In International Symposium on Intelligent and Distributed Computing, pages 67-76. Springer, 2016. S. Cirani, L. Davoli, G. Ferrari, R. Léone, P. Medagliani, M. Picone, and L. Veltri. A scalable and self-configuring architecture for service discovery in the internet of things. IEEE Internet of Things Journal, 1(5):508-521, 2014. J. Colistra. The evolving architecture of smart cities. 2018 IEEE International Smart Cities Conference, ISC2 2018, 2019. B. Costa, P. F. Pires, F. C. Delicato, W. Li, and A. Y. Zomaya. Design and analysis of iot applications: A model-driven approach. In 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/Data-Com/CyberSciTech), pages 392-399. IEEE, 2016. K. Cui, W. Sun, and W. Sun. Joint computation offloading and resource management for usvs cluster of fog-cloud computing architecture. In 2019 IEEE International Conference on Smart Internet of Things (SmartIoT), pages 92-99. IEEE, 2019. K. Czarnecki. Overview of generative software development. In International Workshop on Unconventional Programming Paradigms, pages 326-341. Springer, 2004. M. S. de Brito, S. Hoque, T. Magedanz, R. Steinke, A. Willner, D. Nehls, O. Keils, and F. Schreiner. A service orchestration architecture for fog-enabled infrastructures. In 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC), pages 127-132. IEEE, 2017. G.-C. Deng and K. Wang. An application-aware qos routing algorithm for sdn-based iot networking. In 2018 IEEE Symposium on Computers and Communications (ISCC), pages 00186-00191. IEEE, 2018. K. Desikan, M. Srinivasan, and C. Murthy. A novel distributed latency-aware data processing in fog computing-enabled iot networks. In Proceedings of the ACM Workshop on Distributed Information Processing in Wireless Networks, page 4. ACM, 2017. X. E. DevOps. Best practices for devops: Advanced deployment patterns. urlhttps://www.wsta.org/wp-content/uploads/2018/09/Best-Practicesfor-DevOps-Advanced-Deployment-Patterns.pdf, 2018. J. Dizdarevi, F. Carpio, A. Jukan, and X. Masip-Bruin. A survey of communication protocols for internet of things and related challenges of fog and cloud computing integration. ACM Computing Surveys (CSUR), 51(6):1-29, 2019. Á. Domingo, J. Echeverría, Ó. Pastor, and C. Cetina. Comparing uml-based and dsl-based modeling from subjective and objective perspectives. In International Conference on Advanced Information Systems Engineering, pages 483-498. Springer, 2021. M. Duggan, K. Mason, J. Duggan, E. Howley, and E. Barrett. Predicting host cpu utilization in cloud computing using recurrent neural networks. In 2017 12th international conference for internet technology and secured transactions (ICITST), pages 67-72. IEEE, 2017. S. Dustdar, C. Avasalcai, and I. Murturi. Edge and fog computing: Vision and research challenges. In 2019 IEEE Int. Conf. on Service-Oriented System Engineering (SOSE), pages 96-9609. IEEE, 2019. C. Ebert, G. Gallardo, J. Hernantes, and N. Serrano. Devops. Ieee Software, 33(3):94-100, 2016. L. Erazo-Garzón, P. Cedillo, G. Rossi, and J. Moyano. A domain-specific language for modeling iot system architectures that support monitoring. IEEE Access, 10:61639-61665, 2022. J. Erbel, F. Korte, and J. Grabowski. Comparison and runtime adaptation of cloud application topologies based on occi. In CLOSER, pages 517-525, 2018. D. Ernst, A. Becker, and S. Tai. Rapid canary assessment through proxying and two-stage load balancing. In 2019 IEEE International Conference on Software Architecture Companion (ICSA-C), pages 116-122. IEEE, 2019. T. Eterovic, E. Kaljic, D. Donko, A. Salihbegovic, and S. Ribic. An internet of things visual domain specific modeling language based on uml. In 2015 XXV International Conference on Information, Communication and Automation Technologies (ICAT), pages 1-5. IEEE, 2015. I. G. EV. International energy agency: Paris, 2018. N. Farnaaz and M. Jabbar. Random forest modeling for network intrusion detection system. Procedia Computer Science, 89:213-217, 2016. N. Ferry, F. Chauvel, H. Song, A. Rossini, M. Lushpenko, and A. Solberg. Cloudmf: Model-driven management of multi-cloud applications. ACM Transactions on Internet Technology (TOIT), 18(2):1-24, 2018. N. Ferry, H. Song, A. Rossini, F. Chauvel, and A. Solberg. Cloudmf: applying mde to tame the complexity of managing multi-cloud applications. In 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, pages 269-277. IEEE, 2014. H. Flores, X. Su, V. Kostakos, A. Y. Ding, P. Nurmi, S. Tarkoma, P. Hui, and Y. Li. Large-scale offloading in the internet of things. In 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pages 479-484. IEEE, 2017. F. Fouquet, E. Daubert, N. Plouzeau, O. Barais, J. Bourcier, and J.-M. Jézéquel. Dissemination of reconfiguration policies on mesh networks. In IFIP International Conference on Distributed Applications and Interoperable Systems, pages 16-30. Springer, 2012. M. Fowler. UML distilled: a brief guide to the standard object modeling language. Addison-Wesley Professional, 2004. S. Ganapathy, K. Kulothungan, S. Muthurajkumar, M. Vijayalakshmi, P. Yogesh, and A. Kannan. Intelligent feature selection and classification techniques for intrusion detection in networks: a survey. EURASIP Journal on Wireless Communications and Networking, 2013(1):1-16, 2013. C. G. García, D. Meana-Llorián, V. García-Díaz, A. C. Jiménez, and J. P. Anzola. Midgar: Creation of a graphic domain-specific language to generate smart objects for internet of things scenarios using model-driven engineering. IEEE Access, 8:141872-141894, 2020. D. Garlan, S.-W. Cheng, A.-C. Huang, B. Schmerl, and P. Steenkiste. Rainbow: Architecture-based self-adaptation with reusable infrastructure. Computer, 37(10):46-54, 2004. D. Garlan, B. Schmerl, and S.-W. Cheng. Software architecture-based selfadaptation. In Autonomic computing and networking, pages 31-55. Springer, 2009. O. Gheibi, D.Weyns, and F. Quin. Applying machine learning in self-adaptive systems: A systematic literature review. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 15(3):1-37, 2021. N. K. Giang, R. Lea, M. Blackstock, and V. C. Leung. Fog at the edge: Experiences building an edge computing platform. In 2018 IEEE International Conference on Edge Computing (EDGE), pages 9-16. IEEE, 2018. T. Gomes, P. Lopes, J. Alves, P. Mestre, J. Cabral, J. L. Monteiro, and A. Tavares. A modeling domain-specific language for iot-enabled operating systems. In IECON 2017-43rd Annual Conference of the IEEE Industrial Electronics Society, pages 3945-3950. IEEE, 2017. A. Gómez, M. Iglesias-Urkia, L. Belategi, X. Mendialdua, and J. Cabot. Model-driven development of asynchronous message-driven architectures with asyncapi. Software and Systems Modeling, pages 1-29, 2021. A. Gómez, M. Iglesias-Urkia, A. Urbieta, and J. Cabot. A model-based approach for developing event-driven architectures with asyncapi. In Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, pages 121-131, 2020. J. Greenfield and K. Short. Software factories: assembling applications with patterns, models, frameworks and tools. In Companion of the 18th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications, pages 16-27, 2003. S. Gregor and A. R. Hevner. Positioning and presenting design science research for maximum impact. MIS quarterly, pages 337-355, 2013. J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami. Internet of things (iot): A vision, architectural elements, and future directions. Future generation computer systems, 29(7):1645-1660, 2013. R. Guntha. Iot architectures for noninvasive blood glucose and blood pressure monitoring. In 2019 9th International Symposium on Embedded Computing and System Design (ISED), pages 1-5. IEEE, 2019. Z. Guo, Y. Sun, S.-Y. Pan, and P.-C. Chiang. Integration of green energy and advanced energy-efficient technologies for municipal wastewater treatment plants. International journal of environmental research and public health, 16(7):1282, 2019. A. Hagedorn, D. Starobinski, and A. Trachtenberg. Rateless deluge: Over-the-air programming of wireless sensor networks using random linear codes. In 2008 International Conference on Information Processing in Sensor Networks (ipsn 2008), pages 457-466. IEEE, 2008. T. Holmes. Facilitating migration of cloud infrastructure services: A modelbased approach. In CloudMDE@ MoDELS, pages 7-12, 2015. G. Huang, G.-B. Huang, S. Song, and K. You. Trends in extreme learning machines: A review. Neural Networks, 61:32-48, 2015. M. Hussein, S. Li, and A. Radermacher. Model-driven development of adaptive iot systems. In MODELS (Satellite Events), pages 17-23, 2017. J. Ingeno. Software Architect's Handbook: Become a successful software architect by implementing effective architecture concepts. Packt Publishing Ltd, 2018. J. Islam, E. Harjula, T. Kumar, P. Karhula, and M. Ylianttila. Docker enabled virtualized nanoservices for local IoT edge networks. In IEEE Conf. on Standards for Communications and Networking (CSCN), pages 1-7, 2019. S. Y. Jang, Y. Lee, B. Shin, and D. Lee. Towards application-aware virtualization for edge iot clouds. In Proceedings of the 13th International Conference on Future Internet Technologies, page 4. ACM, 2018. N. Jazdi. Cyber physical systems in the context of industry 4.0. In IEEE Int. Conference on Automation, Quality and Testing, Robotics, pages 1-4. IEEE, 2014. P. G. Jeya, M. Ravichandran, and C. Ravichandran. Efficient classifier for r2l and u2r attacks. International Journal of Computer Applications, 45(21):28-32, 2012. Y. Jiang, Z. Huang, and D. H. Tsang. Challenges and solutions in fog computing orchestration. IEEE Network, 32(3):122-129, 2017. M. Jutila. An adaptive edge router enabling internet of things. IEEE Internet of Things Journal, 3(6):1061-1069, 2016. S. Keele et al. Guidelines for performing systematic literature reviews in software engineering. Technical report, Technical report, Ver. 2.3 EBSE Technical Report. EBSE, 2007. J. O. Kephart and D. M. Chess. The vision of autonomic computing. Computer, 36(1):41-50, 2003. D. Kimovski, H. Ijaz, N. Saurabh, and R. Prodan. Adaptive nature-inspired fog architecture. In 2018 IEEE 2nd International Conference on Fog and Edge Computing (ICFEC), pages 1-8. IEEE, 2018. B. Kitchenham, O. P. Brereton, D. Budgen, M. Turner, J. Bailey, and S. Linkman. Systematic literature reviews in software engineering a systematic literature review. Information and software technology, 51(1):7-15, 2009. B. Kitchenham and P. Brereton. A systematic review of systematic review process research in software engineering. Information and software technology, 55(12):2049-2075, 2013. P. Knights and B. Scanlan. A study of mining fatalities and coal price variation. International Journal of Mining Science and Technology, 29(4):599-602, 2019. C. Krupitzer, F. M. Roth, S. VanSyckel, G. Schiele, and C. Becker. A survey on engineering approaches for self-adaptive systems. Pervasive and Mobile Computing, 17:184-206, 2015. A. Latifah, S. H. Supangkat, and A. Ramelan. Smart building: A literature review. In Int. Conf. on ICT for Smart Society (ICISS), pages 1-6, 2020. E. Lee, Y.-D. Seo, and Y.-G. Kim. Self-adaptive framework based on mape loop for internet of things. sensors, 19(13):2996, 2019. X. Li, D. Li, J.Wan, C. Liu, and M. Imran. Adaptive transmission optimization in sdn-based industrial internet of things with edge computing. IEEE Internet of Things Journal, 5(3):1351-1360, 2018. Y. Li, Y.-h. Chiu, and T.-Y. Lin. Coal production efficiency and land destruction in china's coal mining industry. Resources Policy, 63:101449, 2019. Y. Liao, E. d. F. R. Loures, and F. Deschamps. Industrial internet of things: A systematic literature review and insights. IEEE Internet of Things Journal, 5(6):4515-4525, 2018. B. Lorenzo, J. Garcia-Rois, X. Li, J. Gonzalez-Castano, and Y. Fang. A robust dynamic edge network architecture for the internet of things. IEEE Network, 32(1):8-15, 2018. S. Madakam, V. Lake, V. Lake, V. Lake, et al. Internet of things (iot): A literature review. Journal of Computer and Communications, 3(05):164, 2015. S. Mahdavi-Hezavehi, P. Avgeriou, and D.Weyns. A classification framework of uncertainty in architecture-based self-adaptive systems with multiple quality requirements. In Managing Trade-Offs in Adaptable Software Architectures, pages 45-77. Elsevier, 2017. S. T. March and G. F. Smith. Design and natural science research on information technology. Decision support systems, 15(4):251-266, 1995. S. Martínez, A. Fouche, S. Gérard, and J. Cabot. Automatic generation of security compliant (virtual) model views. In International Conference on Conceptual Modeling, pages 109-117. Springer, 2018. J. Mass, C. Chang, and S. N. Srirama. Context-aware edge process management for mobile thing-to-fog environment. In Proceedings of the 12th European Conference on Software Architecture: Companion Proceedings, pages 1-7, 2018. A. Mavromatis, A. P. Da Silva, K. Kondepu, D. Gkounis, R. Nejabati, and D. Simeonidou. A software defined device provisioning framework facilitating scalability in internet of things. In 2018 IEEE 5G World Forum (5GWF), pages 446-451. IEEE, 2018. C. Mechalikh, H. Taktak, and F. Moussa. A scalable and adaptive tasks orchestration platform for iot. In 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), pages 1557-1563. IEEE, 2019. B. Mishra and A. Kertesz. The use of mqtt in m2m and iot systems: A survey. IEEE Access, 8:201071-201086, 2020. M. T. Moghaddam, E. Rutten, P. Lalanda, and G. Giraud. Ias: an iot architectural self-adaptation framework. In European Conference on Software Architecture, pages 333-351. Springer, 2020. D. Montero and R. Serral-Gracià. Offloading personal security applications to the network edge: A mobile user case scenario. In 2016 International Wireless Communications and Mobile Computing Conference (IWCMC), pages 96-101. IEEE, 2016. R. Morabito and N. Beijar. A framework based on sdn and containers for dynamic service chains on iot gateways. In Proceedings of theWorkshop on Hot Topics in Container Networking and Networked Systems, pages 42-47. ACM, 2017. K. Morris. Infrastructure as code: managing servers in the cloud. " O'Reilly Media, Inc.", 2016. H. Muccini and M. Sharaf. Caps: Architecture description of situational aware cyber physical systems. In 2017 IEEE International Conference on Software Architecture (ICSA), pages 211-220. IEEE, 2017. H. Muccini, R. Spalazzese, M. T. Moghaddam, and M. Sharaf. Self-adaptive iot architectures: An emergency handling case study. In Proceedings of the 12th European Conference on Software Architecture: Companion Proceedings, pages 1-6, 2018. R. Muñoz, R. Vilalta, N. Yoshikane, R. Casellas, R. Martínez, T. Tsuritani, and I. Morita. Integration of iot, transport sdn, and edge/cloud computing for dynamic distribution of iot analytics and efficient use of network resources. Journal of Lightwave Technology, 36(7):1420-1428, 2018. D. Nandan Jha, K. Alwasel, A. Alshoshan, X. Huang, R. K. Naha, S. K. Battula, S. Garg, D. Puthal, P. James, A. Y. Zomaya, et al. Iotsim-edge: A simulation framework for modeling the behaviour of iot and edge computing environments. arXiv e-prints, pages arXiv 1910, 2019. J. Nielsen and T. K. Landauer. Amathematical model of the finding of usability problems. In Proc. of the INTERACT 93 and CHI 93 conf. on Human factors in computing systems, pages 206-213, 1993. C. Pahl, N. El Ioini, S. Helmer, and B. Lee. An architecture pattern for trusted orchestration in iot edge clouds. In 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC), pages 63-70. IEEE, 2018. C. Pahl and B. Lee. Containers and clusters for edge cloud architectures a technology review. In 2015 3rd international conference on future internet of things and cloud, pages 379-386. IEEE, 2015. P. Patel, M. I. Ali, and A. Sheth. On using the intelligent edge for iot analytics. IEEE Intelligent Systems, 32(5):64-69, 2017. P. Patel and D. Cassou. Enabling high-level application development for the internet of things. Journal of Systems and Software, 103:62-84, 2015. K. Peffers, T. Tuunanen, M. A. Rothenberger, and S. Chatterjee. A design science research methodology for information systems research. Journal of management information systems, 24(3):45-77, 2007. S. Peros, H. Janjua, S. Akkermans, W. Joosen, and D. Hughes. Dynamic qos support for iot backhaul networks through sdn. In 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC), pages 187-192. IEEE, 2018. N. Petrovic and M. Tosic. Smada-fog: Semantic model driven approach to deployment and adaptivity in fog computing. Simulation Modelling Practice and Theory, 101:102033, 2020. D. Pizzolli, G. Cossu, D. Santoro, L. Capra, C. Dupont, D. Charalampos, F. De Pellegrini, F. Antonelli, and S. Cretti. Cloud4iot: A heterogeneous, distributed and autonomic cloud platform for the iot. In 2016 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pages 476-479. IEEE, 2016. S. Prabavathy, K. Sundarakantham, and S. M. Shalinie. Design of cognitive fog computing for intrusion detection in internet of things. Journal of Communications and Networks, 20(3):291-298, 2018. F. Pramudianto, M. Eisenhauer, C. A. Kamienski, D. Sadok, and E. J. Souto. Connecting the internet of things rapidly through a model driven approach. In 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), pages 135-140. IEEE, 2016. T. Rausch, S. Nastic, and S. Dustdar. Emma: distributed qos-aware mqtt middleware for edge computing applications. In 2018 IEEE International Conference on Cloud Engineering (IC2E), pages 191-197. IEEE, 2018. M. d. M. y. E. Republica de Colombia. Reglamento de seguridad en labores minera subterráneas, 2015. A. Rhayem, M. B. A. Mhiri, and F. Gargouri. Semantic web technologies for the internet of things: Systematic literature review. Internet of Things, page 100206, 2020. J. Rubin and D. Chisnell. Handbook of usability testing: how to plan, design and conduct effective tests. John Wiley & Sons, New Jersey, 2008. E. Rutten, N. Marchand, and D. Simon. Feedback control as mape-k loop in autonomic computing. In Software Engineering for Self-Adaptive Systems III. Assurances, pages 349-373. Springer, 2017. A. Salihbegovic, T. Eterovic, E. Kaljic, and S. Ribic. Design of a domain specific language and ide for internet of things applications. In 2015 38th international convention on information and communication technology, electronics and microelectronics (MIPRO), pages 996-1001. IEEE, 2015. H. Sami and A. Mourad. Towards dynamic on-demand fog computing formation based on containerization technology. In 2018 International Conference on Computational Science and Computational Intelligence (CSCI), pages 960-965. IEEE, 2018. J. Sandobalin, E. Insfran, and S. Abrahão. Argon: A model-driven infrastructure provisioning tool. In 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), pages 738-742. IEEE, 2019. J. Santos, T.Wauters, B. Volckaert, and F. De Turck. Fog computing: Enabling the management and orchestration of smart city applications in 5g networks. Entropy, 20(1):4, 2018. J. Santos, T.Wauters, B. Volckaert, and F. De Turck. Resource provisioning in fog computing: From theory to practice. Sensors, 19(10):2238, 2019. B. Sarma, G. Kumar, R. Kumar, and T. Tuithung. Fog computing: An enhanced performance analysis emulation framework for iot with load balancing smart gateway architecture. In 2019 International Conference on Communication and Electronics Systems (ICCES), pages 1-5. IEEE, 2019. M. Satyanarayanan. The emergence of edge computing. Computer, 50(1):30-39, 2017. R. Scolati, I. Fronza, N. El Ioini, A. Samir, and C. Pahl. A containerized big data streaming architecture for edge cloud computing on clustered singleboard devices. In 9th Int. Conf. on Cloud Computing and Services Science, pages 68-80, 2019. M. K. M. Shapi, N. A. Ramli, and L. J. Awalin. Energy consumption prediction by using machine learning for smart building: Case study in malaysia. Developments in the Built Environment, 5:100037, 2021. W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu. Edge computing: Vision and challenges. IEEE internet of things journal, 3(5):637-646, 2016. M. Singh and G. Baranwal. Quality of service (qos) in internet of things. In 2018 3rd International Conference On Internet of Things: Smart Innovation and Usages (IoT-SIU), pages 1-6, Feb 2018. S. Singh and N. Singh. Containers & docker: Emerging roles & future of cloud technology. In 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), pages 804-807. IEEE, 2016. O. Skarlat, V. Karagiannis, T. Rausch, K. Bachmann, and S. Schulte. A framework for optimization, service placement, and runtime operation in the fog. In 2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC), pages 164-173. IEEE, 2018. K. Sledziewski, B. Bordbar, and R. Anane. A dsl-based approach to software development and deployment on cloud. In 2010 24th IEEE International Conference on Advanced Information Networking and Applications, pages 414-421. IEEE, 2010. D. Steinberg, F. Budinsky, E. Merks, and M. Paternostro. EMF: eclipse modeling framework. Pearson Education, 2008. T. Suganuma, T. Oide, S. Kitagami, K. Sugawara, and N. Shiratori. Multiagentbased flexible edge computing architecture for iot. IEEE Network, 32(1):16-23, 2018. V. Theodorou and N. Diamantopoulos. Glt: Edge gateway elt for data-driven intelligence placement. In 2019 IEEE/ACM Joint 4th International Workshop on Rapid Continuous Software Engineering and 1st International Workshop on Data-Driven Decisions, Experimentation and Evolution (RCoSE/DDrEE), pages 24-27. IEEE, 2019. J. R. Torres Neto, G. P. Rocha Filho, L. Y. Mano, L. A. Villas, and J. Ueyama. Exploiting offloading in iot-based microfog: experiments with face recognition and fall detection. Wireless Communications and Mobile Computing, 2019, 2019. C.-L. Tseng and F. J. Lin. Extending scalability of iot/m2m platforms with fog computing. In 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), pages 825-830. IEEE, 2018. I. T. Union. Internet of things global standards initiative, 2012. M. F. van Amstel, M. G. van den Brand, and P. H. Nguyen. Metrics for model transformations. In Proceedings of the Ninth Belgian-Netherlands Software Evolution Workshop (BENEVOL 2010), Lille, France (December 2010), 2010. A. Van Deursen, P. Klint, and J. Visser. Domain-specific languages: An annotated bibliography. ACM Sigplan Notices, 35(6):26-36, 2000. K. Velasquez, D. P. Abreu, D. Gonçalves, L. Bittencourt, M. Curado, E. Monteiro, and E. Madeira. Service orchestration in fog environments. In 2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud), pages 329-336. IEEE, 2017. R. Vilalta, C. V. Apte, J. L. Hellerstein, S. Ma, and S. M. Weiss. Predictive algorithms in the management of computer systems. IBM Systems Journal, 41(3):461-474, 2002. M. Villari, M. Fazio, S. Dustdar, O. Rana, and R. Ranjan. Osmotic computing: A new paradigm for edge/cloud integration. IEEE Cloud Computing, 3(6):76-83, 2016. M. Voelter. Embedded software development with projectional language workbenches. In International Conference on Model Driven Engineering Languages and Systems, pages 32-46. Springer, 2010. M. Völter. Language and ide modularization, extension and composition with mps. Pre-proceedings of Summer School on Generative and Transformational Techniques in Software Engineering (GTTSE), pages 395-431, 2011. J.Wang, J. Pan, and F. Esposito. Elastic urban video surveillance system using edge computing. In Proceedings of the Workshop on Smart Internet of Things, page 7. ACM, 2017. Z. Wen, R. Yang, P. Garraghan, T. Lin, J. Xu, and M. Rovatsos. Fog orchestration for internet of things services. IEEE Internet Computing, 21(2):16-24, 2017. D. Weyns, M. U. Iftikhar, D. Hughes, and N. Matthys. Applying architecturebased adaptation to automate the management of internet-of-things. In European Conf. on Software Architecture, pages 49-67, 2018. WINSYSTEMS. Cloud, fog and edge computing ¿What's the difference? urlhttps://www.winsystems.com/cloud-fog-and-edge-computingwhats-the-difference/, 2017. T.Wong, M.Wagner, and C. Treude. Self-adaptive systems: Asystematic literature review across categories and domains. arXiv preprint arXiv:2101.00125, 2021. B. Wood and A. Azim. Triton: a domain specific language for cyber-physical systems. In 2021 22nd IEEE International Conference on Industrial Technology (ICIT), volume 1, pages 810-816. IEEE, 2021. World Wide Web Consortium (W3C). Semantic sensor network ontology. URL: https://www.w3.org/TR/2017/REC-vocab-ssn-20171019/, 10 2017. D. Wu, M. M. Omwenga, Y. Liang, L. Yang, D. Huston, and T. Xia. A fog computing framework for cognitive portable ground penetrating radars. In ICC 2019-2019 IEEE International Conference on Communications (ICC), pages 1-6. IEEE, 2019. B. Wukkadada, K. Wankhede, R. Nambiar, and A. Nair. Comparison with http and mqtt in internet of things (iot). In 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), pages 249-253. IEEE, 2018. B. Yang, A. Sailer, S. Jain, A. E. Tomala-Reyes, M. Singh, and A. Ramnath. Service discovery based blue-green deployment technique in cloud native environments. In 2018 IEEE International Conference on Services Computing (SCC), pages 185-192. IEEE, 2018. M. B. Yassein, M. Q. Shatnawi, S. Aljwarneh, and R. Al-Hatmi. Internet of things: Survey and open issues of mqtt protocol. In 2017 international conference on engineering & MIS (ICEMIS), pages 1-6. Ieee, 2017. E. Yigitoglu, M. Mohamed, L. Liu, and H. Ludwig. Foggy: a framework for continuous automated iot application deployment in fog computing. In 2017 IEEE International Conference on AI & Mobile Services (AIMS), pages 38-45. IEEE, 2017. R. Young, S. Fallon, and P. Jacob. Dynamic collaboration of centralized & edge processing for coordinated data management in an iot paradigm. In 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA), pages 694-701. IEEE, 2018. R. Young, S. Fallon, and P. Jacob. A governance architecture for self-adaption & control in iot applications. In 2018 5th International Conference on Control, Decision and Information Technologies (CoDIT), pages 241-46. IEEE, 2018. A. Yousefpour, A. Patil, G. Ishigaki, I. Kim, X.Wang, H. C. Cankaya, Q. Zhang, W. Xie, and J. P. Jue. Fogplan: a lightweight qos-aware dynamic fog service provisioning framework. IEEE Internet of Things Journal, 6(3):5080-5096, 2019.
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spelling	Attribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Cabot Sagrera, Jordi0a5c9348-d1d0-4a4c-8552-2f0b275ee4a6600Garcés Pernett, Kelly Johanyvirtual::16484-1Castro Barrera, Harold Enriquevirtual::16485-1Alfonso Díaz, Iván David4d39eb7f-97f5-4f27-a5b8-baba5dfe0dea600Capilla Sevilla, RafaelGuilherme, Horta TravassosLozano Garzón, Carlos AndrésTICSw-Tecnologías de Información y Construcción de SoftwareSOM Research Lab2022-12-14T20:47:45Z2022-12-14T20:47:45Z2022-12-05http://hdl.handle.net/1992/6356110.57784/1992/63561instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/Documento de tesis doctoral acerca del desarrollo de una solución basada en modelado para dar soporte en el diseño, despliegue, y autoadaptación de sistemas IoT multicapa.In recent years, the Internet of Things (IoT) has expanded its fields and areas of application, becoming a key component in industrial processes and even in the activities we perform daily. The growth of IoT has generated increasingly restrictive requirements, mainly in systems that analyze information in real time. Similarly, IoT system architectures have evolved to implement new strategies and patterns (such as edge and fog computing) to meet system requirements. Traditionally, an IoT system was composed of two layers: the device layer (sensors and actuators) and the cloud layer for information processing and storage. Today, most IoT systems leverage edge and fog computing to bring computation and storage closer to the device layer, decreasing bandwidth consumption and latency. Although the use of these multi-layer architectures can improve performance, it is challenging to design them because the dynamic and changing IoT environment can impact Quality of Service (QoS) and system operation. IoT systems are often exposed to changing environments that induce unexpected runtime events such as signal strength instability, latency growth and software failures. To cope with these events, system adaptations should be automatically executed at runtime, i.e., IoT systems should have self-adaptation capabilities. In this sense, better support in the design, deployment, and self-adaptation stages of multilayer IoT systems is needed. However, the tools and solutions found in the literature do not address the complexity of multi-layered IoT systems, and the languages for specifying the adaptation rules that govern the system at runtime are limited. Therefore, we propose a modeling-based approach that addresses the limitations of existing studies to support the design, deployment, and management of self-adaptive IoT systems. Our solution is divided into two stages: Modeling (design time): to support the design tasks, we propose a Domain Specific Language (DSL) that enables to specify the multi-layered architecture of the IoT system, the deployment of container-based applications, and rules for the self-adaptation at runtime. Additionally, we design a code generator that produces YAML manifests for the deployment and management of the IoT system at runtime. Self-adaptation (runtime): we have designed a framework based on the MAPE-K loop to monitor and adapt the IoT system following the rules specified in the model (design time). The deployment and configuration of the tools and technologies used by this framework is performed using the YAML manifests produced by the code generator. Additionally, we have designed two extensions to our DSL. The first one is an extension focused on modeling IoT systems deployed in the underground coal mining industry. This DSL addresses new mining domain concepts such as mine structure specification and control points. The second DSL extension is focused on modeling IoT systems deployed in Wastewater Treatment Plants (WWTPs). This DSL extension addresses the modeling of the WWTP process block diagram using a graphical notation. Even with these two DSL extensions, there is no need to modify our framework that manages system adaptation at runtime. Finally, we have conducted experimental studies classified into three groups: (1) we validated the expressiveness and usability of the DSL through experiments with 13 participants who performed modeling exercises (using the DSL) and answered surveys reporting their experience; (2) we functionally validated the architectural adaptations using our framework and comparing the performance and availability between a non-self-adaptive system versus a self-adaptive system implementing our approach; (3) finally, we evaluate the ability and performance of our framework to address the growth of concurrent adaptations on an IoT system. The results of these experiments demonstrated that (1) the DSL has the expressiveness to model multi-layered IoT systems (including its self-adaptive behaviour) and the learning curve is favorable; (2) functional tests demonstrated how the performance and availability of the system improves when using our approach; and (3) we have identified scalability limitations of the framework and proposed insights to address them.Minciencias - Beca BicentenarioSOM Research Lab - Transact ProjectDoctor en IngenieríaDoctorado197 páginasapplication/pdfengUniversidad de los AndesDoctorado en IngenieríaFacultad de IngenieríaDepartamento de Ingeniería Sistemas y ComputaciónIoT@run-time: a model-based approach to support deployment and self-adaptations in IoT systemsTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttps://purl.org/redcol/resource_type/TDInternet of ThingsModel-Driven EngineeringSelf-Adaptive SystemDomain Specific LanguageEdge ComputingFog Computing.IngenieríaF. Ahmadighohandizi and K. Systä. Application development and deployment for iot devices. In European Conference on Service-Oriented and Cloud Computing, pages 74-85. Springer, 2016.A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash. Internet of things: A survey on enabling technologies, protocols, and applications. IEEE communications surveys & tutorials, 17(4):2347-2376, 2015.S. Al-Sarawi, M. Anbar, K. Alieyan, and M. Alzubaidi. Internet of things (iot) communication protocols. In 2017 8th International conference on information technology (ICIT), pages 685-690. IEEE, 2017.M. Alaa, A. A. Zaidan, B. B. Zaidan, M. Talal, and M. L. M. Kiah. A review of smart home applications based on internet of things. Journal of Network and Computer Applications, 97:48-65, 2017.A. H. Alavi, P. Jiao, W. G. Buttlar, and N. Lajnef. Internet of things-enabled smart cities: State-of-the-art and future trends. Measurement, 129:589-606, 2018.I. Alfonso, K. Garcés, H. Castro, and J. Cabot. Self-adaptive architectures in IoT systems: a systematic literature review. Journal of Internet Services and Applications, 12(1):1-28, 2021.I. Alfonso, K. Garcés, H. Castro, and J. Cabot. Modeling self-adaptative IoT architectures. In 2021 ACM/IEEE Int. Conf. on Model Driven Engineering Languages and Systems Companion (MODELS-C), pages 761-766, 2021.I. Alfonso, C. Goméz, K. Garcés, and J. Chavarriaga. Lifetime optimization of wireless sensor networks for gas monitoring in underground coal mining. In 2018 7th International Conference on Computers Communications and Control (ICCCC), pages 224-230. IEEE, 2018.F. Alkhabbas, I. Murturi, R. Spalazzese, P. Davidsson, and S. Dustdar. A goaldriven approach for deploying self-adaptive iot systems. In 2020 IEEE International Conference on Software Architecture (ICSA), pages 146-156. IEEE, 2020.M. Alrowaily and Z. Lu. Secure edge computing in iot systems: Review and case studies. In 2018 IEEE/ACM Symposium on Edge Computing (SEC), pages 440-444. IEEE, 2018.M. Artac, T. Borovak, E. Di Nitto, M. Guerriero, D. Perez-Palacin, and D. A. Tamburri. Infrastructure-as-code for data-intensive architectures: A modeldriven development approach. In 2018 IEEE International Conference on Software Architecture (ICSA), pages 156-15609. IEEE, 2018.K. Ashton et al. That "internet of things" thing. RFID journal, 22(7):97-114, 2009.M.Asif-Ur-Rahman, F. Afsana, M. Mahmud, M. S. Kaiser, M. R. Ahmed, O. Kaiwartya, and A. James-Taylor. Toward a heterogeneous mist, fog, and cloudbased framework for the internet of healthcare things. IEEE Internet of Things Journal, 6(3):4049-4062, 2018.U. Aßmann, S. Götz, J.-M. Jézéquel, B. Morin, and M. Trapp. A reference architecture and roadmap for models@ run. time systems. In Models@ runtime, pages 1-18. Springer, 2014.A. Barii, V. Amaral, and M. Goulão. Usability driven dsl development with use-me. Computer Languages, Systems & Structures, 51:118-157, 2018.J. A. Barriga, P. J. Clemente, E. Sosa-Sánchez, and Á. E. Prieto. Simulateiot: Domain specific language to design, code generation and execute iot simulation environments. IEEE Access, 9:92531-92552, 2021.L. Bass, P. Clements, and R. Kazman. Software architecture in practice. Addison-Wesley Professional, 2003.L. Bass, I. Weber, and L. Zhu. DevOps: A software architect's perspective. Addison-Wesley Professional, 2015.J. Beauquier, B. Bérard, and L. Fribourg. A new rewrite method for proving convergence of self-stabilizing systems. In International Symposium on Distributed Computing, pages 240-255, Bratislava, 1999. Springer.I. Bedhief, L. Foschini, P. Bellavista, M. Kassar, and T. Aguili. Toward selfadaptive software defined fog networking architecture for iiot and industry 4.0. In 2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), pages 1-5. IEEE, 2019.N. Bencomo, R. B. France, B. H. Cheng, and U. Aßmann. Models@ runtime: foundations, applications, and roadmaps, volume 8378. Springer, 2014.N. Bencomo and L. H. G. Paucar. Ram: Causally-connected and requirements aware runtime models using bayesian learning. In 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS), pages 216-226. IEEE, 2019.T. Berger, M. Völter, H. P. Jensen, T. Dangprasert, and J. Siegmund. Efficiency of projectional editing: A controlled experiment. In Proc. of the 24th ACM SIGSOFT Int. Symposium on Foundations of Software Engineering, pages 763-774, 2016.A. Bergmayr, U. Breitenbücher, O. Kopp, M. Wimmer, G. Kappel, and F. Leymann. From architecture modeling to application provisioning for the cloud by combining uml and tosca. In CLOSER (2), pages 97-108, 2016.G. Blair, N. Bencomo, and R. B. France. Models@run.time. Computer, 42(10):22-27, 2009.M. Brambilla, J. Cabot, and M. Wimmer. Model-driven software engineering in practice, 2nd edn. Synthesis Lectures on Software Engineering. Morgan & Claypool Publishers, USA, 2017.M. Breitbach, D. Schäfer, J. Edinger, and C. Becker. Context-aware data and task placement in edge computing environments. In 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom), pages 1-10. IEEE, 2019.D. Bri, M. Fernández-Diego, M. Garcia, F. Ramos, and J. Lloret. How the weather impacts on the performance of an outdoor wlan. IEEE Communications Letters, 16(8):1184-1187, 2012.A. Bucchiarone, A. Cicchetti, F. Ciccozzi, and A. Pierantonio. Domain-specific Languages in Practice: With JetBrains MPS. Springer, 2021.R. Buyya and S. N. Srirama. Fog and edge computing: principles and paradigms. John Wiley & Sons, 2019.A. Cañete, M. Amor, and L. Fuentes. Supporting iot applications deployment on edge-based infrastructures using multi-layer feature models. Journal of Systems and Software, 183:111086, 2022.E. A. Castillo and A. Ahmadinia. Iot-based multi-view machine vision systems. In 2019 IEEE International Conference on Big Data (Big Data), pages 5206-5212. IEEE, 2019.A. Chehri, T. El Ouahmani, and N. Hakem. Mining and iot-based vehicle adhoc network: industry opportunities and innovation. Internet of Things, page 100117, 2019.L. Chen, P. Zhou, L. Gao, and J. Xu. Adaptive fog configuration for the industrial internet of things. IEEE Transactions on Industrial Informatics, 14(10):4656-4664, 2018.W. Chen, C. Liang, Y. Wan, C. Gao, G. Wu, J. Wei, and T. Huang. More: A model-driven operation service for cloud-based it systems. In 2016 IEEE International Conference on Services Computing (SCC), pages 633-640. IEEE, 2016.A. M. K. Cheng. Self-stabilizing real-time rule-based systems. In Proceedings 11th Symposium on Reliable Distributed Systems, pages 172-173, Houston, 1992. IEEE Computer Society.B. Cheng, A. Papageorgiou, F. Cirillo, and E. Kovacs. Geelytics: Geodistributed edge analytics for large scale iot systems based on dynamic topology. In 2015 IEEE 2ndWorld Forum on Internet of Things (WF-IoT), pages 565-570. IEEE, 2015.B. H. Cheng, K. I. Eder, M. Gogolla, L. Grunske, M. Litoiu, H. A. Müller, P. Pelliccione, A. Perini, N. A. Qureshi, B. Rumpe, et al. Using models at runtime to address assurance for self-adaptive systems. In Models@ run. time, pages 101-136. Springer, 2014.L. Cianciaruso, F. di Forenza, E. Di Nitto, M. Miglierina, N. Ferry, and A. Solberg. Using models at runtime to support adaptable monitoring of multiclouds applications. In 2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, pages 401-408. IEEE, 2014.F. Ciccozzi and R. Spalazzese. Mde4iot: supporting the internet of things with model-driven engineering. In International Symposium on Intelligent and Distributed Computing, pages 67-76. Springer, 2016.S. Cirani, L. Davoli, G. Ferrari, R. Léone, P. Medagliani, M. Picone, and L. Veltri. A scalable and self-configuring architecture for service discovery in the internet of things. IEEE Internet of Things Journal, 1(5):508-521, 2014.J. Colistra. The evolving architecture of smart cities. 2018 IEEE International Smart Cities Conference, ISC2 2018, 2019.B. Costa, P. F. Pires, F. C. Delicato, W. Li, and A. Y. Zomaya. Design and analysis of iot applications: A model-driven approach. In 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/Data-Com/CyberSciTech), pages 392-399. IEEE, 2016.K. Cui, W. Sun, and W. Sun. Joint computation offloading and resource management for usvs cluster of fog-cloud computing architecture. In 2019 IEEE International Conference on Smart Internet of Things (SmartIoT), pages 92-99. IEEE, 2019.K. Czarnecki. Overview of generative software development. In International Workshop on Unconventional Programming Paradigms, pages 326-341. Springer, 2004.M. S. de Brito, S. Hoque, T. Magedanz, R. Steinke, A. Willner, D. Nehls, O. Keils, and F. Schreiner. A service orchestration architecture for fog-enabled infrastructures. In 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC), pages 127-132. IEEE, 2017.G.-C. Deng and K. Wang. An application-aware qos routing algorithm for sdn-based iot networking. In 2018 IEEE Symposium on Computers and Communications (ISCC), pages 00186-00191. IEEE, 2018.K. Desikan, M. Srinivasan, and C. Murthy. A novel distributed latency-aware data processing in fog computing-enabled iot networks. In Proceedings of the ACM Workshop on Distributed Information Processing in Wireless Networks, page 4. ACM, 2017.X. E. DevOps. Best practices for devops: Advanced deployment patterns. urlhttps://www.wsta.org/wp-content/uploads/2018/09/Best-Practicesfor-DevOps-Advanced-Deployment-Patterns.pdf, 2018.J. Dizdarevi, F. Carpio, A. Jukan, and X. Masip-Bruin. A survey of communication protocols for internet of things and related challenges of fog and cloud computing integration. ACM Computing Surveys (CSUR), 51(6):1-29, 2019.Á. Domingo, J. Echeverría, Ó. Pastor, and C. Cetina. Comparing uml-based and dsl-based modeling from subjective and objective perspectives. In International Conference on Advanced Information Systems Engineering, pages 483-498. Springer, 2021.M. Duggan, K. Mason, J. Duggan, E. Howley, and E. Barrett. Predicting host cpu utilization in cloud computing using recurrent neural networks. In 2017 12th international conference for internet technology and secured transactions (ICITST), pages 67-72. IEEE, 2017.S. Dustdar, C. Avasalcai, and I. Murturi. Edge and fog computing: Vision and research challenges. In 2019 IEEE Int. Conf. on Service-Oriented System Engineering (SOSE), pages 96-9609. IEEE, 2019.C. Ebert, G. Gallardo, J. Hernantes, and N. Serrano. Devops. Ieee Software, 33(3):94-100, 2016.L. Erazo-Garzón, P. Cedillo, G. Rossi, and J. Moyano. A domain-specific language for modeling iot system architectures that support monitoring. IEEE Access, 10:61639-61665, 2022.J. Erbel, F. Korte, and J. Grabowski. Comparison and runtime adaptation of cloud application topologies based on occi. In CLOSER, pages 517-525, 2018.D. Ernst, A. Becker, and S. Tai. Rapid canary assessment through proxying and two-stage load balancing. In 2019 IEEE International Conference on Software Architecture Companion (ICSA-C), pages 116-122. IEEE, 2019.T. Eterovic, E. Kaljic, D. Donko, A. Salihbegovic, and S. Ribic. An internet of things visual domain specific modeling language based on uml. In 2015 XXV International Conference on Information, Communication and Automation Technologies (ICAT), pages 1-5. IEEE, 2015.I. G. EV. International energy agency: Paris, 2018.N. Farnaaz and M. Jabbar. Random forest modeling for network intrusion detection system. Procedia Computer Science, 89:213-217, 2016.N. Ferry, F. Chauvel, H. Song, A. Rossini, M. Lushpenko, and A. Solberg. Cloudmf: Model-driven management of multi-cloud applications. ACM Transactions on Internet Technology (TOIT), 18(2):1-24, 2018.N. Ferry, H. Song, A. Rossini, F. Chauvel, and A. Solberg. Cloudmf: applying mde to tame the complexity of managing multi-cloud applications. In 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, pages 269-277. IEEE, 2014.H. Flores, X. Su, V. Kostakos, A. Y. Ding, P. Nurmi, S. Tarkoma, P. Hui, and Y. Li. Large-scale offloading in the internet of things. In 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pages 479-484. IEEE, 2017.F. Fouquet, E. Daubert, N. Plouzeau, O. Barais, J. Bourcier, and J.-M. Jézéquel. Dissemination of reconfiguration policies on mesh networks. In IFIP International Conference on Distributed Applications and Interoperable Systems, pages 16-30. Springer, 2012.M. Fowler. UML distilled: a brief guide to the standard object modeling language. Addison-Wesley Professional, 2004.S. Ganapathy, K. Kulothungan, S. Muthurajkumar, M. Vijayalakshmi, P. Yogesh, and A. Kannan. Intelligent feature selection and classification techniques for intrusion detection in networks: a survey. EURASIP Journal on Wireless Communications and Networking, 2013(1):1-16, 2013.C. G. García, D. Meana-Llorián, V. García-Díaz, A. C. Jiménez, and J. P. Anzola. Midgar: Creation of a graphic domain-specific language to generate smart objects for internet of things scenarios using model-driven engineering. IEEE Access, 8:141872-141894, 2020.D. Garlan, S.-W. Cheng, A.-C. Huang, B. Schmerl, and P. Steenkiste. Rainbow: Architecture-based self-adaptation with reusable infrastructure. Computer, 37(10):46-54, 2004.D. Garlan, B. Schmerl, and S.-W. Cheng. Software architecture-based selfadaptation. In Autonomic computing and networking, pages 31-55. Springer, 2009.O. Gheibi, D.Weyns, and F. Quin. Applying machine learning in self-adaptive systems: A systematic literature review. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 15(3):1-37, 2021.N. K. Giang, R. Lea, M. Blackstock, and V. C. Leung. Fog at the edge: Experiences building an edge computing platform. In 2018 IEEE International Conference on Edge Computing (EDGE), pages 9-16. IEEE, 2018.T. Gomes, P. Lopes, J. Alves, P. Mestre, J. Cabral, J. L. Monteiro, and A. Tavares. A modeling domain-specific language for iot-enabled operating systems. In IECON 2017-43rd Annual Conference of the IEEE Industrial Electronics Society, pages 3945-3950. IEEE, 2017.A. Gómez, M. Iglesias-Urkia, L. Belategi, X. Mendialdua, and J. Cabot. Model-driven development of asynchronous message-driven architectures with asyncapi. Software and Systems Modeling, pages 1-29, 2021.A. Gómez, M. Iglesias-Urkia, A. Urbieta, and J. Cabot. A model-based approach for developing event-driven architectures with asyncapi. In Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, pages 121-131, 2020.J. Greenfield and K. Short. Software factories: assembling applications with patterns, models, frameworks and tools. In Companion of the 18th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications, pages 16-27, 2003.S. Gregor and A. R. Hevner. Positioning and presenting design science research for maximum impact. MIS quarterly, pages 337-355, 2013.J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami. Internet of things (iot): A vision, architectural elements, and future directions. Future generation computer systems, 29(7):1645-1660, 2013.R. Guntha. Iot architectures for noninvasive blood glucose and blood pressure monitoring. In 2019 9th International Symposium on Embedded Computing and System Design (ISED), pages 1-5. IEEE, 2019.Z. Guo, Y. Sun, S.-Y. Pan, and P.-C. Chiang. Integration of green energy and advanced energy-efficient technologies for municipal wastewater treatment plants. International journal of environmental research and public health, 16(7):1282, 2019.A. Hagedorn, D. Starobinski, and A. Trachtenberg. Rateless deluge: Over-the-air programming of wireless sensor networks using random linear codes. In 2008 International Conference on Information Processing in Sensor Networks (ipsn 2008), pages 457-466. IEEE, 2008.T. Holmes. Facilitating migration of cloud infrastructure services: A modelbased approach. In CloudMDE@ MoDELS, pages 7-12, 2015.G. Huang, G.-B. Huang, S. Song, and K. You. Trends in extreme learning machines: A review. Neural Networks, 61:32-48, 2015.M. Hussein, S. Li, and A. Radermacher. Model-driven development of adaptive iot systems. In MODELS (Satellite Events), pages 17-23, 2017.J. Ingeno. Software Architect's Handbook: Become a successful software architect by implementing effective architecture concepts. Packt Publishing Ltd, 2018.J. Islam, E. Harjula, T. Kumar, P. Karhula, and M. Ylianttila. Docker enabled virtualized nanoservices for local IoT edge networks. In IEEE Conf. on Standards for Communications and Networking (CSCN), pages 1-7, 2019.S. Y. Jang, Y. Lee, B. Shin, and D. Lee. Towards application-aware virtualization for edge iot clouds. In Proceedings of the 13th International Conference on Future Internet Technologies, page 4. ACM, 2018.N. Jazdi. Cyber physical systems in the context of industry 4.0. In IEEE Int. Conference on Automation, Quality and Testing, Robotics, pages 1-4. IEEE, 2014.P. G. Jeya, M. Ravichandran, and C. Ravichandran. Efficient classifier for r2l and u2r attacks. International Journal of Computer Applications, 45(21):28-32, 2012.Y. Jiang, Z. Huang, and D. H. Tsang. Challenges and solutions in fog computing orchestration. IEEE Network, 32(3):122-129, 2017.M. Jutila. An adaptive edge router enabling internet of things. IEEE Internet of Things Journal, 3(6):1061-1069, 2016.S. Keele et al. Guidelines for performing systematic literature reviews in software engineering. Technical report, Technical report, Ver. 2.3 EBSE Technical Report. EBSE, 2007.J. O. Kephart and D. M. Chess. The vision of autonomic computing. Computer, 36(1):41-50, 2003.D. Kimovski, H. Ijaz, N. Saurabh, and R. Prodan. Adaptive nature-inspired fog architecture. In 2018 IEEE 2nd International Conference on Fog and Edge Computing (ICFEC), pages 1-8. IEEE, 2018.B. Kitchenham, O. P. Brereton, D. Budgen, M. Turner, J. Bailey, and S. Linkman. Systematic literature reviews in software engineering a systematic literature review. Information and software technology, 51(1):7-15, 2009.B. Kitchenham and P. Brereton. A systematic review of systematic review process research in software engineering. Information and software technology, 55(12):2049-2075, 2013.P. Knights and B. Scanlan. A study of mining fatalities and coal price variation. International Journal of Mining Science and Technology, 29(4):599-602, 2019.C. Krupitzer, F. M. Roth, S. VanSyckel, G. Schiele, and C. Becker. A survey on engineering approaches for self-adaptive systems. Pervasive and Mobile Computing, 17:184-206, 2015.A. Latifah, S. H. Supangkat, and A. Ramelan. Smart building: A literature review. In Int. Conf. on ICT for Smart Society (ICISS), pages 1-6, 2020.E. Lee, Y.-D. Seo, and Y.-G. Kim. Self-adaptive framework based on mape loop for internet of things. sensors, 19(13):2996, 2019.X. Li, D. Li, J.Wan, C. Liu, and M. Imran. Adaptive transmission optimization in sdn-based industrial internet of things with edge computing. IEEE Internet of Things Journal, 5(3):1351-1360, 2018.Y. Li, Y.-h. Chiu, and T.-Y. Lin. Coal production efficiency and land destruction in china's coal mining industry. Resources Policy, 63:101449, 2019.Y. Liao, E. d. F. R. Loures, and F. Deschamps. Industrial internet of things: A systematic literature review and insights. IEEE Internet of Things Journal, 5(6):4515-4525, 2018.B. Lorenzo, J. Garcia-Rois, X. Li, J. Gonzalez-Castano, and Y. Fang. A robust dynamic edge network architecture for the internet of things. IEEE Network, 32(1):8-15, 2018.S. Madakam, V. Lake, V. Lake, V. Lake, et al. Internet of things (iot): A literature review. Journal of Computer and Communications, 3(05):164, 2015.S. Mahdavi-Hezavehi, P. Avgeriou, and D.Weyns. A classification framework of uncertainty in architecture-based self-adaptive systems with multiple quality requirements. In Managing Trade-Offs in Adaptable Software Architectures, pages 45-77. Elsevier, 2017.S. T. March and G. F. Smith. Design and natural science research on information technology. Decision support systems, 15(4):251-266, 1995.S. Martínez, A. Fouche, S. Gérard, and J. Cabot. Automatic generation of security compliant (virtual) model views. In International Conference on Conceptual Modeling, pages 109-117. Springer, 2018.J. Mass, C. Chang, and S. N. Srirama. Context-aware edge process management for mobile thing-to-fog environment. In Proceedings of the 12th European Conference on Software Architecture: Companion Proceedings, pages 1-7, 2018.A. Mavromatis, A. P. Da Silva, K. Kondepu, D. Gkounis, R. Nejabati, and D. Simeonidou. A software defined device provisioning framework facilitating scalability in internet of things. In 2018 IEEE 5G World Forum (5GWF), pages 446-451. IEEE, 2018.C. Mechalikh, H. Taktak, and F. Moussa. A scalable and adaptive tasks orchestration platform for iot. In 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), pages 1557-1563. IEEE, 2019.B. Mishra and A. Kertesz. The use of mqtt in m2m and iot systems: A survey. IEEE Access, 8:201071-201086, 2020.M. T. Moghaddam, E. Rutten, P. Lalanda, and G. Giraud. Ias: an iot architectural self-adaptation framework. In European Conference on Software Architecture, pages 333-351. Springer, 2020.D. Montero and R. Serral-Gracià. Offloading personal security applications to the network edge: A mobile user case scenario. In 2016 International Wireless Communications and Mobile Computing Conference (IWCMC), pages 96-101. IEEE, 2016.R. Morabito and N. Beijar. A framework based on sdn and containers for dynamic service chains on iot gateways. In Proceedings of theWorkshop on Hot Topics in Container Networking and Networked Systems, pages 42-47. ACM, 2017.K. Morris. Infrastructure as code: managing servers in the cloud. " O'Reilly Media, Inc.", 2016.H. Muccini and M. Sharaf. Caps: Architecture description of situational aware cyber physical systems. In 2017 IEEE International Conference on Software Architecture (ICSA), pages 211-220. IEEE, 2017.H. Muccini, R. Spalazzese, M. T. Moghaddam, and M. Sharaf. Self-adaptive iot architectures: An emergency handling case study. In Proceedings of the 12th European Conference on Software Architecture: Companion Proceedings, pages 1-6, 2018.R. Muñoz, R. Vilalta, N. Yoshikane, R. Casellas, R. Martínez, T. Tsuritani, and I. Morita. Integration of iot, transport sdn, and edge/cloud computing for dynamic distribution of iot analytics and efficient use of network resources. Journal of Lightwave Technology, 36(7):1420-1428, 2018.D. Nandan Jha, K. Alwasel, A. Alshoshan, X. Huang, R. K. Naha, S. K. Battula, S. Garg, D. Puthal, P. James, A. Y. Zomaya, et al. Iotsim-edge: A simulation framework for modeling the behaviour of iot and edge computing environments. arXiv e-prints, pages arXiv 1910, 2019.J. Nielsen and T. K. Landauer. Amathematical model of the finding of usability problems. In Proc. of the INTERACT 93 and CHI 93 conf. on Human factors in computing systems, pages 206-213, 1993.C. Pahl, N. El Ioini, S. Helmer, and B. Lee. An architecture pattern for trusted orchestration in iot edge clouds. In 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC), pages 63-70. IEEE, 2018.C. Pahl and B. Lee. Containers and clusters for edge cloud architectures a technology review. In 2015 3rd international conference on future internet of things and cloud, pages 379-386. IEEE, 2015.P. Patel, M. I. Ali, and A. Sheth. On using the intelligent edge for iot analytics. IEEE Intelligent Systems, 32(5):64-69, 2017.P. Patel and D. Cassou. Enabling high-level application development for the internet of things. Journal of Systems and Software, 103:62-84, 2015.K. Peffers, T. Tuunanen, M. A. Rothenberger, and S. Chatterjee. A design science research methodology for information systems research. Journal of management information systems, 24(3):45-77, 2007.S. Peros, H. Janjua, S. Akkermans, W. Joosen, and D. Hughes. Dynamic qos support for iot backhaul networks through sdn. In 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC), pages 187-192. IEEE, 2018.N. Petrovic and M. Tosic. Smada-fog: Semantic model driven approach to deployment and adaptivity in fog computing. Simulation Modelling Practice and Theory, 101:102033, 2020.D. Pizzolli, G. Cossu, D. Santoro, L. Capra, C. Dupont, D. Charalampos, F. De Pellegrini, F. Antonelli, and S. Cretti. Cloud4iot: A heterogeneous, distributed and autonomic cloud platform for the iot. In 2016 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pages 476-479. IEEE, 2016.S. Prabavathy, K. Sundarakantham, and S. M. Shalinie. Design of cognitive fog computing for intrusion detection in internet of things. Journal of Communications and Networks, 20(3):291-298, 2018.F. Pramudianto, M. Eisenhauer, C. A. Kamienski, D. Sadok, and E. J. Souto. Connecting the internet of things rapidly through a model driven approach. In 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), pages 135-140. IEEE, 2016.T. Rausch, S. Nastic, and S. Dustdar. Emma: distributed qos-aware mqtt middleware for edge computing applications. In 2018 IEEE International Conference on Cloud Engineering (IC2E), pages 191-197. IEEE, 2018.M. d. M. y. E. Republica de Colombia. Reglamento de seguridad en labores minera subterráneas, 2015.A. Rhayem, M. B. A. Mhiri, and F. Gargouri. Semantic web technologies for the internet of things: Systematic literature review. Internet of Things, page 100206, 2020.J. Rubin and D. Chisnell. Handbook of usability testing: how to plan, design and conduct effective tests. John Wiley & Sons, New Jersey, 2008.E. Rutten, N. Marchand, and D. Simon. Feedback control as mape-k loop in autonomic computing. In Software Engineering for Self-Adaptive Systems III. Assurances, pages 349-373. Springer, 2017.A. Salihbegovic, T. Eterovic, E. Kaljic, and S. Ribic. Design of a domain specific language and ide for internet of things applications. In 2015 38th international convention on information and communication technology, electronics and microelectronics (MIPRO), pages 996-1001. IEEE, 2015.H. Sami and A. Mourad. Towards dynamic on-demand fog computing formation based on containerization technology. In 2018 International Conference on Computational Science and Computational Intelligence (CSCI), pages 960-965. IEEE, 2018.J. Sandobalin, E. Insfran, and S. Abrahão. Argon: A model-driven infrastructure provisioning tool. In 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), pages 738-742. IEEE, 2019.J. Santos, T.Wauters, B. Volckaert, and F. De Turck. Fog computing: Enabling the management and orchestration of smart city applications in 5g networks. Entropy, 20(1):4, 2018.J. Santos, T.Wauters, B. Volckaert, and F. De Turck. Resource provisioning in fog computing: From theory to practice. Sensors, 19(10):2238, 2019.B. Sarma, G. Kumar, R. Kumar, and T. Tuithung. Fog computing: An enhanced performance analysis emulation framework for iot with load balancing smart gateway architecture. In 2019 International Conference on Communication and Electronics Systems (ICCES), pages 1-5. IEEE, 2019.M. Satyanarayanan. The emergence of edge computing. Computer, 50(1):30-39, 2017.R. Scolati, I. Fronza, N. El Ioini, A. Samir, and C. Pahl. A containerized big data streaming architecture for edge cloud computing on clustered singleboard devices. In 9th Int. Conf. on Cloud Computing and Services Science, pages 68-80, 2019.M. K. M. Shapi, N. A. Ramli, and L. J. Awalin. Energy consumption prediction by using machine learning for smart building: Case study in malaysia. Developments in the Built Environment, 5:100037, 2021.W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu. Edge computing: Vision and challenges. IEEE internet of things journal, 3(5):637-646, 2016.M. Singh and G. Baranwal. Quality of service (qos) in internet of things. In 2018 3rd International Conference On Internet of Things: Smart Innovation and Usages (IoT-SIU), pages 1-6, Feb 2018.S. Singh and N. Singh. Containers & docker: Emerging roles & future of cloud technology. In 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), pages 804-807. IEEE, 2016.O. Skarlat, V. Karagiannis, T. Rausch, K. Bachmann, and S. Schulte. A framework for optimization, service placement, and runtime operation in the fog. In 2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC), pages 164-173. IEEE, 2018.K. Sledziewski, B. Bordbar, and R. Anane. A dsl-based approach to software development and deployment on cloud. In 2010 24th IEEE International Conference on Advanced Information Networking and Applications, pages 414-421. IEEE, 2010.D. Steinberg, F. Budinsky, E. Merks, and M. Paternostro. EMF: eclipse modeling framework. Pearson Education, 2008.T. Suganuma, T. Oide, S. Kitagami, K. Sugawara, and N. Shiratori. Multiagentbased flexible edge computing architecture for iot. IEEE Network, 32(1):16-23, 2018.V. Theodorou and N. Diamantopoulos. Glt: Edge gateway elt for data-driven intelligence placement. In 2019 IEEE/ACM Joint 4th International Workshop on Rapid Continuous Software Engineering and 1st International Workshop on Data-Driven Decisions, Experimentation and Evolution (RCoSE/DDrEE), pages 24-27. IEEE, 2019.J. R. Torres Neto, G. P. Rocha Filho, L. Y. Mano, L. A. Villas, and J. Ueyama. Exploiting offloading in iot-based microfog: experiments with face recognition and fall detection. Wireless Communications and Mobile Computing, 2019, 2019.C.-L. Tseng and F. J. Lin. Extending scalability of iot/m2m platforms with fog computing. In 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), pages 825-830. IEEE, 2018.I. T. Union. Internet of things global standards initiative, 2012.M. F. van Amstel, M. G. van den Brand, and P. H. Nguyen. Metrics for model transformations. In Proceedings of the Ninth Belgian-Netherlands Software Evolution Workshop (BENEVOL 2010), Lille, France (December 2010), 2010.A. Van Deursen, P. Klint, and J. Visser. Domain-specific languages: An annotated bibliography. ACM Sigplan Notices, 35(6):26-36, 2000.K. Velasquez, D. P. Abreu, D. Gonçalves, L. Bittencourt, M. Curado, E. Monteiro, and E. Madeira. Service orchestration in fog environments. In 2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud), pages 329-336. IEEE, 2017.R. Vilalta, C. V. Apte, J. L. Hellerstein, S. Ma, and S. M. Weiss. Predictive algorithms in the management of computer systems. IBM Systems Journal, 41(3):461-474, 2002.M. Villari, M. Fazio, S. Dustdar, O. Rana, and R. Ranjan. Osmotic computing: A new paradigm for edge/cloud integration. IEEE Cloud Computing, 3(6):76-83, 2016.M. Voelter. Embedded software development with projectional language workbenches. In International Conference on Model Driven Engineering Languages and Systems, pages 32-46. Springer, 2010.M. Völter. Language and ide modularization, extension and composition with mps. Pre-proceedings of Summer School on Generative and Transformational Techniques in Software Engineering (GTTSE), pages 395-431, 2011.J.Wang, J. Pan, and F. Esposito. Elastic urban video surveillance system using edge computing. In Proceedings of the Workshop on Smart Internet of Things, page 7. ACM, 2017.Z. Wen, R. Yang, P. Garraghan, T. Lin, J. Xu, and M. Rovatsos. Fog orchestration for internet of things services. IEEE Internet Computing, 21(2):16-24, 2017.D. Weyns, M. U. Iftikhar, D. Hughes, and N. Matthys. Applying architecturebased adaptation to automate the management of internet-of-things. In European Conf. on Software Architecture, pages 49-67, 2018.WINSYSTEMS. Cloud, fog and edge computing ¿What's the difference? urlhttps://www.winsystems.com/cloud-fog-and-edge-computingwhats-the-difference/, 2017.T.Wong, M.Wagner, and C. Treude. Self-adaptive systems: Asystematic literature review across categories and domains. arXiv preprint arXiv:2101.00125, 2021.B. Wood and A. Azim. Triton: a domain specific language for cyber-physical systems. In 2021 22nd IEEE International Conference on Industrial Technology (ICIT), volume 1, pages 810-816. IEEE, 2021.World Wide Web Consortium (W3C). Semantic sensor network ontology. URL: https://www.w3.org/TR/2017/REC-vocab-ssn-20171019/, 10 2017.D. Wu, M. M. Omwenga, Y. Liang, L. Yang, D. Huston, and T. Xia. A fog computing framework for cognitive portable ground penetrating radars. In ICC 2019-2019 IEEE International Conference on Communications (ICC), pages 1-6. IEEE, 2019.B. Wukkadada, K. Wankhede, R. Nambiar, and A. Nair. Comparison with http and mqtt in internet of things (iot). In 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), pages 249-253. IEEE, 2018.B. Yang, A. Sailer, S. Jain, A. E. Tomala-Reyes, M. Singh, and A. Ramnath. Service discovery based blue-green deployment technique in cloud native environments. In 2018 IEEE International Conference on Services Computing (SCC), pages 185-192. IEEE, 2018.M. B. Yassein, M. Q. Shatnawi, S. Aljwarneh, and R. Al-Hatmi. Internet of things: Survey and open issues of mqtt protocol. In 2017 international conference on engineering & MIS (ICEMIS), pages 1-6. Ieee, 2017.E. Yigitoglu, M. Mohamed, L. Liu, and H. Ludwig. Foggy: a framework for continuous automated iot application deployment in fog computing. In 2017 IEEE International Conference on AI & Mobile Services (AIMS), pages 38-45. IEEE, 2017.R. Young, S. Fallon, and P. Jacob. Dynamic collaboration of centralized & edge processing for coordinated data management in an iot paradigm. In 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA), pages 694-701. IEEE, 2018.R. Young, S. Fallon, and P. Jacob. A governance architecture for self-adaption & control in iot applications. In 2018 5th International Conference on Control, Decision and Information Technologies (CoDIT), pages 241-46. IEEE, 2018.A. Yousefpour, A. Patil, G. Ishigaki, I. Kim, X.Wang, H. C. Cankaya, Q. Zhang, W. Xie, and J. P. Jue. Fogplan: a lightweight qos-aware dynamic fog service provisioning framework. 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IoT@run-time: a model-based approach to support deployment and self-adaptations in IoT systems

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