Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey
Entre los varios paradigmas emergentes de dimensionamiento, control y despliegue de futuras redes de comunicación, se destaca la característica centrada en el ser humano que crea un intrincado relación entre la telemática y las actividades humanas. La dinámica difícil de modelar del comportamiento d...
- Autores:
-
Alzate Mejía, Néstor
Santos Boada, Germán
Almeida Amazonas, José Roberto de
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2021
- Institución:
- Universidad Cooperativa de Colombia
- Repositorio:
- Repositorio UCC
- Idioma:
- OAI Identifier:
- oai:repository.ucc.edu.co:20.500.12494/46453
- Acceso en línea:
- https://hdl.handle.net/20.500.12494/46453
- Palabra clave:
- Redes de comunicación
Telemática
Incertidumbre
Toma de decisiones
Administración de recursos
Industria 4.0
communication network
telematics
uncertainty
decision-making
resource management
Industry 4.0
- Rights
- closedAccess
- License
- Atribución
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dc.title.spa.fl_str_mv |
Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey |
title |
Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey |
spellingShingle |
Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey Redes de comunicación Telemática Incertidumbre Toma de decisiones Administración de recursos Industria 4.0 communication network telematics uncertainty decision-making resource management Industry 4.0 |
title_short |
Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey |
title_full |
Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey |
title_fullStr |
Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey |
title_full_unstemmed |
Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey |
title_sort |
Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey |
dc.creator.fl_str_mv |
Alzate Mejía, Néstor Santos Boada, Germán Almeida Amazonas, José Roberto de |
dc.contributor.author.none.fl_str_mv |
Alzate Mejía, Néstor Santos Boada, Germán Almeida Amazonas, José Roberto de |
dc.subject.spa.fl_str_mv |
Redes de comunicación Telemática Incertidumbre Toma de decisiones Administración de recursos Industria 4.0 |
topic |
Redes de comunicación Telemática Incertidumbre Toma de decisiones Administración de recursos Industria 4.0 communication network telematics uncertainty decision-making resource management Industry 4.0 |
dc.subject.other.spa.fl_str_mv |
communication network telematics uncertainty decision-making resource management Industry 4.0 |
description |
Entre los varios paradigmas emergentes de dimensionamiento, control y despliegue de futuras redes de comunicación, se destaca la característica centrada en el ser humano que crea un intrincado relación entre la telemática y las actividades humanas. La dinámica difícil de modelar del comportamiento del usuario introduce nuevas incertidumbres en estos sistemas que dan lugar a recursos de red difíciles. desafíos de gestión. De acuerdo con este contexto, este trabajo revisa varios procesos de toma de decisiones métodos computacionales bajo la influencia de incertidumbres. Este trabajo, por medio de una sistemática revisión de la literatura, se centra en escenarios de Internet de las cosas basados en sensores, como Smart Spaces y Industria 4.0. De acuerdo con nuestras conclusiones, es obligatorio establecer un medio para modelar el contexto del comportamiento humano para mejorar la asignación y gestión de recursos. |
publishDate |
2021 |
dc.date.issued.none.fl_str_mv |
2021-05 |
dc.date.accessioned.none.fl_str_mv |
2022-09-20T20:58:49Z |
dc.date.available.none.fl_str_mv |
2022-09-20T20:58:49Z |
dc.type.none.fl_str_mv |
Artículos Científicos |
dc.type.coar.none.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.type.coarversion.none.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.version.none.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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publishedVersion |
dc.identifier.issn.spa.fl_str_mv |
1424-8220 |
dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12494/46453 |
dc.identifier.bibliographicCitation.spa.fl_str_mv |
Alzate-Mejía, N.; Santos-Boada, G.; de Almeida Amazonas, J.R. Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey. Sensors 2021, 21, 3791. https:// doi.org/10.3390/s21113791 |
identifier_str_mv |
1424-8220 Alzate-Mejía, N.; Santos-Boada, G.; de Almeida Amazonas, J.R. Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey. Sensors 2021, 21, 3791. https:// doi.org/10.3390/s21113791 |
url |
https://hdl.handle.net/20.500.12494/46453 |
dc.relation.isversionof.spa.fl_str_mv |
https://doi.org/10.3390/s21113791 |
dc.relation.ispartofjournal.spa.fl_str_mv |
Sensors |
dc.relation.references.spa.fl_str_mv |
Conti, M.; Passarella, A.; Das, S.K. The Internet of People (IoP): A new wave in pervasive mobile computing. Pervasive Mob. Comput. 2017, 41, 1–27. [CrossRef] Dix, A. Human-computer interaction, foundations and new paradigms. J. Vis. Lang. Comput. 2016, 42, 122–134. [CrossRef] Bellini, E.; Bellini, P.; Cenni, D.; Nesi, P.; Pantaleo, G.; Paoli, I.; Paolucci, M. An IoE and Big Multimedia Data Approach for Urban Transport System Resilience Management in Smart Cities. Sensors 2021, 21, 435. [CrossRef] Fei, X.; Shah, N.; Verba, N.; Chao, K.M.; Sanchez-Anguix, V.; Lewandowski, J.; James, A.; Usman, Z. CPS data streams analytics based on machine learning for Cloud and Fog Computing: A survey. Future Gener. Comput. Syst. 2019, 90, 435–450. [CrossRef] Lee, J.; Bagheri, B.; Kao, H.A. A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manuf. Lett. 2015, 3, 18–23. [CrossRef] Chahuara, P.; Portet, F.; Vacher, M. Context-aware decision making under uncertainty for voice-based control of smart home. Expert Syst. Appl. 2017, 75, 63–79. [CrossRef] Jiang, W.; Strufe, M.; Schotten, H.D. A SON decision-making framework for intelligent management in 5G mobile networks. In Proceedings of the 3rd IEEE International Conference on Computer and Communications (ICCC), Chengdu, China, 13–16 December 2017; pp. 1158–1162. Kreutz, D.; Ramos, F.M.V.; Veríssimo, P.E.; Rothenberg, C.E.; Azodolmolky, S.; Uhlig, S. Software-Defined Networking: A Comprehensive Survey. Proc. IEEE 2015, 103, 14–76. [CrossRef] Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021, 372. [CrossRef] Zhou, Z.H. Three perspectives of data mining. Artif. Intell. 2003, 143, 139–146. [CrossRef] Saha, A.; Tasdid, M.N.; Rahman, M.R. Mining Semantic Web Based Ontological Data. In Proceedings of the 21st International Conference of Computer and Information Technology (ICCIT), Dhaka, Bangladesh, 21–23 December 2018; pp. 1–5. Sheth, A. Internet of Things to Smart IoT Through Semantic, Cognitive, and Perceptual Computing. IEEE Intell. Syst. 2016, 31, 108–112. [CrossRef] Perera, C.; Zaslavsky, A.; Christen, P.; Georgakopoulos, D. Context Aware Computing for The Internet of Things: A Survey. IEEE Commun. Surv. Tutor. 2014, 16, 414–454. [CrossRef] Ruta, M.; Scioscia, F.; Pinto, A.; Gramegna, F.; Ieva, S.; Loseto, G.; Sciascio, E.D. Cooperative semantic sensor networks for pervasive computing contexts. In Proceedings of the 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI), Vieste, Italy, 15–16 June 2017; pp. 38–43 Alhakbani, N.; Hassan, M.M.; Ykhlef, M.; Fortino, G. An efficient event matching system for semantic smart data in the Internet of Things (IoT) environment. Future Gener. Comput. Syst. 2019, 95, 163–174. [CrossRef] Wang, R.; Ji, W.; Liu, M.; Wang, X.; Weng, J.; Deng, S.; Gao, S.; Yuan, C.A. Review on mining data from multiple data sources. Pattern Recognit. Lett. 2018, 109, 120–128. [CrossRef] Eichstadt, S.; Gruber, M.; Vedurmudi, A.P.; Seeger, B.; Bruns, T.; Kok, G. Toward Smart Traceability for Digital Sensors and the Industrial Internet of Things. Sensors 2021, 21, 19. [CrossRef] [PubMed] Dong, Y.; Wan, K.; Yue, Y. A Semantic-Based Belief Network Construction Approach in IoT. Sensors 2020, 20, 5747. [CrossRef] [PubMed] Cofta, P.; Karatzas, K.; OrÅ‚owski, C. A Conceptual Model of Measurement Uncertainty in IoT Sensor Networks. Sensors 2021, 21, 1827. [CrossRef] Kabir, S.; Ripon, S.; Rahman, M.; Rahman, T. Knowledge-based Data Mining Using Semantic Web. IERI Procedia 2014, 7, 113–119. [CrossRef] Shadroo, S.; Rahmani, A.M. Systematic survey of big data and data mining in internet of things. Comput. Netw. 2018, 139, 19–47. [CrossRef] Sharma, S.; Kumar, A.; Rana, V. Ontology Based Informational Retrieval System on the Semantic Web: Semantic Web Mining. In Proceedings of the International Conference on Next Generation Computing and Information Systems (ICNGCIS), Jammu, India, 11–12 December 2017; pp. 35–37. Dou, D.; Wang, H.; Liu, H. Semantic data mining: A survey of ontology-based approaches. In Proceedings of the 9th International Conference on Semantic Computing (ICSC), Anaheim, CA, USA, 7–9 February 2015; pp. 244–251. Ristoski, P.; Paulheim, H. Semantic Web in data mining and knowledge discovery: A comprehensive survey. J. Web Semant. 2016, 36, 1–22. [CrossRef] Safwat, H.; Gruzitis, N.; Davis, B.; Enache, R. Extracting Semantic Knowledge from Unstructured Text Using Embedded Controlled Language. In Proceedings of the IEEE Tenth International Conference on Semantic Computing (ICSC), Laguna Hills, CA, USA, 4–6 February 2016; pp. 87–90. Nagorny, K.; Scholze, S.; Ruhl, M.; Colombo, A.W. Semantical support for a CPS data marketplace to prepare Big Data analytics in smart manufacturing environments. In Proceedings of the IEEE Industrial Cyber-Physical Systems (ICPS), St. Petersburg, Russia, 15–18 May 2018; pp. 206–211. Wang, Y.; Bai, X.; Ou, H. Design and Development of Intelligent Logistics System Based on Semantic Web and Data Mining Technology. In Proceedings of the International Conference on Computer Network, Electronic and Automation (ICCNEA), Xi’an, China, 23–25 September 2017; pp. 231–235. Shadbolt, N.; Berners-Lee, T.; Hall, W. The Semantic Web Revisited. IEEE Intell. Syst. 2006, 21, 96–101. [CrossRef] IEML. Le Métalangage de Léconomie de Línformation. LIVRE BLANC. 2019. Available online: https://www.dropbox.com/s/87 5vsj0atbcts43/0-00-IEML-Manifesto-2019-fr.pdf?dl=0 (accessed on 18 February 2021). [CrossRef] Lévy, P. The Semantic Sphere; Addison-Wesley: Reading, MA, USA, 2011 Kochenderfer, M.J.; Amato, C.; Chowdhary, G.; How, J.P.; Reynolds, H.J.D.; Thornton, J.R.; Torres-Carrasquillo, P.A.; Üre, N.K.; Vian, J. Decision Making under Uncertainty: Theory and Application; MIT Lincoln Laboratory Series; The MIT Press: Cambridge, MA, USA, 2015; p. 352. Asadabadi, M.R. The stratified multi-criteria decision-making method. Knowl. Based Syst. 2018, 162, 115–123. [CrossRef] Ferrara, M.; Rasouli, S.; Khademi, M.; Salimi, M. A robust optimization model for a decision-making problem: An application for stock market. Oper. Res. Perspect. 2017, 4, 136–141. [CrossRef] Chen, S..; Liu, J.; Wang, H.; Augusto, J.C. An evidential reasoning based approach for decision making with partially ordered preference under uncertainty. In Proceedings of the International Conference on Machine Learning and Cybernetics, Tianjin, China, 14–17 July 2013; pp. 1712–1717. Zhang, P.; Yao, H.; Qiu, C.; Liu, Y. Virtual Network Embedding Using Node Multiple Metrics Based on Simplified ELECTRE Method. IEEE Access 2018, 6, 37314–37327. [CrossRef] Dammak, F.; Baccour, L.; Ayed, A.B.; Alimi, A.M. ELECTRE method using interval-valued intuitionistic fuzzy sets and possibility theory for multi-criteria decision making problem resolution. In Proceedings of the IEEE International Conference on Fuzzy Systems, Naples, Italy, 9–12 July 2017; pp. 1–6. Gervasio, H.; Simoes Da Silva, L. A probabilistic decision-making approach for the sustainable assessment of infrastructures. Expert Syst. Appl. 2012, 39, 7121–7131. [CrossRef] Wei, L.; Yuan, Z.; Yan, Y.; Hou, J.; Qin, T. Evaluation of energy saving and emission reduction effect in thermal power plants based on entropy weight and PROMETHEE method. In Proceedings of the Chinese Control and Decision Conference (CCDC), Yinchuan, China, 28–30 May 2016; pp. 143–146. Almeida, A.T.d.; Morais, D.C.; Alencar, L.H.; Clemente, T.R.N.; Krym, E.M.; Barboza, C.Z. A multicriteria decision model for technology readiness assessment for energy based on PROMETHEE method with surrogate weights. In Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management, Selangor, Malaysia, 9–12 December 2014; pp. 64–68 Smet, Y.D. About the computation of robust PROMETHEE II rankings: Empirical evidence. In Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Bali, Indonesia, 4–7 December 2016; pp. 1116–1120 Zavadskas, E.K.; Turskis, Z.; Bagoˇcius, V. Multi-criteria selection of a deep-water port in the Eastern Baltic Sea. Appl. Soft Comput. 2015, 26, 180–192. [CrossRef] Mousavi, S.M.; Gitinavard, H.; Siadat, A. A new hesitant fuzzy analytical hierarchy process method for decision-making problems under uncertainty. In Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management, Selangor, Malaysia, 9–12 December 2014; pp. 622–626. Kumar, G. A multi-criteria decision making approach for recommending a product using sentiment analysis. In Proceedings of the 12th International Conference on Research Challenges in Information Science (RCIS), Nantes, France, 29–31 May 2018; pp. 1–6. Qin, J.; Liu, X.; Pedrycz, W. An extended VIKOR method based on prospect theory for multiple attribute decision making under interval type-2 fuzzy environment. Knowl. Based Syst. 2015, 86, 116–130. [CrossRef] Morente-Molinera, J.A.; Kou, G.; Samuylov, K.; Ureña, R.; Herrera-Viedma, E. Carrying out consensual Group Decision Making processes under social networks using sentiment analysis over comparative expressions. Knowl. Based Syst. 2019, 165, 335–345. [CrossRef] Perçin, S. Evaluating airline service quality using a combined fuzzy decision-making approach. J. Air Transp. Manag. 2018, 68, 48–60. [CrossRef] Yu, B.; Cai, M.; Li, Q. A λ-rough set model and its applications with TOPSIS method to decision making. Knowl. Based Syst. 2019, 165, 420–431. [CrossRef] Chen, S.M.; Cheng, S.H.; Lan, T.C. A new multicriteria decision making method based on the topsis method and similarity measures between intuitionistic fuzzy sets. In Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC), Jeju, Korea, 10–13 July 2016; pp. 692–696. Cables, E.H.; Lamata, M.T.; Verdegay, J.L. Ideal Reference Method with Linguistic Labels: A Comparison with LTOPSIS. In Uncertainty Management with Fuzzy and Rough Sets: Recent Advances and Applications; Springer International Publishing: Cham, Switzerland, 2019; pp. 115–126. Khezrimotlagh, D.; Chen, Y. Data envelopment analysis. In International Series in Operations Research and Management Science; Springer: Dordrecht, The Netherlands, 2018; Volume 269, pp. 217–234. Roy, B. Classement et choix en présence de points de vue multiples. Rev. Française d’Informatique Rech. Opérationnelle 1968, 2, 57–75. [CrossRef] Mareschal, B.; Brans, J.P.; Vincke, P. PROMETHEE: A new family of outranking methods in multicriteria analysis. Oper. Res. ORIJ 1984, 84. Available online: https://ideas.repec.org/p/ulb/ulbeco/2013-9305.html (accessed on 18 February 2021). Saaty, T.L. The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation; McGraw-Hill: New York, USA, 1980; p. 287 Duckstein, L.; Opricovic, S. Multiobjective optimization in river basin development. Water Resour. Res. 1980, 16, 14–20. [CrossRef] Hwang, C.L.; Yoon, K. Methods for Multiple Attribute Decision Making. In Multiple Attribute Decision Making: Methods and Applications A State-of-the-Art Survey; Springer: Berlin/Heidelberg, Germany, 1981; pp. 58–191. doi:10.1007/978-3-642-48318-9_3. [CrossRef] Charnes, A.; Cooper, W.; Rhodes, E. Measuring the efficiency of decision making units. Eur. J. Oper. Res. 1978, 2, 429–444. [CrossRef] Peng, X.; Garg, H. Algorithms for interval-valued fuzzy soft sets in emergency decision making based on WDBA and CODAS with new information measure. Comput. Ind. Eng. 2018, 119, 439–452. [CrossRef] Mokhtarian, M.N.; Sadi-Nezhad, S.; Makui, A. A new flexible and reliable IVF-TOPSIS method based on uncertainty risk reduction in decision making process. Appl. Soft Comput. J. 2014, 23, 509–520. [CrossRef] Madi, E.N.; Garibaldi, J.M.; Wagner, C. Exploring the use of type-2 fuzzy sets in multi-criteria decision making based on TOPSIS. In Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Naples, Italy, 9–12 July 2017; pp. 1–6. Yue, Z.; Jia, Y. An application of soft computing technique in group decision making under interval-valued intuitionistic fuzzy environment. Appl. Soft Comput. J. 2013, 13, 2490–2503. [CrossRef] Shen, L.; Wang, H.; Feng, X. Ranking Methods of Intuitionistic Fuzzy Numbers in Multicriteria Decision Making. In Proceedings of the 3rd International Conference on Information Management, Innovation Management and Industrial Engineering, Kunming, China, 26–28 November 2010; pp. 143–146 Yan, R.; Zheng, J.; Wang, X. Vague set methods of multi-criteria fuzzy decision-making. In Proceedings of the Chinese Control and Decision Conference, Xuzhou, China, 26–28 May 2010; pp. 658–661. Gao, C.; Lai, Z.; Zhou, J.; Zhao, C.; Miao, D. Maximum decision entropy-based attribute reduction in decision-theoretic rough set model. Knowl. Based Syst. 2018, 143, 179–191. [CrossRef] Remesh, K.M.; Nair, L.R. Rough set theory and three way decisions: Refinement of boundary region in the decision making process. In Proceedings of the International Conference on Information Science (ICIS), Kochi, India, 12–13 August 2016; pp. 156–159. Yao, J.; Azam, N. Web-Based Medical Decision Support Systems for Three-Way Medical Decision Making With Game-Theoretic Rough Sets. IEEE Trans. Fuzzy Syst. 2015, 23, 3–15. [CrossRef] Kondratenko, Y.; Kondratenko, G.; Sidenko, I. Multi-criteria Decision Making and Soft Computing for the Selection of Specialized IoT Platform. In Recent Developments in Data Science and Intelligent Analysis of Information; Chertov, O., Mylovanov, T., Kondratenko, Y., Kacprzyk, J., Kreinovich, V., Stefanuk, V., Eds.; Springer International Publishing: Cham, Switzerland, 2019; pp. 71–80 Lamata, M.T.; Pelta, D.A.; Rosete, A.; Verdegay, J.L. Context-Based Decision and Optimization: The Case of the Maximal Coverage Location Problem. In Information Processing and Management of Uncertainty in Knowledge-Based Systems; Medina, J., Ojeda-Aciego, M., Verdegay, J.L., Perfilieva, I., Bouchon-Meunier, B., Yager, R.R., Eds.; Springer International Publishing: Cham, Switzerland, 2018; pp. 330–341. Mendel, J.M. Type-1 Fuzzy Sets and Fuzzy Logic. In Uncertain Rule-Based Fuzzy Systems: Introduction and New Directions, 2nd ed.; Springer International Publishing: Cham, Switzerland, 2017; pp. 25–99. doi:10.1007/978-3-319-51370-6_2. [CrossRef] Keshavarz Ghorabaee, M.; Amiri, M.; Zavadskas, E.K.; Turskis, Z.; Antucheviciene, J. A new multi-criteria model based on interval type-2 fuzzy sets and EDAS method for supplier evaluation and order allocation with environmental considerations. Comput. Ind. Eng. 2017, 112, 156–174. [CrossRef] Matía, F.; Jiménez, V.; Alvarado, B.P.; Haber, R. The fuzzy Kalman filter: Improving its implementation by reformulating uncertainty representation. Fuzzy Sets Syst. 2021, 402, 78–104. [CrossRef] Ma, Z.; Wang, S.; Deng, X.; Jiang, W. An improved approach for adversarial decision making under uncertainty based on simultaneous game. In Proceedings of the Chinese Control And Decision Conference (CCDC), Shenyang, China, 9–11 June 2018; pp. 2499–2503. Giraldo, F.A.; Gomez, J. The evolution of neural networks for decision making in non-cooperative repetitive games. In Proceedings of the 8th Computing Colombian Conference (8CCC), Armenia, Colombia, 21–23 August 2013; pp. 1–6 Chen, Y.; Jiang, C.; Wang, C.Y.; Gao, Y.; Liu, K.R. Decision Learning: Data analytic learning with strategic decision making. IEEE Signal Process. Mag. 2016, 33, 37–56. [CrossRef] Vamvakas, P.; Tsiropoulou, E.E.; Papavassiliou, S. Risk-Aware Resource Management in Public Safety Networks. Sensors 2019, 19, 3853. [CrossRef] Bin, Z.; Ming-jun, L.; Kai-ying, W.; Lin, W. Hybrid interval uncertain multi-attribute decision making based on set pair analysis. In Proceedings of the International Conference on Mechatronic Science, Electric Engineering and Computer (MEC), Jilin, China, 19–22 August 2011; pp. 938–941. Du, P.; Liu, H. Study on air combat tactics decision-making based on bayesian networks. In Proceedings of the 2nd IEEE International Conference on Information Management and Engineering, Chengdu, China, 16–18 April 2010; pp. 252–256 |
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Alzate Mejía, NéstorSantos Boada, GermánAlmeida Amazonas, José Roberto de21(11)2022-09-20T20:58:49Z2022-09-20T20:58:49Z2021-051424-8220https://hdl.handle.net/20.500.12494/46453Alzate-Mejía, N.; Santos-Boada, G.; de Almeida Amazonas, J.R. Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey. Sensors 2021, 21, 3791. https:// doi.org/10.3390/s21113791Entre los varios paradigmas emergentes de dimensionamiento, control y despliegue de futuras redes de comunicación, se destaca la característica centrada en el ser humano que crea un intrincado relación entre la telemática y las actividades humanas. La dinámica difícil de modelar del comportamiento del usuario introduce nuevas incertidumbres en estos sistemas que dan lugar a recursos de red difíciles. desafíos de gestión. De acuerdo con este contexto, este trabajo revisa varios procesos de toma de decisiones métodos computacionales bajo la influencia de incertidumbres. Este trabajo, por medio de una sistemática revisión de la literatura, se centra en escenarios de Internet de las cosas basados en sensores, como Smart Spaces y Industria 4.0. De acuerdo con nuestras conclusiones, es obligatorio establecer un medio para modelar el contexto del comportamiento humano para mejorar la asignación y gestión de recursos.Among the several emerging dimensioning, control and deployment of future communication network paradigms stands out the human-centric characteristic that creates an intricate relationship between telematics and human activities. The hard to model dynamics of user behavior introduces new uncertainties into these systems that give rise to difficult network resource management challenges. According to this context, this work reviews several decision-making computational methods under the influence of uncertainties. This work, by means of a systematic literature review, focuses on sensor-based Internet of Things scenarios such as Smart Spaces and Industry 4.0. According to our conclusions, it is mandatory to establish a means for modeling the human behavior context in order to improve resource assignment and management.nestor.alzatem@campusucc.edu.co30 p.Universidad Cooperativa de Colombia, Facultad de Ingenierías, Ingeniería de Sistemas, CaliIngeniería de SistemasCalihttps://doi.org/10.3390/s21113791SensorsConti, M.; Passarella, A.; Das, S.K. The Internet of People (IoP): A new wave in pervasive mobile computing. Pervasive Mob. Comput. 2017, 41, 1–27. [CrossRef]Dix, A. Human-computer interaction, foundations and new paradigms. J. Vis. Lang. Comput. 2016, 42, 122–134. [CrossRef]Bellini, E.; Bellini, P.; Cenni, D.; Nesi, P.; Pantaleo, G.; Paoli, I.; Paolucci, M. An IoE and Big Multimedia Data Approach for Urban Transport System Resilience Management in Smart Cities. Sensors 2021, 21, 435. [CrossRef]Fei, X.; Shah, N.; Verba, N.; Chao, K.M.; Sanchez-Anguix, V.; Lewandowski, J.; James, A.; Usman, Z. CPS data streams analytics based on machine learning for Cloud and Fog Computing: A survey. Future Gener. Comput. Syst. 2019, 90, 435–450. [CrossRef]Lee, J.; Bagheri, B.; Kao, H.A. A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manuf. Lett. 2015, 3, 18–23. [CrossRef]Chahuara, P.; Portet, F.; Vacher, M. Context-aware decision making under uncertainty for voice-based control of smart home. Expert Syst. Appl. 2017, 75, 63–79. [CrossRef]Jiang, W.; Strufe, M.; Schotten, H.D. A SON decision-making framework for intelligent management in 5G mobile networks. In Proceedings of the 3rd IEEE International Conference on Computer and Communications (ICCC), Chengdu, China, 13–16 December 2017; pp. 1158–1162.Kreutz, D.; Ramos, F.M.V.; Veríssimo, P.E.; Rothenberg, C.E.; Azodolmolky, S.; Uhlig, S. Software-Defined Networking: A Comprehensive Survey. Proc. IEEE 2015, 103, 14–76. [CrossRef]Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021, 372. [CrossRef]Zhou, Z.H. Three perspectives of data mining. Artif. Intell. 2003, 143, 139–146. [CrossRef]Saha, A.; Tasdid, M.N.; Rahman, M.R. Mining Semantic Web Based Ontological Data. In Proceedings of the 21st International Conference of Computer and Information Technology (ICCIT), Dhaka, Bangladesh, 21–23 December 2018; pp. 1–5.Sheth, A. Internet of Things to Smart IoT Through Semantic, Cognitive, and Perceptual Computing. IEEE Intell. Syst. 2016, 31, 108–112. [CrossRef]Perera, C.; Zaslavsky, A.; Christen, P.; Georgakopoulos, D. Context Aware Computing for The Internet of Things: A Survey. IEEE Commun. Surv. Tutor. 2014, 16, 414–454. [CrossRef]Ruta, M.; Scioscia, F.; Pinto, A.; Gramegna, F.; Ieva, S.; Loseto, G.; Sciascio, E.D. Cooperative semantic sensor networks for pervasive computing contexts. In Proceedings of the 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI), Vieste, Italy, 15–16 June 2017; pp. 38–43Alhakbani, N.; Hassan, M.M.; Ykhlef, M.; Fortino, G. An efficient event matching system for semantic smart data in the Internet of Things (IoT) environment. Future Gener. Comput. Syst. 2019, 95, 163–174. [CrossRef]Wang, R.; Ji, W.; Liu, M.; Wang, X.; Weng, J.; Deng, S.; Gao, S.; Yuan, C.A. Review on mining data from multiple data sources. Pattern Recognit. Lett. 2018, 109, 120–128. [CrossRef]Eichstadt, S.; Gruber, M.; Vedurmudi, A.P.; Seeger, B.; Bruns, T.; Kok, G. Toward Smart Traceability for Digital Sensors and the Industrial Internet of Things. Sensors 2021, 21, 19. [CrossRef] [PubMed]Dong, Y.; Wan, K.; Yue, Y. A Semantic-Based Belief Network Construction Approach in IoT. Sensors 2020, 20, 5747. [CrossRef] [PubMed]Cofta, P.; Karatzas, K.; OrÅ‚owski, C. A Conceptual Model of Measurement Uncertainty in IoT Sensor Networks. Sensors 2021, 21, 1827. [CrossRef]Kabir, S.; Ripon, S.; Rahman, M.; Rahman, T. Knowledge-based Data Mining Using Semantic Web. IERI Procedia 2014, 7, 113–119. [CrossRef]Shadroo, S.; Rahmani, A.M. Systematic survey of big data and data mining in internet of things. Comput. Netw. 2018, 139, 19–47. [CrossRef]Sharma, S.; Kumar, A.; Rana, V. Ontology Based Informational Retrieval System on the Semantic Web: Semantic Web Mining. In Proceedings of the International Conference on Next Generation Computing and Information Systems (ICNGCIS), Jammu, India, 11–12 December 2017; pp. 35–37.Dou, D.; Wang, H.; Liu, H. Semantic data mining: A survey of ontology-based approaches. In Proceedings of the 9th International Conference on Semantic Computing (ICSC), Anaheim, CA, USA, 7–9 February 2015; pp. 244–251.Ristoski, P.; Paulheim, H. Semantic Web in data mining and knowledge discovery: A comprehensive survey. J. Web Semant. 2016, 36, 1–22. [CrossRef]Safwat, H.; Gruzitis, N.; Davis, B.; Enache, R. Extracting Semantic Knowledge from Unstructured Text Using Embedded Controlled Language. In Proceedings of the IEEE Tenth International Conference on Semantic Computing (ICSC), Laguna Hills, CA, USA, 4–6 February 2016; pp. 87–90.Nagorny, K.; Scholze, S.; Ruhl, M.; Colombo, A.W. Semantical support for a CPS data marketplace to prepare Big Data analytics in smart manufacturing environments. In Proceedings of the IEEE Industrial Cyber-Physical Systems (ICPS), St. Petersburg, Russia, 15–18 May 2018; pp. 206–211.Wang, Y.; Bai, X.; Ou, H. Design and Development of Intelligent Logistics System Based on Semantic Web and Data Mining Technology. In Proceedings of the International Conference on Computer Network, Electronic and Automation (ICCNEA), Xi’an, China, 23–25 September 2017; pp. 231–235.Shadbolt, N.; Berners-Lee, T.; Hall, W. The Semantic Web Revisited. IEEE Intell. Syst. 2006, 21, 96–101. [CrossRef]IEML. Le Métalangage de Léconomie de Línformation. LIVRE BLANC. 2019. Available online: https://www.dropbox.com/s/87 5vsj0atbcts43/0-00-IEML-Manifesto-2019-fr.pdf?dl=0 (accessed on 18 February 2021). [CrossRef]Lévy, P. The Semantic Sphere; Addison-Wesley: Reading, MA, USA, 2011Kochenderfer, M.J.; Amato, C.; Chowdhary, G.; How, J.P.; Reynolds, H.J.D.; Thornton, J.R.; Torres-Carrasquillo, P.A.; Üre, N.K.; Vian, J. Decision Making under Uncertainty: Theory and Application; MIT Lincoln Laboratory Series; The MIT Press: Cambridge, MA, USA, 2015; p. 352.Asadabadi, M.R. The stratified multi-criteria decision-making method. Knowl. Based Syst. 2018, 162, 115–123. [CrossRef]Ferrara, M.; Rasouli, S.; Khademi, M.; Salimi, M. A robust optimization model for a decision-making problem: An application for stock market. Oper. Res. Perspect. 2017, 4, 136–141. [CrossRef]Chen, S..; Liu, J.; Wang, H.; Augusto, J.C. An evidential reasoning based approach for decision making with partially ordered preference under uncertainty. In Proceedings of the International Conference on Machine Learning and Cybernetics, Tianjin, China, 14–17 July 2013; pp. 1712–1717.Zhang, P.; Yao, H.; Qiu, C.; Liu, Y. Virtual Network Embedding Using Node Multiple Metrics Based on Simplified ELECTRE Method. IEEE Access 2018, 6, 37314–37327. [CrossRef]Dammak, F.; Baccour, L.; Ayed, A.B.; Alimi, A.M. ELECTRE method using interval-valued intuitionistic fuzzy sets and possibility theory for multi-criteria decision making problem resolution. In Proceedings of the IEEE International Conference on Fuzzy Systems, Naples, Italy, 9–12 July 2017; pp. 1–6.Gervasio, H.; Simoes Da Silva, L. A probabilistic decision-making approach for the sustainable assessment of infrastructures. Expert Syst. Appl. 2012, 39, 7121–7131. [CrossRef]Wei, L.; Yuan, Z.; Yan, Y.; Hou, J.; Qin, T. Evaluation of energy saving and emission reduction effect in thermal power plants based on entropy weight and PROMETHEE method. In Proceedings of the Chinese Control and Decision Conference (CCDC), Yinchuan, China, 28–30 May 2016; pp. 143–146.Almeida, A.T.d.; Morais, D.C.; Alencar, L.H.; Clemente, T.R.N.; Krym, E.M.; Barboza, C.Z. A multicriteria decision model for technology readiness assessment for energy based on PROMETHEE method with surrogate weights. In Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management, Selangor, Malaysia, 9–12 December 2014; pp. 64–68Smet, Y.D. About the computation of robust PROMETHEE II rankings: Empirical evidence. In Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Bali, Indonesia, 4–7 December 2016; pp. 1116–1120Zavadskas, E.K.; Turskis, Z.; Bagoˇcius, V. Multi-criteria selection of a deep-water port in the Eastern Baltic Sea. Appl. Soft Comput. 2015, 26, 180–192. [CrossRef]Mousavi, S.M.; Gitinavard, H.; Siadat, A. A new hesitant fuzzy analytical hierarchy process method for decision-making problems under uncertainty. In Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management, Selangor, Malaysia, 9–12 December 2014; pp. 622–626.Kumar, G. A multi-criteria decision making approach for recommending a product using sentiment analysis. In Proceedings of the 12th International Conference on Research Challenges in Information Science (RCIS), Nantes, France, 29–31 May 2018; pp. 1–6.Qin, J.; Liu, X.; Pedrycz, W. An extended VIKOR method based on prospect theory for multiple attribute decision making under interval type-2 fuzzy environment. Knowl. Based Syst. 2015, 86, 116–130. [CrossRef]Morente-Molinera, J.A.; Kou, G.; Samuylov, K.; Ureña, R.; Herrera-Viedma, E. Carrying out consensual Group Decision Making processes under social networks using sentiment analysis over comparative expressions. Knowl. Based Syst. 2019, 165, 335–345. [CrossRef]Perçin, S. Evaluating airline service quality using a combined fuzzy decision-making approach. J. Air Transp. Manag. 2018, 68, 48–60. [CrossRef]Yu, B.; Cai, M.; Li, Q. A λ-rough set model and its applications with TOPSIS method to decision making. Knowl. Based Syst. 2019, 165, 420–431. [CrossRef]Chen, S.M.; Cheng, S.H.; Lan, T.C. A new multicriteria decision making method based on the topsis method and similarity measures between intuitionistic fuzzy sets. In Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC), Jeju, Korea, 10–13 July 2016; pp. 692–696.Cables, E.H.; Lamata, M.T.; Verdegay, J.L. Ideal Reference Method with Linguistic Labels: A Comparison with LTOPSIS. In Uncertainty Management with Fuzzy and Rough Sets: Recent Advances and Applications; Springer International Publishing: Cham, Switzerland, 2019; pp. 115–126.Khezrimotlagh, D.; Chen, Y. Data envelopment analysis. In International Series in Operations Research and Management Science; Springer: Dordrecht, The Netherlands, 2018; Volume 269, pp. 217–234.Roy, B. Classement et choix en présence de points de vue multiples. Rev. Française d’Informatique Rech. Opérationnelle 1968, 2, 57–75. [CrossRef]Mareschal, B.; Brans, J.P.; Vincke, P. PROMETHEE: A new family of outranking methods in multicriteria analysis. Oper. Res. ORIJ 1984, 84. Available online: https://ideas.repec.org/p/ulb/ulbeco/2013-9305.html (accessed on 18 February 2021).Saaty, T.L. The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation; McGraw-Hill: New York, USA, 1980; p. 287Duckstein, L.; Opricovic, S. Multiobjective optimization in river basin development. Water Resour. Res. 1980, 16, 14–20. [CrossRef]Hwang, C.L.; Yoon, K. Methods for Multiple Attribute Decision Making. In Multiple Attribute Decision Making: Methods and Applications A State-of-the-Art Survey; Springer: Berlin/Heidelberg, Germany, 1981; pp. 58–191. doi:10.1007/978-3-642-48318-9_3. [CrossRef]Charnes, A.; Cooper, W.; Rhodes, E. Measuring the efficiency of decision making units. Eur. J. Oper. Res. 1978, 2, 429–444. [CrossRef]Peng, X.; Garg, H. Algorithms for interval-valued fuzzy soft sets in emergency decision making based on WDBA and CODAS with new information measure. Comput. Ind. Eng. 2018, 119, 439–452. [CrossRef]Mokhtarian, M.N.; Sadi-Nezhad, S.; Makui, A. A new flexible and reliable IVF-TOPSIS method based on uncertainty risk reduction in decision making process. Appl. Soft Comput. J. 2014, 23, 509–520. [CrossRef]Madi, E.N.; Garibaldi, J.M.; Wagner, C. Exploring the use of type-2 fuzzy sets in multi-criteria decision making based on TOPSIS. In Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Naples, Italy, 9–12 July 2017; pp. 1–6.Yue, Z.; Jia, Y. An application of soft computing technique in group decision making under interval-valued intuitionistic fuzzy environment. Appl. Soft Comput. J. 2013, 13, 2490–2503. [CrossRef]Shen, L.; Wang, H.; Feng, X. Ranking Methods of Intuitionistic Fuzzy Numbers in Multicriteria Decision Making. In Proceedings of the 3rd International Conference on Information Management, Innovation Management and Industrial Engineering, Kunming, China, 26–28 November 2010; pp. 143–146Yan, R.; Zheng, J.; Wang, X. Vague set methods of multi-criteria fuzzy decision-making. In Proceedings of the Chinese Control and Decision Conference, Xuzhou, China, 26–28 May 2010; pp. 658–661.Gao, C.; Lai, Z.; Zhou, J.; Zhao, C.; Miao, D. Maximum decision entropy-based attribute reduction in decision-theoretic rough set model. Knowl. Based Syst. 2018, 143, 179–191. [CrossRef]Remesh, K.M.; Nair, L.R. Rough set theory and three way decisions: Refinement of boundary region in the decision making process. In Proceedings of the International Conference on Information Science (ICIS), Kochi, India, 12–13 August 2016; pp. 156–159.Yao, J.; Azam, N. Web-Based Medical Decision Support Systems for Three-Way Medical Decision Making With Game-Theoretic Rough Sets. IEEE Trans. Fuzzy Syst. 2015, 23, 3–15. [CrossRef]Kondratenko, Y.; Kondratenko, G.; Sidenko, I. Multi-criteria Decision Making and Soft Computing for the Selection of Specialized IoT Platform. In Recent Developments in Data Science and Intelligent Analysis of Information; Chertov, O., Mylovanov, T., Kondratenko, Y., Kacprzyk, J., Kreinovich, V., Stefanuk, V., Eds.; Springer International Publishing: Cham, Switzerland, 2019; pp. 71–80Lamata, M.T.; Pelta, D.A.; Rosete, A.; Verdegay, J.L. Context-Based Decision and Optimization: The Case of the Maximal Coverage Location Problem. In Information Processing and Management of Uncertainty in Knowledge-Based Systems; Medina, J., Ojeda-Aciego, M., Verdegay, J.L., Perfilieva, I., Bouchon-Meunier, B., Yager, R.R., Eds.; Springer International Publishing: Cham, Switzerland, 2018; pp. 330–341.Mendel, J.M. Type-1 Fuzzy Sets and Fuzzy Logic. In Uncertain Rule-Based Fuzzy Systems: Introduction and New Directions, 2nd ed.; Springer International Publishing: Cham, Switzerland, 2017; pp. 25–99. doi:10.1007/978-3-319-51370-6_2. [CrossRef]Keshavarz Ghorabaee, M.; Amiri, M.; Zavadskas, E.K.; Turskis, Z.; Antucheviciene, J. A new multi-criteria model based on interval type-2 fuzzy sets and EDAS method for supplier evaluation and order allocation with environmental considerations. Comput. Ind. Eng. 2017, 112, 156–174. [CrossRef]Matía, F.; Jiménez, V.; Alvarado, B.P.; Haber, R. The fuzzy Kalman filter: Improving its implementation by reformulating uncertainty representation. Fuzzy Sets Syst. 2021, 402, 78–104. [CrossRef]Ma, Z.; Wang, S.; Deng, X.; Jiang, W. An improved approach for adversarial decision making under uncertainty based on simultaneous game. In Proceedings of the Chinese Control And Decision Conference (CCDC), Shenyang, China, 9–11 June 2018; pp. 2499–2503.Giraldo, F.A.; Gomez, J. The evolution of neural networks for decision making in non-cooperative repetitive games. In Proceedings of the 8th Computing Colombian Conference (8CCC), Armenia, Colombia, 21–23 August 2013; pp. 1–6Chen, Y.; Jiang, C.; Wang, C.Y.; Gao, Y.; Liu, K.R. Decision Learning: Data analytic learning with strategic decision making. IEEE Signal Process. Mag. 2016, 33, 37–56. [CrossRef]Vamvakas, P.; Tsiropoulou, E.E.; Papavassiliou, S. Risk-Aware Resource Management in Public Safety Networks. Sensors 2019, 19, 3853. [CrossRef]Bin, Z.; Ming-jun, L.; Kai-ying, W.; Lin, W. Hybrid interval uncertain multi-attribute decision making based on set pair analysis. In Proceedings of the International Conference on Mechatronic Science, Electric Engineering and Computer (MEC), Jilin, China, 19–22 August 2011; pp. 938–941.Du, P.; Liu, H. Study on air combat tactics decision-making based on bayesian networks. In Proceedings of the 2nd IEEE International Conference on Information Management and Engineering, Chengdu, China, 16–18 April 2010; pp. 252–256Redes de comunicaciónTelemáticaIncertidumbreToma de decisionesAdministración de recursosIndustria 4.0communication networktelematicsuncertaintydecision-makingresource managementIndustry 4.0Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A SurveyArtículos Científicoshttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionAtribucióninfo:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbPublicationORIGINAL2021_decision_making_under.pdf2021_decision_making_under.pdfArtículoapplication/pdf838200https://repository.ucc.edu.co/bitstreams/2bd34a1e-ba8d-4bb1-932d-c979a485d700/download5ea3a122c115f929c9817393c76ae1d4MD512021_decision_making_under-licencia.pdf2021_decision_making_under-licencia.pdfLicencia de usoapplication/pdf212561https://repository.ucc.edu.co/bitstreams/cec85592-c210-48a7-8beb-c01c6f86ec01/download67a2e6bfb1b093a9c0f0dd0c63e3ca6eMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repository.ucc.edu.co/bitstreams/8aefa82f-5b9a-46dd-820b-4aa6938b6ffc/download8a4605be74aa9ea9d79846c1fba20a33MD53THUMBNAIL2021_decision_making_under.pdf.jpg2021_decision_making_under.pdf.jpgGenerated Thumbnailimage/jpeg5669https://repository.ucc.edu.co/bitstreams/0bcd1642-5ea7-4876-b489-1af8d154d946/downloadd6049eb5affaff0b7363169c0ce5fafeMD542021_decision_making_under-licencia.pdf.jpg2021_decision_making_under-licencia.pdf.jpgGenerated Thumbnailimage/jpeg5099https://repository.ucc.edu.co/bitstreams/4dfb2751-b3f4-4982-ba2d-5f2a8003d3bb/downloade54443649866365d576867d084cb432dMD55TEXT2021_decision_making_under.pdf.txt2021_decision_making_under.pdf.txtExtracted texttext/plain100665https://repository.ucc.edu.co/bitstreams/3ddffe4a-7c3f-4886-916b-46cda60f44ac/download84e1dba4cb84467d6531f0b177d04d50MD562021_decision_making_under-licencia.pdf.txt2021_decision_making_under-licencia.pdf.txtExtracted texttext/plain5849https://repository.ucc.edu.co/bitstreams/29dbefb4-9d97-4b34-bfcd-df3d740cf03e/download4f42edfa25e7182aa080ed26801e3423MD5720.500.12494/46453oai:repository.ucc.edu.co:20.500.12494/464532024-08-10 21:02:41.845restrictedhttps://repository.ucc.edu.coRepositorio Institucional Universidad Cooperativa de Colombiabdigital@metabiblioteca.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 |