Um Framework Fuzzy para Suporte à Seleção em Regime de Incerteza de Recursos na IoT
Resumo
A seleção de recursos no cenário de elevada escalabilidade da IoT, considerando as preferências dos clientes, se reveste de significativa complexidade. Por sua vez, a definição da prioridade dos atributos de QoS associados aos serviços disponibilizados pelos recursos, constitui por si só uma etapa desafiadora em todo processo. Considerando isso, este trabalho tem como objetivo geral a concepção de um Framework para gerenciamento de regras fuzzy, a ser integrado à proposta EXEHDA-RR, para o tratamento de incertezas na definição dos atributos de QoS. As preferências dos clientes são traduzidas em regras com o emprego de Lógica Fuzzy Tipo-2 Intervalar. Os resultados obtidos se mostraram promissores indicando a continuidade da pesquisa.
Palavras-chave:
gerenciamento de regras fuzzy, tratamento de incertezas, lógica fuzzy tipo-2 intervalar
Referências
Al-Masri, E. and Mahmoud, Q. H. (2008). Investigating web services on the world wide web. In Proceedings of the 17th international conference on World Wide Web, pages 795–804.
Alhadithy, H. and Al-Shargabi, B. (2018). Fuzzy rule based web service composition in cloud. In Proceedings of the First International Conference on Data Science, E- learning and Information Systems, pages 1–4.
Barros, L. and Bassanezi, R. (2010). Tópicos de lógica fuzzy e biomatemática. UNI- CAMP/IMECC.
Belouaar, H., Kazar, O., and Kabachi, N. (2018). A new model for web services selection based on fuzzy logic. Courrier du Savoir, 1(26):393–400.
Branke, J. (2016). Mcda and multiobjective evolutionary algorithms. In Multiple Criteria Decision Analysis, pages 977–1008. Springer.
Dilli, R., Argou, A., Pernas, A., Reiser, R., and Yamin, A. (2018). EXEHDA-RR : Uma proposta para tratar incertezas e otimizar o processo de classificação de recursos na IoT. Simpósio Brasileiro de Computação Ubíqua - CSBC.
IHS Markit (2017). The Internet of Things: a movement, not a market.
Jatoth, C., Gangadharan, G., Fiore, U., and Buyya, R. (2019). Selcloud: a hybrid multi-criteria decision-making model for selection of cloud services. Soft Computing, 23(13):4701–4715.
Khutade, P. A. and Phalnikar, R. (2014). QoS Aware Web Service Selection and Ran- king Framework Based on Ontology. International Journal of Soft Computing and Engineering (IJSCE), 4(3):77–81.
Kumar, R. R., Mishra, S., and Kumar, C. (2017). Prioritizing the solution of cloud ser- vice selection using integrated MCDM methods under Fuzzy environment. Journal of Supercomputing, 73(11):4652–4682.
Mamdani, E. H. and Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies.
Mendel, J. M. (2017). Uncertain Rule-Based Fuzzy Systems: Introduction and New Di- rections. Springer International Publishing, 2 edition.
Priya, N. H. and Chandramathi, S. (2014). QoS Based Optimal Selection of Web Services Using Fuzzy Logic. Journal of Emerging Technologies in Web Intelligence 6.3 (2014): 331-339, 6(3):331–339.
Silva, M., Cardoso, M. A., Machado, M. C., and Ferreira, A. P. L. (2019). Sistema de inferência fuzzy para estimativa de crescimento populacional. Anais do Salão Interna- cional de Ensino, Pesquisa e Extensão, 11(2).
Wagner, C. (2013). Juzzy - A Java based toolkit for Type-2 Fuzzy Logic. Proceedings of the 2013 IEEE Symposium on Advances in Type-2 Fuzzy Logic Systems, T2FUZZ 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013, 1(April 2013):45–52.
Xu, Z. and Yager, R. R. (2006). Some geometric aggregation operators based on intuitio- nistic fuzzy sets. International Journal of General Systems, 35(4):417–433.
Zumelzu, N., Bedregal, B., Mansilla, E., Bustince, H., and Díaz, R. (2020). Admissible orders on fuzzy numbers. arXiv preprint arXiv:2003.01530.
Alhadithy, H. and Al-Shargabi, B. (2018). Fuzzy rule based web service composition in cloud. In Proceedings of the First International Conference on Data Science, E- learning and Information Systems, pages 1–4.
Barros, L. and Bassanezi, R. (2010). Tópicos de lógica fuzzy e biomatemática. UNI- CAMP/IMECC.
Belouaar, H., Kazar, O., and Kabachi, N. (2018). A new model for web services selection based on fuzzy logic. Courrier du Savoir, 1(26):393–400.
Branke, J. (2016). Mcda and multiobjective evolutionary algorithms. In Multiple Criteria Decision Analysis, pages 977–1008. Springer.
Dilli, R., Argou, A., Pernas, A., Reiser, R., and Yamin, A. (2018). EXEHDA-RR : Uma proposta para tratar incertezas e otimizar o processo de classificação de recursos na IoT. Simpósio Brasileiro de Computação Ubíqua - CSBC.
IHS Markit (2017). The Internet of Things: a movement, not a market.
Jatoth, C., Gangadharan, G., Fiore, U., and Buyya, R. (2019). Selcloud: a hybrid multi-criteria decision-making model for selection of cloud services. Soft Computing, 23(13):4701–4715.
Khutade, P. A. and Phalnikar, R. (2014). QoS Aware Web Service Selection and Ran- king Framework Based on Ontology. International Journal of Soft Computing and Engineering (IJSCE), 4(3):77–81.
Kumar, R. R., Mishra, S., and Kumar, C. (2017). Prioritizing the solution of cloud ser- vice selection using integrated MCDM methods under Fuzzy environment. Journal of Supercomputing, 73(11):4652–4682.
Mamdani, E. H. and Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies.
Mendel, J. M. (2017). Uncertain Rule-Based Fuzzy Systems: Introduction and New Di- rections. Springer International Publishing, 2 edition.
Priya, N. H. and Chandramathi, S. (2014). QoS Based Optimal Selection of Web Services Using Fuzzy Logic. Journal of Emerging Technologies in Web Intelligence 6.3 (2014): 331-339, 6(3):331–339.
Silva, M., Cardoso, M. A., Machado, M. C., and Ferreira, A. P. L. (2019). Sistema de inferência fuzzy para estimativa de crescimento populacional. Anais do Salão Interna- cional de Ensino, Pesquisa e Extensão, 11(2).
Wagner, C. (2013). Juzzy - A Java based toolkit for Type-2 Fuzzy Logic. Proceedings of the 2013 IEEE Symposium on Advances in Type-2 Fuzzy Logic Systems, T2FUZZ 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013, 1(April 2013):45–52.
Xu, Z. and Yager, R. R. (2006). Some geometric aggregation operators based on intuitio- nistic fuzzy sets. International Journal of General Systems, 35(4):417–433.
Zumelzu, N., Bedregal, B., Mansilla, E., Bustince, H., and Díaz, R. (2020). Admissible orders on fuzzy numbers. arXiv preprint arXiv:2003.01530.
Publicado
30/06/2020
Como Citar
FERNANDES, Patrick; DILLI, Renato; REISER, Renata; YAMIN, Adenauer.
Um Framework Fuzzy para Suporte à Seleção em Regime de Incerteza de Recursos na IoT. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 47. , 2020, Cuiabá.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 151-161.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2020.11325.
