EXEHDA-RR: Uma proposta para tratar incertezas e otimizar o processo de classificação de recursos na IoT
Resumo
A Internet das Coisas (IoT) é caracterizada por uma ampla gama de recursos conectados à Internet, solicitando e fornecendo serviços simultaneamente. Diante desse cenário, selecionar adequadamente os recursos que melhor atendam às demandas dos usuários tem sido um desafio de pesquisa relevante e atual. Este artigo apresenta o EXEHDA-RR, uma proposta para classificar e selecionar o recurso mais apropriado, aplicando lógica fuzzy para resolver incertezas na definição de pesos ideais para os atributos de QoS, e agregando aprendizado de máquina à pré-classificação dos recursos visando reduzir o custo computacional gerado pelos algoritmos MCDA. Os resultados experimentais da pré-classificação mostram a eficiência do modelo proposto.
Referências
Al-Masri, E. and Mahmoud, Q. H. (2007). QoS-based discovery and ranking of Web services. In Proceedings - International Conference on Computer Communications and Networks, ICCCN, pages 529–534.
Almulla, M., Yahyaoui, H., and Al-Matori, K. (2015). A new fuzzy hybrid technique for ranking real world Web services. Knowledge-Based Systems, 77:1–15.
Argou, A., Dilli, R., Reiser, R., and Yamin, A. (2017). Lógica Fuzzy na Definição da Imortância dos Atributos de QoS na Seleção de Recursos da IoT. In IV Workshop-Escola de Informática Teórica, pages 173–180.
Davet, P. T. (2015). Uma Contribuicao à Inclusão Digital dos Métodos de Pesquisa Agropecuário no Cenário da IoT.
Dilli, R., Filho, H. K., Pernas, A. M., and Yamin, A. (2017). EXEHDA-RR: Machine Learning and MCDA with Semantic Web in IoT Resources Classification. WebMedia ’17, October 17–20, 2017, Gramado, Brazil.
García, J. M. (2012). Improving Semantic Web Services Discovery and Ranking. PhD thesis, University of Seville.
Gomes, P., Cavalcante, E., Batista, T., Taconet, C., Chabridon, S., Conan, D., Delicato, F., and Pires, P. (2016). A QoC-Aware Discovery Service for the Internet of Things. Ubiquitous Computing and Ambient Intelligence, 7656:344–355.
IHS Markit (2017). The Internet of Things : a movement , not a market. Technical report.
Maheswari, S. and Karpagam, G. R. (2015). Comparative analysis of semantic web service selection methods.
Perera, C., Zaslavsky, A., Christen, P., Compton, M., and Georgakopoulos, D. (2013). Context-aware sensor search, selection and ranking model for internet of things middleware. In Proceedings - IEEE International Conference on Mobile Data Management, volume 1, pages 314–322.
Salah, N. B. and Saadi, I. B. (2016). Fuzzy AHP for Learning Service Selection in Context-Aware Ubiquitous Learning Systems. 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Comp., Adv. and Trusted Comp., Scalable Comp. and Communications, Cloud and Big Data Comp., Internet of People, and Smart World Congress, pages 171–179.
Schröpfer, C., Schönherr, M., Offermann, P., and Ahrens, M. (2006). A Flexible Approach to Service Management-Related Service Description in SOAs. In CEUR Workshop Proceedings, volume 234.
Suchithra, M. and Ramakrishnan, M. (2015). A survey on different web service discovery techniques.
Vaadaala, V. (2013). Classification of Web Services Using JForty Eight. A National Conference on Recent Trends in Computer Science and Technology International Journal of Electronics Communication and Computer Engineering, 4(6):181–184.
Almulla, M., Yahyaoui, H., and Al-Matori, K. (2015). A new fuzzy hybrid technique for ranking real world Web services. Knowledge-Based Systems, 77:1–15.
Argou, A., Dilli, R., Reiser, R., and Yamin, A. (2017). Lógica Fuzzy na Definição da Imortância dos Atributos de QoS na Seleção de Recursos da IoT. In IV Workshop-Escola de Informática Teórica, pages 173–180.
Davet, P. T. (2015). Uma Contribuicao à Inclusão Digital dos Métodos de Pesquisa Agropecuário no Cenário da IoT.
Dilli, R., Filho, H. K., Pernas, A. M., and Yamin, A. (2017). EXEHDA-RR: Machine Learning and MCDA with Semantic Web in IoT Resources Classification. WebMedia ’17, October 17–20, 2017, Gramado, Brazil.
García, J. M. (2012). Improving Semantic Web Services Discovery and Ranking. PhD thesis, University of Seville.
Gomes, P., Cavalcante, E., Batista, T., Taconet, C., Chabridon, S., Conan, D., Delicato, F., and Pires, P. (2016). A QoC-Aware Discovery Service for the Internet of Things. Ubiquitous Computing and Ambient Intelligence, 7656:344–355.
IHS Markit (2017). The Internet of Things : a movement , not a market. Technical report.
Maheswari, S. and Karpagam, G. R. (2015). Comparative analysis of semantic web service selection methods.
Perera, C., Zaslavsky, A., Christen, P., Compton, M., and Georgakopoulos, D. (2013). Context-aware sensor search, selection and ranking model for internet of things middleware. In Proceedings - IEEE International Conference on Mobile Data Management, volume 1, pages 314–322.
Salah, N. B. and Saadi, I. B. (2016). Fuzzy AHP for Learning Service Selection in Context-Aware Ubiquitous Learning Systems. 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Comp., Adv. and Trusted Comp., Scalable Comp. and Communications, Cloud and Big Data Comp., Internet of People, and Smart World Congress, pages 171–179.
Schröpfer, C., Schönherr, M., Offermann, P., and Ahrens, M. (2006). A Flexible Approach to Service Management-Related Service Description in SOAs. In CEUR Workshop Proceedings, volume 234.
Suchithra, M. and Ramakrishnan, M. (2015). A survey on different web service discovery techniques.
Vaadaala, V. (2013). Classification of Web Services Using JForty Eight. A National Conference on Recent Trends in Computer Science and Technology International Journal of Electronics Communication and Computer Engineering, 4(6):181–184.
Publicado
22/07/2018
Como Citar
DILLI, Renato; ARGOU, Amanda; PERNAS, Ana; REISER, Renata; YAMIN, Adenauer.
EXEHDA-RR: Uma proposta para tratar incertezas e otimizar o processo de classificação de recursos na IoT. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 10. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 81-90.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2018.3290.
