Abstract
The Internet of Things (IoT) is characterized by a broad range of resources connected to the Internet, requesting and providing services simultaneously. Given this scenario, suitably selecting the resources that best meet users’ demands has been a relevant and current research challenge. Based on the non-functional parameters of Quality of Service (QoS), IoT plays an important role in the ranking of these resources according to the offered services. This paper presents a proposal to classify and select the most appropriate resource for the client’s request, applying fuzzy logic to address uncertainties in the definition of ideal weights for QoS attributes, and aggregating machine learning to the pre-classification of EXEHDA middleware resources, in order to reduce the computational cost generated by MCDA algorithms. As the main contribution, the pre-classification of new resources of the EXEHDA-RR is presented. The experimental results show the efficiency of the proposed model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
BIS Research: Global Sensors in Internet of Things (IoT) Devices Market, Analysis & Forecast: 2016 to 2022. Technical report (2017)
GarcĂa, J.M.: Improving Semantic Web Services Discovery and Ranking. Ph.D. thesis, University of Seville (2012)
Schröpfer, C., Schönherr, M., Offermann, P., Ahrens, M.: A flexible approach to service management-related service description in SOAs. In: CEUR Workshop Proceedings. Vol. 234 (2006)
Nakamura, L.H., Estrella, J.C., Santana, M.J., Santana, R.H.: Using semantic web for selection of web services with QoS. In: WebMedia’11, Proceedings of the 17th Brazilian Symposium on Multimedia and the Web, pp. 4–7 (2011)
Khutade, P.A., Phalnikar, R.: QoS aware web service selection and ranking framework based on ontology. Int. J. Soft Comput. Eng. (IJSCE) 4(3), 77–81 (2014)
Lopes, J.B.: Uma Arquitetura para Provimento de CiĂŞncia de Situação Direcionada Ă s Aplicações UbĂquas na Infraestrutura da Internet das Coisas. Ph.D. thesis, Universidade Federal do Rio Grande do Sul (2016)
Alabool, H.M., Mahmood, A.K.: Trust-based service selection in public cloud computing using fuzzy modified VIKOR method. Aust. J. Basic Appl. Sci. 7(9), 211–220 (2013)
Figueira, J., Greco, S., Ehrgott, M.: Multiple Criteria Decision Analysis: State of the Art Surveys, vol. 78. Springer, New York (2005). https://doi.org/10.1007/b100605
Tzeng, G.H., Huang, J.J.: Multiple Attribute Decision Making: Methods and Applications. A Chapman & Hall book. Taylor & Francis, Boco Raton (2011)
Al-Masri, E., Mahmoud, Q.H.: QoS-based discovery and ranking of Web services. In: Proceedings - International Conference on Computer Communications and Networks, ICCCN, pp. 529–534 (2007)
Liu, Y., Ngu, A.H., Zeng, L.Z.: QoS computation and policing in dynamic web service selection. In: Proceedings of the 13th International World Wide Web Conference on Alternate Track Papers & Posters, pp. 66–73 (2004)
Dilli, R., Filho, H.K., Pernas, A.M., Yamin, A.: EXEHDA-RR: machine learning and MCDA with semantic web in IoT resources classification. In: Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web. WebMedia 2017, pp. 293–300. ACM, New York (2017)
Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann Publishers Inc., Burlington (2011)
Maheswari, S., Karpagam, G.R.: Comparative analysis of semantic web service selection methods (2015)
Salah, N.B., Saadi, I.B.: Fuzzy AHP for learning service selection in context-aware ubiquitous learning systems. In: International IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress, pp. 171–179 (2016)
Perera, C., Zaslavsky, A., Christen, P., Compton, M., Georgakopoulos, D.: Context-aware sensor search, selection and ranking model for internet of things middleware. In: Proceedings - IEEE International Conference on Mobile Data Management. vol. 1, pp. 314–322 (2013)
Gomes, P., Cavalcante, E., Batista, T., Taconet, C., Chabridon, S., Conan, D., Delicato, F., Pires, P.: A QoC-aware discovery service for the internet of things. Ubiquitous Comput. Ambient Intell. 7656, 344–355 (2016)
Almulla, M., Yahyaoui, H., Al-Matori, K.: A new fuzzy hybrid technique for ranking real world web services. Knowl.-Based Syst. 77, 1–15 (2015)
Nunes, L.H., Estrella, J.C., Perera, C., Reiff-Marganiec, S., Delbem, A.N.: Multi-criteria IoT resource discovery: a comparative analysis. pp. 1–16. Wiley InterScience (2016)
Suchithra, M., Ramakrishnan, M.: A survey on different web service discovery techniques (2015)
Vaadaala, V.: Classification of web services using Jforty eight. Natl. Conf. Recent Trends Comput. Sci. Technol. Int. J. Electron. Commun. Comput. Eng. 4(6), 181–184 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Dilli, R., Argou, A., Reiser, R., Yamin, A. (2018). IoT Resources Ranking: Decision Making Under Uncertainty Combining Machine Learning and Fuzzy Logic. In: Barreto, G., Coelho, R. (eds) Fuzzy Information Processing. NAFIPS 2018. Communications in Computer and Information Science, vol 831. Springer, Cham. https://doi.org/10.1007/978-3-319-95312-0_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-95312-0_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95311-3
Online ISBN: 978-3-319-95312-0
eBook Packages: Computer ScienceComputer Science (R0)