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Reseach Article

Scalable and Self-Adaptive Service Selection Method for the Internet of Things

by Manel Mejri, Nadia Ben Azzouna
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 167 - Number 10
Year of Publication: 2017
Authors: Manel Mejri, Nadia Ben Azzouna
10.5120/ijca2017914542

Manel Mejri, Nadia Ben Azzouna . Scalable and Self-Adaptive Service Selection Method for the Internet of Things. International Journal of Computer Applications. 167, 10 ( Jun 2017), 43-49. DOI=10.5120/ijca2017914542

@article{ 10.5120/ijca2017914542,
author = { Manel Mejri, Nadia Ben Azzouna },
title = { Scalable and Self-Adaptive Service Selection Method for the Internet of Things },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2017 },
volume = { 167 },
number = { 10 },
month = { Jun },
year = { 2017 },
issn = { 0975-8887 },
pages = { 43-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume167/number10/27933-2017914542/ },
doi = { 10.5120/ijca2017914542 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:14:31.918038+05:30
%A Manel Mejri
%A Nadia Ben Azzouna
%T Scalable and Self-Adaptive Service Selection Method for the Internet of Things
%J International Journal of Computer Applications
%@ 0975-8887
%V 167
%N 10
%P 43-49
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Internet of Things goes beyond the regular Internet by offering new functionalities and creating new range of services provided by the deployed objects. Therefore, one of the most challenging issues is to select the best service among similar functionally available ones. In this paper, we propose to involve both artifcial intelligence through the use of Artifcial Neural Network (ANN) and multi criteria analysis through the use of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) model in order to return the best service to the requestor. First, The ANN is introduced as a predictive model to estimate the Qualities of services (QoS) according to user context, service context and network context. Second, the TOPSIS model evaluates, then aggregates these QoS values in order to provide the best service according to user preferences. To improve the scalability of the proposed service selection system we conduct a parallel implementation of the prototype.

References
  1. Zainab Aljazzaf. Bootstrapping quality of web services. Journal of King Saud University-Computer and Information Sciences, 27(3):323–333, 2015.
  2. Luigi Atzori, Antonio Iera, and Giacomo Morabito. The internet of things: A survey. Computer Networks, 54:2787–2805, 2010.
  3. Jianmin Bao, Yiming Ding, and Haifeng Hu. A new service selection algorithm in uspiot. In Computer Science and Automation Engineering (CSAE), 2012 IEEE International Conference on, volume 2, pages 22–26. IEEE, 2012.
  4. Harun Baraki, Diana Comes, and Kurt Geihs. Context-aware prediction of qos and qoe properties for web services. In Networked Systems (NetSys), 2013 Conference on, pages 102– 109, 2013.
  5. Peter Paul Beran, Elisabeth Vinek, Erich Schikuta, and Maria Leitner. An adaptive heuristic approach to service selection problems in dynamic distributed systems. 2012 ACM/IEEE 13th International Conference on Grid Computing, pages 66– 75, 2012.
  6. Rohit Chandra. Parallel programming in OpenMP. Morgan Kaufmann, 2001.
  7. Alfonso Garcia de Prado and Guadalupe Ortiz. Context-aware services: A survey on current proposals. The Third International Conferences on Advanced Service Computing, pages 104–109, 2011.
  8. Jeffrey Dean and Sanjay Ghemawat. Mapreduce: simplified data processing on large clusters. Communications of the ACM, 51(1):107–113, 2008.
  9. G´erard Dreyfus. Neural Networks. Springer Science, Paris, France, 2005.
  10. Randall Farmer and Bryce Glass. Building Web Reputation Systems. O’Reilly Media, Inc, 2010.
  11. Zhengdong Gao and Gengfeng Wu. Combining qos-based service selection with performance prediction. In e-Business Engineering, 2005. ICEBE 2005. IEEE International Conference on, pages 611–614. IEEE, 2005.
  12. Jeff Heaton. Encog: Library of interchangeable machine learning models for java and c#. arXiv preprint arXiv:1506.04776, 2015.
  13. T Jayalakshmi and A Santhakumaran. Statistical normalization and back propagation for classification. International Journal of Computer Theory and Engineering, 3(1):1793– 8201, 2011.
  14. Xiongnan Jin, Sejin Chun, Jooik Jung, and Kyong-Ho Lee. Iot selection based on physical service model and absolute dominance relationship. In Service-Oriented Computing and Applications (SOCA), 2014 IEEE 7th International Conference on, pages 65–72. IEEE, 2014.
  15. Sol Ji Kang, Sang Yeon Lee, and Keon Myung Lee. Performance comparison of openmp, mpi, and mapreduce in practical problems. Advances in Multimedia, 2014.
  16. KangChan Lee, JongHong Jeon, WonSeok Lee, Seong-Ho Jeong, and Sang-Won Park. QoS for Web Services: Requirements and Possible Approaches.World Wide Web Consortium (W3C) note. http://www.w3c.or.kr/kr-office/ TR/2003/ws-qos/, 2008. Accessed on September 27, 2015.
  17. Umardand Shripad Manikrao and T.V.Prabhakar. Dynamic selection ofweb services with recommendation system. Proceedings - International Conference on Next Generation Web Services Practices, NWeSP, pages 117–121, 2005.
  18. E. Michael Maximilien and Munindar P. Singh. Conceptual model of web service reputation. ACM SIGMOD Record, 31:36, 2002.
  19. M.Swarnamugi. Taxonomy of web service selection approaches. International Conference on Computing and information Technology (IC2IT-2013), pages 18–22, 2013.
  20. Nwe Htay Win NWE, Jian-min BAO, and CUI Gang. Flexible user-centric service selection algorithm for internet of things services. The Journal of China Universities of Posts and Telecommunications, 21:64–70, 2014.
  21. Maryam Saeedi, Zeqian Shen, and Neel Sundaresan. The value of feedback:an analysis of reputation system. pages 1– 41, 2013.
  22. Marc Snir. MPI–the Complete Reference: The MPI core, volume 1. MIT press, 1998.
  23. Lu Tan and Neng Wang. Future internet: The internet of things. 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE), 5:376–380, 2010.
  24. Jureen Thor, Siew-Hong Ding, and Shahrul Kamaruddin. Comparison of multi criteria decision making methods from the maintenance alternative selection perspective. The International Journal of Engineering And Science (IJES), 2(6):27– 34, 2013.
  25. Evangelos Triantaphyllou, B Shu, S Nieto Sanchez, and Tony Ray. Multi-criteria decision making: an operations research approach. Encyclopedia of electrical and electronics engineering, 15:175–186, 1998.
  26. Le-Hung Vu, Manfred Hauswirth, and Karl Aberer. Qosbased service selection and ranking with trust and reputation management. In On the Move to Meaningful Internet Systems 2005: CoopIS, DOA, and ODBASE, pages 466–483. Springer, 2005.
  27. Yao Wang and Julita Vassileva. Toward trust and reputation based web service selection: A survey. International Transactions on Systems Science and Applications (ITSSA), 2007.
  28. Qihui Wu, Guoru Ding, Yuhua Xu, Shuo Feng, Zhiyong Du, JinlongWang, and Keping Long. Cognitive internet of things: A new paradigm beyond connection. Internet of Things Journal, IEEE, 1:129–143, 2014.
  29. Hong Qing Yu and Stephan Reiff-Marganiec. Non-functional property based service selection: A survey and classification of approaches. In: Non Functional Properties and Service Level Agreements in Service Oriented Computing Workshop co-located with The 6th IEEE European Conference on Web Services, 12 - 14 Nov 2008, Ireland, Dublin, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Internet of Things non-functional properties QoS Contextual attributes preferences