Dynamic Service Adaptation Framework for Context Aware Mobile Cloud Learning Using Semantic-Based Approach

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    Current developments in IT involving mobile technology and cloud computing have also offered a modern way of teaching and learning. Effective learning methodologies that meet the learners' requirements as well as support learning resources (LR) with high flexibility, mobility and accessibility are currently the most key contribution of this technological advancement. Hence, Mobile Cloud Learning (MCL) has been introduced to boost the feature of m-learning that has already been on the market. MCL comprises of services that integrate with each other which are learning, assessment, feedback and analysis. These services need to be monitored, and continuous enhancement and evolvement are also required in the account of new learners’ requirements for the upgrading of services or maintaining the Quality of Services (QoS). Service-oriented systems need to be enhanced through a dynamic adaptation process during runtime to sense and react to the learners’ context or device’s context known as context awareness to provide a better user experience. This is where ontologies and rule-based approaches are used for context representation, descriptions, and reasoning mechanisms to support the semantic discovery and matching of services. QoS consideration is essential to ensure the efficiency and correctness of the services offered during the adaptation process. Thus, this paper describes dynamic adaptation framework that leverages on a semantic-based approach for context-aware mobile cloud learning. This framework aims to support in-service adaptation by considering contextual information and QoS using ontologies and rule-based approaches.


  • Keywords

    Dynamic Service Adaptation; Context Aware; Mobile Cloud Learning; Semantic-Based Approach

  • References

      [1] N. Ibrahim and F. Le Mou, “Semantic Service Substitution in Pervasive Environments,” Inderscience Enterp. Ltd, p. pp: 1-26, 2015.

      [2] G. H. Alferez, V. Pelechano, R. Mazo, C. Salinesi, and D. Diaz, “Dynamic adaptation of service compositions with variability models,” J. Syst. Softw., vol. 91, no. 1, pp. 24–47, 2014.

      [3] and O. L. T. Berners-Lee, J. Hendler, “The Semantic Web.,” Sci. Am., vol. 284, no. 5, pp. 34–43, 2001.

      [4] H. Nacer and D. Aissani, “Journal of Network and Computer Applications Semantic web services : Standards , applications , challenges and solutions,” J. Netw. Comput. Appl., vol. 44, pp. 134–151, 2014.

      [5] M. Uschold and R. Jasper, “A Framework for Understanding and Classifying Ontology Applications,” Proc. IJCAI-99 Work. Ontol. Probl. Stock. Sweden, vol. 18, pp. 1–12, 1999.

      [6] H. H. Madani, L. Jemni, B. E. N. Ayed, M. Jemni, and D. G. Sampson, “Towards Accessible and Personalized Mobile Learning for Learners with Disabilities,” Fourth Int. Conf. Inf. Commun. Technol. Access., 2013.

      [7] S. Gomez, P. Zervas, D. G. Sampson, and R. Fabregat, “Context-aware adaptive and personalized mobile learning delivery supported by UoLmP,” J. King Saud Univ. - Comput. Inf. Sci., vol. 26, no. 1, pp. 47–61, 2014.

      [8] A. Harchay, L. Cheniti-Belcadhi, and R. Braham, “A context-aware approach for personalized mobile self-assessment,” J. Univers. Comput. Sci., vol. 21, no. 8, pp. 1061–1085, 2015.

      [9] K. Karoudis and G. Magoulas, “Ubiquitous Learning Architecture to Enable Learning Path Design across the Cumulative Learning Continuum,” Informatics, vol. 3, no. 4, p. 19, 2016.

      [10] M. M. Fuad and D. Deb, “Cloud-Enabled Hybrid Architecture For In-Class Interactive Learning Using Mobile Device,” 5th IEEE Int. Conf. Mob. Cloud Comput. Serv. Eng., pp. 0–3, 2017.

      [11] R. R. Makwana and N. D. Mehta, “A Context-Aware Mobile Learning System Using Dynamic Content Adaptation for Personalized Learning,” Emerg. Trends Electr. Electron. Commun. Eng., vol. 416, no. 1, pp. 379–384, 2017.

      [12] R. K. Gurung, A. Alsadoon, P. W. C. Prasad, and A. Elchouemi, “Impacts of Mobile Cloud Learning (MCL) on Blended Flexible Learning (BFL),” in IDT 2016 - Proceedings of the International Conference on Information and Digital Technologies 2016, 2016, pp. 108–114.

      [13] M. Wang, Y. Chen, and M. Jahanzaib Khan, “Mobile Cloud Learning for Higher Education: A Case Study of Moodle in the Cloud,” J. Educ. Pract., vol. 7, p. 6, 2016.

      [14] H. Gao, H. Miao, and H. Zeng, “Service Reconfiguration Architecture Based on Probabilistic Modeling Checking,” 2014 IEEE Int. Conf. Web Serv., pp. 714–715, 2014.

      [15] M. Wang and J. W. P. Ng, “Intelligent mobile cloud education: Smart anytime-anywhere learning for the next generation campus environment,” Proc. - 8th Int. Conf. Intell. Environ. IE 2012, pp. 149–156, 2012.

      [16] M. Wang, Y. Chen, and M. J. Khan, “Mobile cloud learning for higher education: A case study of Moodle in the cloud,” Int. Rev. Res. Open Distrib. Learn., vol. 15, no. 2, 2014.

      [17] G. Woodill, “Unique Affordances of Mobile Unique Affordances of Mobile Learning Learning,” Mastering Mob. Learn. Tips Tech. Success, pp. 111–126, 2015.

      [18] P. Brézillon, “Focusing on Context in Human-Centered Computing,” IEEE Intell. Syst., vol. 18, no. 3, pp. 62–66, 2003.

      [19] B. N. Schilit, N. Adams, and R. Want, “Context-aware computing applications,” Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications. pp. 85--90, 1994.

      [20] A. K. Dey and G. D. Abowd, “Towards a Better Understanding of Context and Context-Awareness,” Comput. Syst., vol. 40, no. 3, pp. 304–307, 1999.

      [21] J. D. B. and X. C. Brown, Peter J., “Context-aware Applications- from the Laboratory to the Marketplace.pdf,” IEEE Personal Communications, vol. 4. pp. 58–64, 1997.

      [22] Y. H. Y. Hu and X. L. X. Li, “An Ontology Based Context-Aware Model for Semantic Web Services,” 2009 Second Int. Symp. Knowl. Acquis. Model., vol. 1, pp. 426–429, 2009.

      [23] S. Muhammad, N. Admodisastro, H. Osman, and N. M. Ali, “A Review of Dynamic Adaptation In Context Aware System Using Semantic-Based Approach,” J. Fundam. Appl. Sci. ISSN 1112-9867, no. Special Issue, pp. 1288–1301, 2017.

      [24] H. Guermah, T. Fissaa, H. Hafiddi, M. Nassar, and A. Kriouile, “A semantic approach for service adaptation in context-aware environment,” Procedia Comput. Sci., vol. 34, pp. 587–592, 2014.

      [25] H. Guermah, T. Fissaa, H. Hafiddi, M. Nassar, and A. Kriouile, “An Ontology Oriented Architecture for Context Aware Services Adaptation,” vol. 11, no. 2, pp. 24–33, 2014.

      [26] O. Hoftberger and R. Obermaisser, “Ontology-based runtime reconfiguration of distributed embedded real-time systems,” 16th IEEE Int. Symp. Object/Component/Service-Oriented Real-Time Distrib. Comput. ISORC 2013, 2014.

      [27] S. N. Han, G. M. Lee, and N. Crespi, “Semantic context-aware service composition for building automation system,” IEEE Trans. Ind. Informatics, vol. 10, no. 1, pp. 252–261, 2014.

      [28] M. N. Widad Ettazi, Hatim hafiddi, “A User Centered Design Approach for Transactional Service Adaptation in Context Aware Environment,” Int. Conf. Networked Syst., vol. 1, no. NETYS 2015: Networked Systems pp 479-484, pp. 1–18, 2015.

      [29] M. Ruta et al., “Semantic-enabled resource discovery, composition and substitution in 802.11 pervasive environments,” in ICSC 2009 - 2009 IEEE International Conference on Semantic Computing, 2009, pp. 503–508.

      [30] K. Amailef and J. Lu, “Ontology-supported case-based reasoning approach for intelligent m-Government emergency response services,” Decis. Support Syst., vol. 55, no. 1, pp. 79–97, 2013.

      [31] S. Paktinat, A. Salajeghe, M. A. Seyyedi, and Y. Rastegari, “Service-Based Application Adaptation Strategies : A Survey,” vol. 8, no. 8, pp. 1321–1325, 2014.

      [32] S. Lane, Q. Gu, P. Lago, and I. Richardson, “Towards a framework for the development of adaptable service-based applications,” Serv. Oriented Comput. Appl., vol. 8, no. 3, pp. 239–257, 2014.

      [33] A. Schmidt, “Interactive Context-Aware Systems Interacting with Ambient Intelligence,” Ambient Intell., no. March, pp. 159–178, 2005.

      [34] S. Peinado, G. Ortiz, and J. M. Dodero, “A metamodel and taxonomy to facilitate context-aware service adaptation,” Comput. Electr. Eng., vol. 44, pp. 262–279, 2015.

      [35] M. Al-Yahya, R. George, and A. Alfaries, “Ontologies in E-Learning: Review of the literature,” Int. J. Softw. Eng. its Appl., vol. 9, no. 2, pp. 67–84, 2015.

      [36] A. Casals, S. Paulo, and A. Alves Franco Brandão, “Modeling a Mobile Learning Context Data Ontology,” IEEE World Eng. Educ. Conf., 2017.

      [37] C. M. Chen and Y. L. Li, “Personalised context-aware ubiquitous learning system for supporting effective english vocabulary learning,” Interact. Learn. Environ., vol. 18, no. 4, pp. 341–364, 2010.

      [38] A. Karadimce and D. Davcev, “Adaptive multimedia content in mobile cloud computing environment,” Cloud Netw. (CLOUDNET), 2012 IEEE 1st Int. Conf., pp. 209–211, 2012.

      [39] G. Wu, J. Li, L. Feng, and K. Wang, “Identifying potentially important concepts and relations in an ontology,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 5318 LNCS, pp. 33–49, 2008.




Article ID: 23363
DOI: 10.14419/ijet.v7i4.31.23363

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.