Fault Tolerance and QoS based Pervasive Computing using Markov State Transition Model

  • Authors

    • Beaulah David Karpagam Academy of Higher Education
    • Dr R. Santhosh Karpagam Academy of Higher Education
  • Fault-Tolerance, Pervasive Computing Environments, Quality of Service (QoS), Markov State Transition, Mobile Nodes.
  • Fault-tolerance is significant in pervasive computing environments. Recently, few research works has been developed for reducing the fault, occurring in pervasive computing. However, there is a need for a fault tolerance mechanism to reduce the link failures and unwanted mobile node access (in pervasive computing environment). In order to overcome these limitations, Markov State Transition Based Fault Tolerance (MST-FT) Model is proposed. The main objective of MST-FT Model is to achieve resource efficient QoS in pervasive computing environment by avoiding the link failures and unwanted mobile node usages. Initially, the optimization of link failures is achieved by maintaining Markov chain of high energy mobile nodes on the wireless network communication path. The mobile nodes with higher energy and minimal drain rate are combined to form a chain in its corresponding path of communication in order to minimize the link failures in pervasive computing. Next, the inappropriate mobile node usage is avoided by selecting only the authorized mobile nodes for Markov chain construction to effective network communication, which resulting in improved fault tolerant rate. Therefore, MST-FT Model provides higher resource efficient QoS as compared to existing works. The performance of MST-FT Model is measured in terms of fault tolerant rate, execution time, energy consumption rate and quality of service level. The simulation results show that the MST-FT Model is able to improve the fault tolerant rate by 13% and also reduces the energy consumption rate of resource efficient QoS by 25%, when compared to previous works.



  • References

    1. [1] Salaja Silas, Kirubakaran Ezra and Elijah Blessing Rajsingh (2012), “A novel fault tolerant service selection framework for pervasive computingâ€, Human-centric Computing and Information Sciences, Springer, Pages 1-14. https://doi.org/10.1186/2192-1962-2-5.

      [2] P. Kumaran and R. Shriram (2011), “Critical Aware Community Based Parallel Service Composition Model for Pervasive Computing Environmentâ€, Advances in Parallel Distributed Computing, Springer, Pages 112-121. https://doi.org/10.1007/978-3-642-24037-9_12.

      [3] Gustavo G. Pascual, Lidia Fuentes, Mónica Pinto (2011), “Towards a Reconfigurable Middleware Architecture for Pervasive Computing Systemsâ€, Advanced Information Systems Engineering Workshops, Springer, Pages 318-329.https://doi.org/10.1007/978-3-642-22056-2_35.

      [4] R.S. Shaji, Dr. R.S. Rajesh and B Ramakrishnan (2010), SFUSP: A Fault-tolerant Routing Scheme for path establishment among Mobile Devices in Pervasive Spacesâ€, Journal of Computing, Volume 2, Issue 11, Pages 40-49.

      [5] Hen-I Yang, Raja Bose, Abdelsalam (Sumi) Helal, Jinchun Xia, Carl K. Chang (2009), “Fault-Resilient Pervasive Service Compositionâ€, Advanced Intelligent Environments, Springer, Pages 195-223.

      [6] Nlerum Promise. A. And Onuodu Friday E. (2015), “Service Preferences in Pervasive Computing Environments; A Theoretical Approachâ€, International Journal of Innovative Science, Engineering & Technology, Vol. 2 Issue 9, Pages 408-416.

      [7] Shameem Ahmed, MoushumiSharmin, and Sheikh I. Ahamed (2007), “ETS (Efficient, Transparent, and Secured) Self-healing Service for Pervasive Computing Applicationsâ€, International Journal of Network Security, Volume 4, Issue 3, Pages 271–281.

      [8] Yu Wang, Hongyi Wu (2007), “Delay/Fault-Tolerant Mobile Sensor Network (DFT-MSN): A New Paradigm for Pervasive Information Gathering†IEEE Transactions on Mobile Computing, Volume 6, Issue 9, Pages 1021-1034.https://doi.org/10.1109/TMC.2007.1006.

      [9] Youssif B. Al-Nashif, Salim Hariri, MazinYousif (2008), “Anomaly-based Fault Detection in Pervasive Computing Systemâ€, Research Gate, Pages 147-155.

      [10] Neal N. Xiong, Hongju Cheng, Sajid Hussain, and Yanzhen Qu (2013), “Fault-Tolerant and Ubiquitous Computing in Sensor Networksâ€, Hindawi Publishing Corporation, International Journal of Distributed Sensor Networks, Volume 2013, Article ID 524547, 2 pages.

      [11] Jens Kamenik (2009), “Energy optimized fault tolerance for pervasive communication spacesâ€, IEEE International Conference on Pervasive Computing and Communications, 2009. PerCom 2009, Pages 9-13.https://doi.org/10.1109/PERCOM.2009.4912814.

      [12] Eun-Kyung Kim, Yoonhee Kim (2007), “Autonomic service reconfiguration for fault-tolerant ubiquitous computingâ€, Research Gate, Volume: 10.

      [13] HaibinCai, Chao Peng, Yue Zhang, Linhua Jiang (2014), “A Component-based Intelligent Seamless Service Migration Mechanism and Flexible Communication Protocol in Pervasive Computing Systems†International Journal of Computational Intelligence Systems, Volume 7, Issue 3, Pages 493-505.https://doi.org/10.1080/18756891.2013.864480.

      [14] Sheikh I. Ahamed, MoushumiSharmin (2008), “A trust-based secure service discovery (TSSD) model for pervasive computingâ€, Computer Communications, Springer, Volume 31, Pages 4281–4293. https://doi.org/10.1016/j.comcom.2008.07.014.

      [15] HaibinCai, Chao Peng, Linhua Jiang, Yue Zhang (2012), “A Novel Self-Adaptive Fault-Tolerant Mechanism and Its Application for a Dynamic Pervasive Computing Environmentâ€, 2012 15th IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops (ISORCW), Pages 48-52.

      [16] Kimberly Garcıa, Sonia Mendoza, Dominique Decouchant, Patrick Brezillon (2016), “Facilitating resource sharing and selection in ubiquitous multi-user environmentsâ€, Information Systems Frontiers, Springer, Pages 1–21.

      [17] A. Samydurai, C. Vijayakumaran,aG. Kumaresan, K. Revathi (2015), “Towards a Middleware for Resource Sharing in Collaboration of Pervasive Computingâ€, Procedia Computer Science, Elsevier, Volume 50, Pages 87-92. https://doi.org/10.1016/j.procs.2015.04.065.

      [18] MohcineMadkour, Mohamed Bakhouya, AbdelilahMaach, and Driss El Ghanami (2013), “An Approach for Context-Aware Service Selection Using QoS and User Preferencesâ€, Trends in Mobile Web Information Systems, Springer, Pages 110-119.https://doi.org/10.1007/978-3-319-03737-0_12.

      [19] Sonia Ben Mokhtar, Davy Preuveneers, Nikolaos Georgantas, Valerie Issarnya, Yolande Berbers (2008), “EASY: Efficient semAntic Service discoverY in pervasive computing environments with QoS and context supportâ€, Journal of Systems and Software, Volume 81, Issue 5, Pages 785–808. https://doi.org/10.1016/j.jss.2007.07.030.

      [20] Sarada Prasad Gochhayat, VenkatrarmPallapa (2015), “An Efficient QoS Support for Ubiquitous Networksâ€, IEEE Transactions on Emerging Topics in Computing, Volume: 3, Issue: 4, Pages 524 – 533.https://doi.org/10.1109/TETC.2015.2449669.

  • Downloads

  • How to Cite

    David, B., & R. Santhosh, D. (2018). Fault Tolerance and QoS based Pervasive Computing using Markov State Transition Model. International Journal of Engineering & Technology, 7(4), 2403-2409. https://doi.org/10.14419/ijet.v7i4.12664