Skip to main content
SpringerLink
Log in

QoS provisioning framework for service-oriented internet of things (IoT)

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The proliferation of ubiquitous sensing technology is bringing a rising number of the innovative models that have unique characteristics of the utility computing. These models have offered great opportunities to improve IT industries and business processes through the convergence of cloud computing and internet of things (IoT). Although this convergence establishes seamless intelligent interaction among physical and virtual entities, it has difficulty not only to meet the required level of quality of service(QoS) but also to satisfy the user’s complex demands. As a result, the predisposition to create a dynamic service-oriented environment has become a fundamental design issue. The main objective of this study is to introduce a dynamic QoS provisioning framework (QoPF) for service-oriented IoT using backtracking search optimization algorithm (BSOA). The QoPF framework is proposed to maximize the composite service quality in IoT application layer by making a balance between service reliability and acceptable cost of the computational time. The effectiveness of the QoPF framework is evaluated using a number of performance metrics such as throughput, delay time, and jitter. The experimental results demonstrate that worthiness of the QoPF to meet QoS requirements more than other state-of-the-art techniques in the literature review.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Saied, Y.B., Olivereau, A., Zeghlache, D., Laurent, M.: Trust management system design for the internet of things: a context-aware and multi-service approach. Comput. Secur. 39, 351 (2013)

    Article  Google Scholar 

  2. Thar, B., Muhammad, A., Hissam, T., Bandar, A., Rajkumar, B.: An energy-aware service composition algorithm for multiple cloud-based IoT applications. J. Netw. Comput. Appl. 89, 96–108 (2017)

    Article  Google Scholar 

  3. Asyabi, E., Azhdari, A., Dehsangi, M., Khan, M.G., Sharifi, M., Azhari, S.V.: Kani: a qos-aware hypervisor-level scheduler for cloud computing environments. Clust. Comput. 19(2), 567 (2016)

    Article  Google Scholar 

  4. Asghari, P., Rahmani, A.M., Javadi, H.H.S.: Service composition approaches in iot: a systematic review. J. Netw. Comput. Appl. 120, 61 (2018)

    Article  Google Scholar 

  5. Civicioglu, P.: Backtracking search optimization algorithm for numerical optimization problems. Appl. Math. Comput. 219(15), 8121 (2013)

    MathSciNet  MATH  Google Scholar 

  6. Ming, Z., Yan, M.: QoS-aware computational method for iot composite service. J. China Univ. Posts Telecommun. 20, 35 (2013)

    Article  Google Scholar 

  7. Braden, R., Clark, D.D., Shenker, S.: Integrated services in the internet architecture: an overview. RFC 1633, 1–33 (1994)

    Google Scholar 

  8. Ali, Z.H., Ali, H.A., Badawy, M.M.: A new proposed the internet of things (iot) virtualization framework based on sensor-as-a-service concept. Wirel. Pers. Commun. 97(1), 1419 (2017)

    Article  Google Scholar 

  9. Li, L., Li, S., Zhao, S.: QoS-aware scheduling of services-oriented internet of things. IEEE Trans. Ind. Inform. 10(2), 1497 (2014)

    Article  MathSciNet  Google Scholar 

  10. Haikal, A.Y., Badawy, M., Ali, H.A.: Towards internet QoS provisioning based on generic distributed QoS adaptive routing engine. Sci. World J. 2014, 29 (2014)

    Article  Google Scholar 

  11. Jin, J., Gubbi, J., Luo, T., Palaniswami, M.: 2012 International Symposium on Communications and Information Technologies (ISCIT) (IEEE), pp. 956–961 (2012)

  12. Razzaque, M.A., Milojevic-Jevric, M., Palade, A., Clarke, S.: Middleware for internet of things: a survey. IEEE Internet Things J. 3(1), 70 (2016)

    Article  Google Scholar 

  13. Zhou, Z., Zhao, D., Liu, L., Hung, P.C.: Energy-aware composition for wireless sensor networks as a service. Future Gener. Comput. Syst. 80, 299 (2018)

    Article  Google Scholar 

  14. Jian, C., Li, M., Kuang, X.: Edge cloud computing service composition based on modified bird swarm optimization in the internet of things. Cluster Comput. (2018). https://doi.org/10.1007/s10586-017-1630-9

    Article  Google Scholar 

  15. Montori, F., Bedogni, L., Bononi, L.: A collaborative internet of things architecture for smart cities and environmental monitoring. IEEE Internet Things J. 5(2), 592 (2018)

    Article  Google Scholar 

  16. Li, Q., Dou, R., Chen, F., Nan, G.: A qos-oriented web service composition approach based on multi-population genetic algorithm for internet of things. Int. J. Comput. Intell. Syst. 7(sup2), 26 (2014)

    Article  Google Scholar 

  17. Salman, A.A., Ahmad, I., Omran, M.G., Mohammad, M.G.: Frequency assignment problem in satellite communications using differential evolution. Comput. Oper. Res. 37(12), 2152 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  18. De Falco, I., Della Cioppa, A., Maisto, D., Tarantino, E.: Differential evolution as a viable tool for satellite image registration. Appl. Soft Comput. 8(4), 1453 (2008)

    Article  MATH  Google Scholar 

  19. Wang, X., Wang, S., Ma, J.J.: An improved co-evolutionary particle swarm optimization for wireless sensor networks with dynamic deployment. Sensors 7(3), 354 (2007)

    Article  Google Scholar 

  20. Sousa, T., Silva, A., Neves, A.: Particle swarm based data mining algorithms for classification tasks. Parallel Comput. 30(5–6), 767 (2004)

    Article  Google Scholar 

  21. Srinivas, M., Patnaik, L.M.: Genetic algorithms: a survey. Computer 27(6), 17 (1994)

    Article  Google Scholar 

  22. Schaffer, J.D., Whitley, D., Eshelman, L.J.: International Workshop on Combinations of Genetic Algorithms and Neural Networks, 1992, COGANN-92. (IEEE), pp. 1–37 (1992)

  23. Yang, S.: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation (ACM, 2015), pp. 629–649

  24. Rajasekhar, A., Lynn, N., Das, S., Suganthan, P.N.: Computing with the collective intelligence of honey bees—a survey. Swarm Evol. Comput. 32, 25–48 (2017)

    Article  Google Scholar 

  25. Guo, H., Hsu, W.: Join Workshop on Real Time Decision Support and Diagnosis Systems (2002)

  26. Mohan, B.C., Baskaran, R.: A survey: ant colony optimization based recent research and implementation on several engineering domain. Expert Syst. Appl. 39(4), 4618 (2012)

    Article  Google Scholar 

  27. He, X., Gao, X., Zhang, Y., Zhou, Z.H., Liu, Z.Y., Fu, B., Hu, F., Zhang, Z.: Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques: 5th International Conference, IScIDE 2015, Suzhou, China, June 14–16, 2015, Revised Selected Papers, vol. 9243 (2015)

  28. Guney, K., Durmus, A., Basbug, S.: Backtracking search optimization algorithm for synthesis of concentric circular antenna arrays. Int. J. Antennas Propag 2014, 11 (2014)

    Google Scholar 

  29. Shafiullah, M., Abido, M., Coelho, L.: 18th International Conference on Intelligent System Application to Power Systems (ISAP), 2015 (IEEE), pp. 1–6 (2015)

  30. Li, H., Zhu, G., Zhao, Y., Dai, Y., Tian, W.: Energy-efficient and qos-aware model based resource consolidation in cloud data centers. Clust. Comput. 20(3), 2793 (2017)

    Article  Google Scholar 

  31. Kenniche, H., Ravelomananana, V.: 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE) (IEEE), vol. 4, pp. 103–107 (2010)

  32. Yang, X., Tao, X., Dutkiewicz, E., Huang, X., Guo, Y.J., Cui, Q.: Energy-efficient distributed data storage for wireless sensor networks based on compressed sensing and network coding. IEEE Trans. Wirel. Commun. 12(10), 5087 (2013)

    Article  Google Scholar 

  33. NS. Ns2 offical web site. https://ns2tutor.weebly.com/wireless-simulation.html

Download references

Author information

Authors and Affiliations

  1. Computer Engineering and Control System Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt

    Mahmoud M. Badawy, Zainab H. Ali & Hesham A. Ali

Authors
  1. Mahmoud M. Badawy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zainab H. Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hesham A. Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mahmoud M. Badawy.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Badawy, M.M., Ali, Z.H. & Ali, H.A. QoS provisioning framework for service-oriented internet of things (IoT). Cluster Comput 23, 575–591 (2020). https://doi.org/10.1007/s10586-019-02945-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-019-02945-x

Keywords

Search

Navigation