Skip to main content

Advertisement

SpringerLink
Log in

Energy Efficient Service Selection from IoT Based on QoS Using HMM with KNN and XGBoost

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The data gathering and composite the services through the IoT devices are the significant need in current scenario. There are many existing systems which gather the data from IoT devices and provide as an analysis based on the service linked with them. The objective of this paper is to build a middleware for IOT tech stack, which can recognize different services and features and categorize them via a ranking solution similar to the page ranking algorithm. The service ranking algorithm (SRA) are linked with HMM to fixates on domain specific requirements and controls services based on said fixated domain. Hence, this algorithm has to be dynamic in nature and should be able to accommodate different domain schemas as possible, where the parameters of distinction for each domain is to be pre specified and the algorithm is to be tuned accordingly. Before ranking begins, selecting the relevant service based on the availability of services, the Service Provider has to decide the kind of services to be offered for the clients based on the weight’s reliability, completeness and energy availability. For implementing this, many intelligent systems are suggested to choose the low cost and high reliable services. In this chapter, a fuzzy rule-based K Nearest Neighbour Classifier is used for categorize the IoT service based on user request. In addition, XGBoost (Extreme Gradient Boosting) is used for dynamic service selection from the available categorized services based on response time and cost. Hidden Markov Model (HMM) is the prediction model used in this proposed work to solve the energy prediction problem.

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

Similar content being viewed by others

Data availability

Data sharing not applicable to this article as no datasets were generated or analyzed during current study.

Code availability

The experiments are conducted in Openstack environment which may available on request after publication.

References

  1. Perera, C., Zaslavsky, A., Christen, P., Compton, M., Georgakopoulos, D. (2013). Context-aware sensor search, selection and ranking model for internet of things middleware. In 2013 IEEE 14th international conference on mobile data management (vol 1, pp 314–322). IEEE.

  2. Stelmach P. (2013). Service composition scenarios in the internet of things paradigm. In Doctoral conference on computing, electrical and industrial systems (pp. 53–60), Springer, Berlin, Heidelberg.

  3. Shaoshuai, F., Wenxiao, S., Nan, W., & Yan, L. (2011). MODM-based evaluation model of service quality in the internet of things. Procedia Environmental Sciences, 11, 63–69.

    Article  Google Scholar 

  4. Shehu, U. G., Safdar, G. A., & Epiphaniou, G. (2015). Network aware composition for internet of thing services. Transactions on Networks and Communications, 3(1), 45–45.

    Google Scholar 

  5. Esposito, C., Ficco, M., Palmieri, F., & Castiglione, A. (2015). Smart cloud storage service selection based on fuzzy logic, theory of evidence and game theory. IEEE Transactions on Computers, 65(8), 2348–2362.

    Article  MathSciNet  Google Scholar 

  6. Li, L., Li, S., & Zhao, S. (2014). QoS-aware scheduling of services-oriented internet of things. IEEE Transactions on Industrial Informatics, 10(2), 1497–1505.

    Article  Google Scholar 

  7. Balasubramaniam, S., Jagannath, R. (2015). A service oriented iot using cluster controlled decision making. In: 2015 IEEE international advance computing conference (IACC) (pp. 558–563) IEEE

  8. Gungor, V. C., Sahin, D., Kocak, T., Ergut, S., Buccella, C., Cecati, C., & Hancke, G. P. (2013). A survey on smart grid potential applications and communication requirements. IEEE Transactions on Industrial Informatics, 9(1), 28–42.

    Article  Google Scholar 

  9. Xiang, C., Panlong, Y., Xuangou, W., Hong, H., & Shucheng, X. (2015). QoS-based service selection with lightweight description for large-scale service-oriented internet of things. Tsinghua Science and Technology, 20(4), 336–347.

    Article  Google Scholar 

  10. Eisa, M., Muhammad, Y., Kashinath, B., & Hong, Z. (2016) Trends and directions in cloud service selection. In: 2016 IEEE symposium on service-oriented system engineering (SOSE) (pp. 423–432) IEEE.

  11. Angelakis, V., Ioannis, A., Nikolaos, P., Emma, F., & Di, Y. (2016). Allocation of heterogeneous resources of an IoT device to flexible services. IEEE Internet of Things Journal, 3(5), 691–700.

    Article  Google Scholar 

  12. Han, D. M., & Lim, J. H. (2010). Design and implementation of smart home energy management systems based on zigbee. IEEE Transactions on Consumer Electronics, 56(3), 1417–1425.

    Article  Google Scholar 

  13. Rodríguez-Valenzuela, S., Holgado-Terriza, J., Muros-Cobos, J.L., & Gutiérrez-Guerrero, J.M. (2012). Data fusion mechanism based on a service composition model for the internet of things. Actas de las III Jornadas de Computación Empotrada (JCE), Septiembre, 19–21.

  14. Khanouche, M. E., Amirat, Y., Chibani, A., Kerkar, M., & Yachir, A. (2016). Energy-centered and QoS-aware services selection for Internet of Things. IEEE Transactions on Automation Science and Engineering, 13(3), 1256–1269.

    Article  Google Scholar 

  15. Wang, C. (2011). A QoS-aware middleware for dynamic and adaptive service execution.

  16. Corno, F., & Razzak, F. (2012). Intelligent energy optimization for user intelligible goals in smart home environments. IEEE Transactions on Smart Grid, 3(4), 2128–2135.

    Article  Google Scholar 

  17. Rault, T., Bouabdallah, A., & Challal, Y. (2014). Energy efficiency in wireless sensor networks: a top-down survey. Computer Networks, 67, 104–122.

    Article  Google Scholar 

  18. Akkaya, K., Guvenc, I., Aygun, R., Pala, N., & Kadri, A. (2015). IoT-based occupancy monitoring techniques for energy-efficient smart buildings. In Wireless communications and networking conference workshops (WCNCW), 2015 IEEE (pp. 58–63). IEEE.

  19. Machado, K., Rosário, D., Cerqueira, E., Loureiro, A. A., Neto, A., & de Souza, J. N. (2013). A routing protocol based on energy and link quality for internet of things applications. Sensors, 13(2), 1942–1964.

    Article  Google Scholar 

  20. Jahn, M., Jentsch, M., Prause, C.R., Pramudianto, F., AlAkkad, A., & Reiners, R. (2010). The energy aware smart home. In Future information technology (FutureTech), 2010 5th international conference on (pp. 1–8). IEEE.

  21. Byun, J., & Park, S., (2011). Development of a self-adapting intelligent system for building energy saving and context-aware smart services. IEEE Transactions on Consumer Electronics, 57(1).

  22. Jaithunbi, A. K., Sabena, S., & SaiRamesh, L.: Trust evaluation of public cloud service providers using genetic algorithm with intelligent rules. Wireless Personal Communications (2021): 1–15.

  23. Deng, Z., Xiaoshu, Z., Debo, C., Ming, Z., & Shichao, Z. (2016). Efficient kNN classification algorithm for big data. Neurocomputing, 195, 143–148.

    Article  Google Scholar 

Download references

Funding

There is no funding for carrying out this research work.

Author information

Authors and Affiliations

  1. Department of Information Science and Technology, CEG Campus, Anna University, Chennai, India

    L. SaiRamesh

  2. Department of Computer Science and Engineering, Anna University Regional Centre, Tirunelveli, India

    S. Sabena

  3. Department of Computer Applications, National Institute of Technology, Trichy, India

    K. Selvakumar

Authors
  1. L. SaiRamesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Sabena
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. Selvakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Selvakumar.

Ethics declarations

Conflict of interest

The authors declare that they do no have any conflict of interests.

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

SaiRamesh, L., Sabena, S. & Selvakumar, K. Energy Efficient Service Selection from IoT Based on QoS Using HMM with KNN and XGBoost. Wireless Pers Commun 124, 3591–3602 (2022). https://doi.org/10.1007/s11277-022-09527-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-022-09527-y

Keywords

Search

Navigation