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A Group Teaching Optimization-Based Approach for Energy and QoS-Aware Internet of Things Services Composition

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Abstract

Due to the dynamic nature of Internet of Things (IoT) services hosted by energy-constrained devices, the problem of composing services to provide added-value ones with a reduced energy consumption and a high quality of service (QoS) is attracting more attention. Several existing services composition approaches have limited computation time, QoS utility, and composition lifetime since they do not simultaneously address energy and user’s QoS constraints, consider all of the services during the composition process, or usually require tuning specific algorithm parameters. This paper proposes a group teaching-based energy efficient and QoS-aware services composition approach (GT-EQCA) to deal with the aforementioned limitations. To reduce the composition time, while increasing the composition lifetime and QoS utility, only the relevant services in terms of energy and QoS are considered during the composition process. Furthermore, the composition satisfying the QoS constraints with the highest utility in terms of QoS and energy is determined using the group teaching optimization method, which does not require adjusting specific parameters to achieve satisfactory performance. The large-scale simulation scenarios using a real dataset show that the GT-EQCA approach outperforms four baseline algorithms in terms of composition time, energy consumption, and the QoS utility of the composition.

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References

  1. Bisio, I., Garibotto, C., Grattarola, A., Lavagetto, F., Sciarrone, A.: Exploiting context-aware capabilities over the internet of things for industry 4.0 applications. IEEE Netw. 32(3), 101–107 (2018)

    Article  Google Scholar 

  2. Sisinni, E., Saifullah, A., Han, S., Jennehag, U., Gidlund, M.: Industrial internet of things: Challenges, opportunities, and directions. IEEE Trans. Ind. Inform. 14(11), 4724–4734 (2018)

    Article  Google Scholar 

  3. Khanouche, M.E., Atmani, N., Cherifi, A.: Improved teaching learning-based qos-aware services composition for internet of things. IEEE Syst. J. 14(3), 4155–4164 (2020)

    Article  Google Scholar 

  4. Guinard, D., Trifa, V., Karnouskos, S., Spiess, P., Savio, D.: Interacting with the soa-based internet of things: Discovery, query, selection, and on-demand provisioning of web services. IEEE Trans. Services Comput. 3(3), 223–235 (2010)

    Article  Google Scholar 

  5. Chandra, M., Agrawal, A., Kishor, A., Niyogi, R.: Web service selection with global constraints using modified gray wolf optimizer. In: 2016 Int. Conf. on Advances in Computing, Communications and Informatics (ICACCI), pp. 1989–1994 (2016). IEEE

  6. Wu, Q., Ishikawa, F., Zhu, Q., Shin, D.-H.: Qos-aware multigranularity service composition: Modeling and optimization. IEEE Trans. Syst., Man, and Cybern.: Syst. 46(11), 1565–1577 (2016)

    Article  Google Scholar 

  7. Tong, E., Chen, L., Li, H.: Energy-aware service selection and adaptation in wireless sensor networks with qos guarantee. IEEE Trans. Services Comput. 5(13), 829–842 (2020)

    Article  Google Scholar 

  8. Khanouche, M.E., Attal, F., Amirat, Y., Chibani, A., Kerkar, M.: Clustering-based and qos-aware services composition algorithm for ambient intelligence. Inf. Sci. 482, 419–439 (2019)

    Article  Google Scholar 

  9. Khanouche, M.E., Amirat, Y., Chibani, A., Kerkar, M., Yachir, A.: Energy-centered and qos-aware services selection for internet of things. IEEE Trans. Automat. Sci. Eng. 13(3), 1256–1269 (2016)

    Article  Google Scholar 

  10. Ardagna, D., Pernici, B.: Adaptive service composition in flexible processes. IEEE Trans. Software Eng. 33(6), 369–384 (2007)

    Article  Google Scholar 

  11. Razian, M., Fathian, M., Bahsoon, R., Toosi, A.N., Buyya, R.: Service composition in dynamic environments: A systematic review and future directions. J. Syst. Softw. 188, 111290 (2022)

    Article  Google Scholar 

  12. Kouicem, A., Khanouche, M.E., Tari, A.: Novel bat algorithm for qos-aware services composition in large scale internet of things. Clust. Comput. 25(5), 3683–3697 (2022)

    Article  Google Scholar 

  13. Alrifai, M., Risse, T., Nejdl, W.: A hybrid approach for efficient web service composition with end-to-end qos constraints. ACM Transact. Web (TWEB) 6(2), 7–1731 (2012)

    Google Scholar 

  14. Yuan, Y., Zhang, W., Zhang, X., Zhai, H.: Dynamic service selection based on adaptive global qos constraints decomposition. Symmetry 11(3), 403 (2019)

    Article  Google Scholar 

  15. Halfaoui, A., Hadjila, F., Didi, F.: Qos-aware web services selection based on fuzzy dominance. In: IFIP Int. Conf. on Computer Science and Its Applications, Cham, pp. 291–300 (2015). Springer

  16. Chattopadhyay, S., Banerjee, A.: Qos-aware automatic web service composition with multiple objectives. ACM Transact. Web (TWEB) 14(3), 1–38 (2020)

    Article  Google Scholar 

  17. Wang, H., Hu, X., Yu, Q., Gu, M., Zhao, W., Yan, J., Hong, T.: Integrating reinforcement learning and skyline computing for adaptive service composition. Inf. Sci. 519, 141–160 (2020)

    Article  Google Scholar 

  18. Wang, H., Li, J., Yu, Q., Hong, T., Yan, J., Zhao, W.: Integrating recurrent neural networks and reinforcement learning for dynamic service composition. Fut. Gener. Comput. Syst. 107, 551–563 (2020)

    Article  Google Scholar 

  19. Khanouche, M.E., Gadouche, H., Farah, Z., Tari, A.: Flexible qos-aware services composition for service computing environments. Comput. Netw. 166, 106982 (2020)

    Article  Google Scholar 

  20. Palade, A., Clarke, S.: Collaborative agent communities for resilient service composition in mobile environments. IEEE Trans. Services Comput., 1–14 (Jan. 2020). Early Access, https://doi.org/10.1109/TSC.2020.2964753

  21. Zeng, L., Benatallah, B., Ngu, A.H., Dumas, M., Kalagnanam, J., Chang, H.: Qos-aware middleware for web services composition. IEEE Trans. Software Eng. 30(5), 311–327 (2004)

    Article  Google Scholar 

  22. Wang, S., Guo, Y., Li, Y., Hsu, C.-H.: Cultural distance for service composition in cyber-physical-social systems. Future Gener. Comput. Syst. 108, 1049–1057 (2020)

    Article  Google Scholar 

  23. Chen, Y., Huang, J., Lin, C., Shen, X.: Multi-objective service composition with qos dependencies. IEEE Transact. Cloud Comput. 7(2), 537–552 (2019)

    Article  Google Scholar 

  24. Ding, Z., Liu, J., Sun, Y., Jiang, C., Zhou, M.: A transaction and qos-aware service selection approach based on genetic algorithm. IEEE Trans. Syst., Man, and Cybern.: Syst. 45(7), 1035–1046 (2015)

    Article  Google Scholar 

  25. Zo, H., Nazareth, D.L., Jain, H.K.: Service-oriented application composition with evolutionary heuristics and multiple criteria. ACM Trans. Manag. Inf. Syst. 10(3), 1–28 (2019)

    Article  Google Scholar 

  26. Xu, X., Sheng, Q.Z., Wang, Z., Yao, L., et al.: Novel artificial bee colony algorithms for qos-aware service selection. IEEE Trans. Services Comput. 12(2), 247–261 (2019)

    Article  Google Scholar 

  27. Jatoth, C., Gangadharan, G., Buyya, R.: Optimal fitness aware cloud service composition using an adaptive genotypes evolution based genetic algorithm. Future Gener. Comput. Syst. 94, 185–198 (2019)

    Article  Google Scholar 

  28. Dahan, F., Binsaeedan, W., Altaf, M., Al-Asaly, M.S., Hassan, M.M.: An efficient hybrid metaheuristic algorithm for qos-aware cloud service composition problem. IEEE Access 9, 95208–95217 (2021)

    Article  Google Scholar 

  29. Jin, H., Lv, S., Yang, Z., Liu, Y.: Eagle strategy using uniform mutation and modified whale optimization algorithm for qos-aware cloud service composition. Appl. Soft Comput. 114, 108053 (2022)

    Article  Google Scholar 

  30. Sun, S.X., Zhao, J.: A decomposition-based approach for service composition with global qos guarantees. Inf. Sci. 199, 138–153 (2012)

    Article  Google Scholar 

  31. Deng, S., Huang, L., Taheri, J., Yin, J., Zhou, M., Zomaya, A.Y.: Mobility-aware service composition in mobile communities. IEEE Trans. Syst., Man, and Cybern.: Syst. 47(3), 555–568 (2017)

    Article  Google Scholar 

  32. Seghir, F.: A genetic algorithm with an elitism replacement method for solving the nonfunctional web service composition under fuzzy qos parameters. In: 2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS), pp. 1–7 (2021). IEEE

  33. Boucetti, R., Hemam, S.M., Hioual, O.: An approach based on genetic algorithms and neural networks for qos-aware iot services composition. J. King Saud Univ.-Comput. Inform. Sci. 34(8), 5619–5632 (2022)

    Google Scholar 

  34. Zhao, D., Zhou, Z., Ning, K., Duan, Y., Zhang, L.-J.: An energy-aware service composition mechanismss in service-oriented wireless sensor networks. In: 2017 IEEE Int. Conf. on Internet of Things (ICIOT), Honolulu, USA, pp. 89–96 (2017). IEEE

  35. Sun, M., Zhou, Z., Wang, J., Du, C., Gaaloul, W.: Energy-efficient iot service composition for concurrent timed applications. Future Gener. Comput. Syst. 100, 1017–1030 (2019)

    Article  Google Scholar 

  36. Deng, S., Wu, H., Tan, W., Xiang, Z., Wu, Z.: Mobile service selection for composition: an energy consumption perspective. IEEE Trans. Autom. Sci. Eng. 14(3), 1478–1490 (2017)

    Article  Google Scholar 

  37. Chen, N., Cardozo, N., Clarke, S.: Goal-driven service composition in mobile and pervasive computing. IEEE Trans. Services Comput. 11(1), 49–62 (2018)

    Article  Google Scholar 

  38. Ngoko, Y., Goldman, A., Milojicic, D.: Service selection in web service compositions optimizing energy consumption and service response time. J. Internet Services Appl. 4(1), 19 (2013)

    Article  Google Scholar 

  39. Ibrahim, G.J., Rashid, T.A., Akinsolu, M.O.: An energy efficient service composition mechanism using a hybrid meta-heuristic algorithm in a mobile cloud environment. J. Parallel Distributed Comput. 143, 77–87 (2020)

    Article  Google Scholar 

  40. Sefati, S., Navimipour, N.J.: A qos-aware service composition mechanism in the internet of things using a hidden-markov-model-based optimization algorithm. IEEE Internet Things J. 8(20), 15620–15627 (2021)

    Article  Google Scholar 

  41. Zhang, Y., Jin, Z.: Group teaching optimization algorithm: A novel metaheuristic method for solving global optimization problems. Expert Syst. Appl. 148, 113246 (2020)

    Article  Google Scholar 

  42. Serrai, W., Abdelli, A., Mokdad, L., Hammal, Y.: Towards an efficient and a more accurate web service selection using mcdm methods. J. Comput. Sci. 22, 253–267 (2017)

    Article  Google Scholar 

  43. Dimolitsas, I., Dechouniotis, D., Papavassiliou, S., Papadimitriou, P., Theodorou, V.: Edge cloud selection: The essential step for network service marketplaces. IEEE Commun. Mag. 59(10), 28–33 (2021)

    Article  Google Scholar 

  44. Shahzaad, B., Bouguettaya, A., Mistry, S., Neiat, A.G.: Resilient composition of drone services for delivery. Futur. Gener. Comput. Syst. 115, 335–350 (2021)

    Article  Google Scholar 

  45. Li, J., Ren, H., Li, C., Chen, H.: A novel and efficient salp swarm algorithm for large-scale qos-aware service composition selection. Computing 104(9), 2031–2051 (2022)

    Article  Google Scholar 

  46. Cherifi, A., Khanouche, M.E., Amirat, Y., Farah, Z.: A parallel approach for user-centered qos-aware services composition in the internet of things. Eng. Appl. Artif. Intell. 123, 106277 (2023)

    Article  Google Scholar 

  47. Seghir, F., Khababa, G.: An improved discrete flower pollination algorithm for fuzzy qos-aware iot services composition based on skyline operator. J. Supercomput. 79(10), 10645–10676 (2023)

    Article  Google Scholar 

  48. Al-Masri, E., Mahmoud, Q.H.: Investigating web services on the world wide web. In: Proceedings of the 17th Int. Conf. on World Wide Web, New York, USA, pp. 795–804 (2008). ACM

  49. Li, J., Zhu, S.: Service composition considering energy consumption of users and transferring files in a multicloud environment. J. Cloud Comput. 12(1), 1–12 (2023)

    Article  Google Scholar 

  50. Yu, T., Zhang, Y., Lin, K.-J.: Efficient algorithms for web services selection with end-to-end qos constraints. ACM Transact. Web (TWEB) 1(1), 6 (2007)

    Article  Google Scholar 

  51. Fishburn, P.C.: Exceptional paper-lexicographic orders, utilities and decision rules: A survey. Manage. Sci. 20(11), 1442–1471 (1974)

    Article  Google Scholar 

  52. Furthmüller, J., Waldhorst, O.P.: Energy-aware resource sharing with mobile devices. Comput. Netw. 56(7), 1920–1934 (2012)

    Article  Google Scholar 

  53. Sun, M., Zhou, Z., Zhang, W., Hung, P.C.: Iot service composition for concurrent timed applications. In: 2019 IEEE International Conference on Web Services (ICWS), pp. 50–54 (2019). IEEE

  54. Khanam, R., Kumar, R.R., Kumar, C.: Qos based cloud service composition with optimal set of services using pso. In: 2018 4th International Conference on Recent Advances in Information Technology (RAIT), pp. 1–6 (2018). IEEE

  55. Deng, S., Huang, L., Hu, D., Zhao, J.L., Wu, Z.: Mobility-enabled service selection for composite services. IEEE Trans. Serv. Comput. 9(3), 394–407 (2016)

    Article  Google Scholar 

  56. Geebelen, D., Geebelen, K., Truyen, E., Michiels, S., Suykens, J.A., Vandewalle, J., Joosen, W.: Qos prediction for web service compositions using kernel-based quantile estimation with online adaptation of the constant offset. Inf. Sci. 268, 397–424 (2014)

    Article  Google Scholar 

  57. Cho, J.-H., Ko, H.-G., Ko, I.-Y.: Adaptive service selection according to the service density in multiple qos aspects. IEEE Trans. Services Comput. 9(6), 883–894 (2015)

    Article  Google Scholar 

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Authors and Affiliations

  1. Laboratoire d’Informatique Médicale (LIMED), Faculté des Sciences Exactes, Université de Bejaia, 06000, Bejaia, Algeria

    Salma Hameche & Abdelkamel Tari

  2. Université Claude Bernard Lyon 1, Laboratoire d’InfoRmatique en Image et Systèmes d’information (LIRIS) UMR 5205 CNRS, 69100, Villeurbanne, France

    Mohamed Essaid Khanouche

  3. Univ Paris Est Creteil, LISSI, F-94400, Vitry, France

    Abdelghani Chibani

  4. Laboratoire LITAN, École supérieure en Sciences et Technologies de l’Informatique et du Numérique, RN 75, 06300, Amizour, Bejaia, Algeria

    Mohamed Essaid Khanouche & Abdelkamel Tari

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  1. Salma Hameche
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  2. Mohamed Essaid Khanouche
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  3. Abdelghani Chibani
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  4. Abdelkamel Tari
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All authors contributed to the research design and performance evaluation. The first draft of the manuscript was written by SH and MEK. All authors commented on previous versions of the paper and approved the final manuscript.

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Correspondence to Mohamed Essaid Khanouche.

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Hameche, S., Khanouche, M.E., Chibani, A. et al. A Group Teaching Optimization-Based Approach for Energy and QoS-Aware Internet of Things Services Composition. J Netw Syst Manage 32, 4 (2024). https://doi.org/10.1007/s10922-023-09779-4

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