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Fair and energy-aware IoT service composition under QoS constraints

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Abstract

The Internet of Things (IoT) concept is expected to be a crucial component of human life in the near future. In IoT, devices provide functionalities as services. By integrating primitive services of interconnected IoT devices, complex IoT applications are composed. In a large-scale IoT environment, the selection of IoT services is essential. The services must be selected considering Quality-of-Service (QoS), energy consumption, and fairness among IoT services. This paper proposes a multi-objective optimization-based IoT service composition framework for fog-based IoT networks. The proposed solution takes advantage of the NSGA-II (Non-dominated Sorting Genetic Algorithm II). The cloud controller distributes application requests to fog servers as application requests arrive. Fog servers decompose application requests into IoT service requests and then slice IoT service requests into time windows. Each time window is optimized individually using the proposed model considering QoS, energy consumption, and fairness. Experimental evaluation results show that the proposed approach can optimize energy consumption and fairness without causing any QoS degradation.

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References

  1. Whitmore A, Agarwal A, Da Xu L (2015) The internet of things-a survey of topics and trends. Inf Syst Front 17(2):261–274

    Article  Google Scholar 

  2. Nord JH, Koohang A, Paliszkiewicz J (2019) The internet of things: review and theoretical framework. Expert Syst Appl 133:97–108

    Article  Google Scholar 

  3. Meo M (2020) Guest editorial: special issue on energy efficiency for internet of things. IEEE Trans Green Commun Netw 4(4):944–945. https://doi.org/10.1109/TGCN.2020.3035220

    Article  Google Scholar 

  4. Alsaryrah O, Mashal I, Chung TY (2018) Energy-aware services composition for internet of things. In: Proceedings of IEEE 4th World Forum on Internet of Things (WF-IoT), pp. 604–608. https://doi.org/10.1109/WF-IoT.2018.8355213

  5. Arellanes D, Lau KK (2020) Evaluating iot service composition mechanisms for the scalability of iot systems. Future Gener Comput Syst 108:827–848. https://doi.org/10.1016/j.future.2020.02.073

    Article  Google Scholar 

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

    Article  Google Scholar 

  7. Xiao R, Wu Z, Wang D (2019) A finite-state-machine model driven service composition architecture for internet of things rapid prototyping. Future Gener Comput Syst 99:473–488. https://doi.org/10.1016/j.future.2019.04.050

    Article  Google Scholar 

  8. Urbieta A, González-Beltrán A, Mokhtar SB, Hossain MA, Capra L (2017) Adaptive and context-aware service composition for iot-based smart cities. Future Gener Comput Syst 76:262–274. https://doi.org/10.1016/j.future.2016.12.038

    Article  Google Scholar 

  9. Safaei A, Nassiri R, Rahmani AM (2021) Enterprise service composition models in iot context: solutions comparison. J Supercomput 78:2015–2042

    Article  Google Scholar 

  10. Khanouche ME, Amirat Y, Chibani A, Kerkar M, Yachir A (2016) Energy-centered and qos-aware services selection for internet of things. IEEE Trans Autom Sci Eng 13(3):1256–1269. https://doi.org/10.1109/TASE.2016.2539240

    Article  Google Scholar 

  11. Haghi Kashani M, Rahmani AM, Jafari Navimipour N (2020) Quality of service-aware approaches in fog computing. Int J Commun Syst 33(8):e4340

    Article  Google Scholar 

  12. Khanouche ME, Mouloudj S, Hammoum M (2019) Two-steps qos-aware services composition algorithm for internet of things. In: ICFNDS ’19 Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3341325.3342017

  13. Adel Serhani M et al (2020) Self-adapting cloud services orchestration for fulfilling intensive sensory data-driven iot workflows. Future Gener Comput Syst 108:583–597. https://doi.org/10.1016/j.future.2020.02.066

    Article  Google Scholar 

  14. Arshad R, Zahoor S, Shah MA, Wahid A, Yu H (2017) Green iot: an investigation on energy saving practices for 2020 and beyond. IEEE Access 5:15667–15681. https://doi.org/10.1109/ACCESS.2017.2686092

    Article  Google Scholar 

  15. Shaikh FK, Zeadally S, Exposito E (2017) Enabling technologies for green internet of things. IEEE Syst J 11(2):983–994. https://doi.org/10.1109/JSYST.2015.2415194

    Article  Google Scholar 

  16. Li S, Huang J, Cheng B, Cui L, Shi Y (2019) Fass: a fairness-aware approach for concurrent service selection with constraints. In: Proceedings of the IEEE International Conference on Web Services (ICWS), pp. 255–259. https://doi.org/10.1109/ICWS.2019.00051

  17. Godinho N, Curado M, Paquete L (2019) Optimization of service placement with fairness. In: Proceedings of the IEEE Symposium on Computers and Communications (ISCC), pp. 1–6. https://doi.org/10.1109/ISCC47284.2019.8969652

  18. Shi H, Prasad RV, Onur E, Niemegeers IGMM (2014) Fairness in wireless networks: issues, measures and challenges. IEEE Commun Surv Tutor 16(1):5–24. https://doi.org/10.1109/SURV.2013.050113.00015

    Article  Google Scholar 

  19. Yu R, Kilari VT, Xue G, Yang D (2019) Load balancing for interdependent iot microservices. In: Proceedings of the IEEE INFOCOM 2019 - IEEE Conference on Computer Communications, pp. 298–306. https://doi.org/10.1109/INFOCOM.2019.8737450

  20. Mouradian C et al (2017) A comprehensive survey on fog computing: state-of-the-art and research challenges. IEEE Commun Surv Tutor 20(1):416–464

    Article  Google Scholar 

  21. Naha RK et al (2018) Fog computing: survey of trends, architectures, requirements, and research directions. IEEE Access 6:47980–48009

    Article  Google Scholar 

  22. Hu P, Dhelim S, Ning H, Qiu T (2017) Survey on fog computing: architecture, key technologies, applications and open issues. J Netw Comput Appl 98:27–42

    Article  Google Scholar 

  23. Kumari A, Tanwar S, Tyagi S, Kumar N (2018) Fog computing for healthcare 4.0 environment: opportunities and challenges. Comput Electr Eng 72:1–13

    Article  Google Scholar 

  24. Salman O, Elhajj I, Chehab A, Kayssi A (2018) Iot survey: an sdn and fog computing perspective. Comput Netw 143:221–246

    Article  Google Scholar 

  25. Serdaroglu KC, Baydere S (2021) An efficient multipriority data packet traffic scheduling approach for fog of things. IEEE Internet Things J 9(1):525–534

    Article  Google Scholar 

  26. Bellavista P et al (2019) A survey on fog computing for the internet of things. Pervasive Mob Comput 52:71–99

    Article  Google Scholar 

  27. Sheikh Sofla M, Haghi Kashani M, Mahdipour E, Faghih Mirzaee R (2021) Towards effective offloading mechanisms in fog computing. Multimed Tools Appl 81:1997–2042

    Article  Google Scholar 

  28. Akasiadis C, Tzortzis G, Spyrou E, Spyropoulos C (2015) Developing complex services in an iot ecosystem. In: Proceedings of the IEEE 2nd World Forum on Internet of Things (WF-IoT), pp. 52–56. https://doi.org/10.1109/WF-IoT.2015.7389026

  29. Yachir A, Amirat Y, Chibani A, Badache N (2016) Event-aware framework for dynamic services discovery and selection in the context of ambient intelligence and internet of things. IEEE Trans Autom Sci Eng 13(1):85–102

    Article  Google Scholar 

  30. Adewuyi AA et al (2021) Sc-trust: a dynamic model for trustworthy service composition in the internet of things. IEEE Internet of Things J 9(5):3298–3312

    Article  Google Scholar 

  31. Adewuyi AA et al (2019) Ctrust: a dynamic trust model for collaborative applications in the internet of things. IEEE Internet Things J 6(3):5432–5445

    Article  Google Scholar 

  32. Chai Z-Y, Du M-M, Song G-Z (2021) A fast energy-centered and qos-aware service composition approach for internet of things. Appl Soft Comput 100:106914

    Article  Google Scholar 

  33. Alsaryrah O, Mashal I, Chung T (2018) Bi-objective optimization for energy aware internet of things service composition. IEEE Access 6:26809–26819. https://doi.org/10.1109/ACCESS.2018.2836334

    Article  Google Scholar 

  34. Alsaryrah O, Mashal I, Chung TY (2019) A fast iot service composition scheme for energy efficient qos services. In: Proceedings of the 2019 7th International Conference on Computer and Communications Management, pp. 231–237. https://doi.org/10.1145/3348445.3348469

  35. Kurdi H, Ezzat F, Altoaimy L, Ahmed SH, Youcef-Toumi K (2018) Multicuckoo: multi-cloud service composition using a cuckoo-inspired algorithm for the internet of things applications. IEEE Access 6:56737–56749. https://doi.org/10.1109/ACCESS.2018.2872744

    Article  Google Scholar 

  36. Sun M, Zhou Z, Duan Y (2018) Energy-aware service composition of configurable iot smart things. In: Proceedings of the IEEE 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN), pp. 37–42. https://doi.org/10.1109/MSN.2018.00013

  37. Sun M, Zhou Z, Wang J, Du C, Gaaloul W (2019) Energy-efficient iot service composition for concurrent timed applications. Future Gener Comput Syst 100:1017–1030. https://doi.org/10.1016/j.future.2019.05.070

    Article  Google Scholar 

  38. Zhou Z, Zhao D, Liu L, Hung PC (2018) Energy-aware composition for wireless sensor networks as a service. Future Gener Comput Syst 80:299–310. https://doi.org/10.1016/j.future.2017.02.050

    Article  Google Scholar 

  39. Badawy MM, Ali ZH, Ali HA (2019) Qos provisioning framework for service-oriented internet of things (iot). Clust Comput 23:575–591

    Article  Google Scholar 

  40. Li T, He T, Wang Z, Zhang Y (2018) An approach to iot service optimal composition for mass customization on cloud manufacturing. IEEE Access 6:50572–50586

    Article  Google Scholar 

  41. Esmaeilyfard R, Naderi M (2021) Distributed composition of complex event services in iot network. J Supercomput 77(6):6123–6144

    Article  Google Scholar 

  42. Razian M, Fathian M, Wu H, Akbari A, Buyya R (2020) Saiot: scalable anomaly-aware services composition in cloudiot environments. IEEE Internet Things J 8(5):3665–3677

    Article  Google Scholar 

  43. Sefati S, Navimipour NJ (2021) 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

    Article  Google Scholar 

  44. Souri A, Ghobaei-Arani M (2021) Cloud manufacturing service composition in iot applications: a formal verification-based approach. Multimed Tools Appl. https://doi.org/10.1007/s11042-021-10645-1

    Article  Google Scholar 

  45. Gharineiat A, Bouguettaya A, Ba-hutair MN (2021) A deep reinforcement learning approach for composing moving iot services. IEEE Trans Serv Comput. https://doi.org/10.1109/TSC.2021.3064329

    Article  Google Scholar 

  46. Ghari Neiat A, Bouguettaya A, Sellis T (2015) Spatio-temporal composition of crowdsourced services. In: Barros A., Grigori D., Narendra N., Dam H. (eds) Service-Oriented Computing. ICSOC 2015. Lecture Notes in Computer Science, vol 9435. Springer, Berlin, Heidelberg, pp. 373–382. https://doi.org/10.1007/978-3-662-48616-0_26

    Chapter  Google Scholar 

  47. Neiat AG, Bouguettaya A, Sellis T, Mistry S (2017) Crowdsourced coverage as a service: two-level composition of sensor cloud services. IEEE Trans Knowl Data Eng 29(7):1384–1397

    Article  Google Scholar 

  48. Tärneberg W (2019) The confluence of Cloud computing, 5G, and IoT in the Fog. Dissertation, Lund University

  49. Bonomi F, Milito R, Zhu J, Addepalli S (2012) Fog computing and its role in the internet of things. In: MCC '12: Proceedings of the first edition of the MCC workshop on Mobile cloud computing, pp. 13–16. https://doi.org/10.1145/2342509.2342513

  50. Okay FY, Ozdemir S (2018) Routing in fog-enabled iot platforms: a survey and an sdn-based solution. IEEE Internet Things J 5(6):4871–4889

    Article  Google Scholar 

  51. Mukherjee M, Shu L, Wang D (2018) Survey of fog computing: fundamental, network applications, and research challenges. IEEE Commun Surv Tutor 20(3):1826–1857. https://doi.org/10.1109/COMST.2018.2814571

    Article  Google Scholar 

  52. Tange K, De Donno M, Fafoutis X, Dragoni N (2020) A systematic survey of industrial internet of things security: requirements and fog computing opportunities. IEEE Commun Surv Tutor 22(4):2489–2520. https://doi.org/10.1109/COMST.2020.3011208

    Article  Google Scholar 

  53. Hernández-Nieves E, Hernández G, Gil-González A-B, Rodríguez-González S, Corchado JM (2020) Fog computing architecture for personalized recommendation of banking products. Expert Syst Appl 140:112900

    Article  Google Scholar 

  54. Masdari M, Nozad Bonab M, Ozdemir S (2021) Qos-driven metaheuristic service composition schemes: a comprehensive overview. Artif Intell Rev 54(5):3749–3816

    Article  Google Scholar 

  55. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evol Comput 6(2):182–197. https://doi.org/10.1109/4235.996017

    Article  Google Scholar 

  56. Campos Ciro G, Dugardin F, Yalaoui F, Kelly R (2016) A nsga-ii and nsga-iii comparison for solving an open shop scheduling problem with resource constraints. IFAC-PapersOnLine 49(12):1272–1277. https://doi.org/10.1016/j.ifacol.2016.07.690

    Article  Google Scholar 

  57. Zhang X, Tian Y, Cheng R, Jin Y (2015) An efficient approach to nondominated sorting for evolutionary multiobjective optimization. IEEE Trans Evol Comput 19(2):201–213. https://doi.org/10.1109/TEVC.2014.2308305

    Article  Google Scholar 

  58. Cui L et al (2019) Joint optimization of energy consumption and latency in mobile edge computing for internet of things. IEEE Internet Things J 6(3):4791–4803. https://doi.org/10.1109/JIOT.2018.2869226

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK), Grant Numbers: 118E212, 119N049.

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  1. Computer Engineering Department, Gazi University, 06570, Ankara, Turkey

    Metehan Guzel

  2. Computer Engineering Department, Hacettepe University, 06800, Ankara, Turkey

    Suat Ozdemir

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  1. Metehan Guzel
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  2. Suat Ozdemir
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Correspondence to Metehan Guzel.

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Guzel, M., Ozdemir, S. Fair and energy-aware IoT service composition under QoS constraints. J Supercomput 78, 13427–13454 (2022). https://doi.org/10.1007/s11227-022-04398-3

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