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Enterprise service composition models in IoT context: solutions comparison

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Abstract

Business processes decomposition and also smart awareness are the two major emerging trends regarding future business values. Effecting factors on the complexity of decision models in Internet of Things (IoT) ecosystems such as nonlinearity of objective functions, discretization of problem solving space, problem size, and also limited resources resulted many recent precise methods unable to find the optimal answer in a reasonable time. Thus, given the NP-hard (nondeterministic polynomial) of the enterprise service composition (ESC) problem, the key issue would be to find a highly improved (nearly optimal) response among the candidate enterprise services by an admirable manner. In fact, depending on the type of issue, we try to improve competency. Creating an improved model of ESC in IoT context may play an effective role in supporting and preferably meeting the needs of an organization with respect to enterprise business processes. This model in turn would allow tailoring the right selection of enterprise services to meet the needs of end users. To manage the complex needs of end users in an enterprise, enterprise services are combined into different models to satisfy end user’s requirements. Usually an atomic service can not address the entire issue of complex requirements, so atomic services have to be composed by an ESC procedure. The ESC ends up reducing time and increasing user satisfaction in addition to improve a few other Quality of Services (QoS). This comparative survey enables enterprise architecture domains (especially enterprise application architecture) to model improved ESC in IoT context despite existing restrictions to achieve admirable value.

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Notes

  1. The Open Group Architecture Framework (TOGAF).

  2. Application architecture supported by The Open Group Architecture Framework (TOGAF) Standard is a blueprint for the individual applications to be deployed, their interactions, and their relationships to the core business processes of the organization.

References

  1. Asghari P, Rahmani AM, Javadi HHS (2018) Service composition approaches in IoT: A systematic review. J Netw Comput Appl 120:61–77

    Article  Google Scholar 

  2. Rajeswari M, Sambasivam G, Balaji N, Basha MS, Vengattaraman T, Dhavachelvan P (2014) Appraisal and analysis on various web service composition approaches based on QoS factors. J King Saud Univ Comput Inf Sci 26(1):143–152

    Google Scholar 

  3. Stelmach P (2013) Service composition scenarios in the internet of things paradigm. In: Doctoral Conference on Computing, Electrical and Industrial Systems. Springer, pp 53–60

  4. Kacprzyk J (2019) Lecture notes in networks and systems. Springer, Berlin

    Google Scholar 

  5. Li S, Xu LD, Zhao S (2015) The internet of things: a survey. Inf Syst Front 17(2):243–259

    Article  Google Scholar 

  6. Wang H, Chen X, Wu Q, Yu Q, Hu X, Zheng Z, Bouguettaya A (2017) Integrating reinforcement learning with multi-agent techniques for adaptive service composition. ACM Trans Auton Adapt Syst (TAAS) 12(2):1–42

    Google Scholar 

  7. Kashyap N, Kumari AC, Chhikara R (2019) Service composition in IoT—a review. In: International Conference on Intelligent Data Communication Technologies and Internet of Things. Springer, pp 287–291

  8. Adadi N, Berrada M, Chenouni D, Halim M (2019) AWSCPM: a framework for automation of web services composition processes. In: 7th Mediterranean Congress of Telecommunications (CMT). IEEE, pp 1–4

  9. Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15):2787–2805

    Article  Google Scholar 

  10. Jeong H-Y, Yi G, Park JH (2016) A service composition model based on user experience in Ubi-cloud comp. Telecommun Syst 61(4):897–907

    Article  Google Scholar 

  11. Arch-int N, Arch-int S, Sonsilphong S, Wanchai P (2017) Graph-based semantic web service composition for healthcare data integration. J Healthc Eng. https://doi.org/10.1155/2017/4271273

    Article  MATH  Google Scholar 

  12. Rodriguez-Mier P, Mucientes M, Lama M (2011) Automatic web service composition with a heuristic-based search algorithm. In: 2011 IEEE International Conference on Web Services. IEEE, pp 81–88.

  13. Souri A, Rahmani AM, Navimipour NJ, Rezaei R (2020) A hybrid formal verification approach for QoS-aware multi-cloud service composition. Clust Comput 23(4):2453–2470

    Article  Google Scholar 

  14. Dizdarević J, Carpio F, Jukan A, Masip-Bruin X (2019) A survey of communication protocols for internet of things and related challenges of fog and cloud computing integration. ACM Comput Surv (CSUR) 51(6):1–29

    Article  Google Scholar 

  15. Luoto A (2019) Log analysis of 360-degree video users via MQTT. In: Proceedings of the 2019 2nd International Conference on Geoinformatics and Data Analysis, pp 130–137

  16. Yoo T, Jeong B, Cho H (2010) A Petri Nets based functional validation for services composition. Expert Syst Appl 37(5):3768–3776

    Article  Google Scholar 

  17. Qi J, Xu B, Xue Y, Wang K, Sun Y (2018) Knowledge based differential evolution for cloud computing service composition. J Ambient Intell Humaniz Comput 9(3):565–574

    Article  Google Scholar 

  18. Wang L, Shen J, Luo J (2015) Bio-inspired cost-aware optimization for data-intensive service provision. Concurr Comput Pract Exp 27(18):5662–5685

    Article  Google Scholar 

  19. Li L, Jin Z, Li G, Zheng L, Wei Q (2012) Modeling and analyzing the reliability and cost of service composition in the IoT: a probabilistic approach. In: 2012 IEEE 19th International Conference on Web Services. IEEE, pp 584–591

  20. Zhang W, Chang CK, Feng T, Jiang H (2010) QoS-based dynamic web service composition with ant colony optimization. In: 2010 IEEE 34th Annual Computer Software and Applications Conference. IEEE, pp 493–502

  21. P. Świa̧tek, P. Stelmach, A. Prusiewicz, K. Juszczyszyn, (2012) Service composition in knowledge-based SOA systems. New Gener Comput 30(2–3):165–188

    Google Scholar 

  22. Wang H, Peng S, Yu Q (2019) A parallel refined probabilistic approach for QoS-aware service composition. Futur Gener Comput Syst 98:609–626

    Article  Google Scholar 

  23. Jula A, Othman Z, Sundararajan E (2013) A hybrid imperialist competitive-gravitational attraction search algorithm to optimize cloud service composition. In: 2013 IEEE Workshop on Memetic Computing (MC). IEEE, pp 37–43

  24. Jula A, Sundararajan E, Othman Z (2014) Cloud computing service composition: a systematic literature review. Expert Syst Appl 41(8):3809–3824

    Article  Google Scholar 

  25. Hosseinzadeh M, Tho QT, Ali S, Rahmani AM, Souri A, Norouzi M, Huynh B (2020) A hybrid service selection and composition model for cloud-edge computing in the internet of things. IEEE Access 8:85939–85949

    Article  Google Scholar 

  26. Kashyap N, Kumari AC (2018) Hyper-heuristic approach for service composition in internet of things. Electron Gov Int J 14(4):321–339

    Google Scholar 

  27. Awad S, Malki A, Malki M, Barhamgi M, Benslimane D (2019) Composing WoT services with uncertain data. Futur Gener Comput Syst 101:940–950

    Article  Google Scholar 

  28. Han SN, Khan I, Lee GM, Crespi N, Glitho RH (2016) Service composition for IP smart object using realtime web protocols: concept and research challenges. Comput Stand Interfaces 43:79–90

    Article  Google Scholar 

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

    Article  Google Scholar 

  30. Razian M, Fathian M, Buyya R (2020) ARC: anomaly-aware robust cloud-integrated IoT service composition based on uncertainty in advertised quality of service values. J Syst Softw 164:110557

    Article  Google Scholar 

  31. Yaghoubi M, Maroosi A (2020) Simulation and modeling of an improved multi-verse optimization algorithm for QoS-aware web service composition with service level agreements in the cloud environments. Simul Model Pract Theory 103:102090

    Article  Google Scholar 

  32. García-Magariño I, Gray G, Muttukrishnan R, Asif W (2019) Agent-based IoT coordination for smart cities considering security and privacy. In: 2019 Sixth International Conference on Internet of Things: systems, management and security (IOTSMS). IEEE, pp 221–226

  33. AsirTRG, Manohar HL, Anandaraj W, Sivaranjani KN (2016) IoT as a service. In: International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), pp 1093–1096

  34. Hamzei M, Navimipour NJ (2018) Toward efficient service composition techniques in the internet of things. IEEE Internet Things J 5(5):3774–3787

    Article  Google Scholar 

  35. Cambronero ME, Macià H, Valero V, Orozco-Barbosa L (2018) Modeling and analysis of the 1-wire communication protocol using timed colored Petri nets. IEEE Access 6:27356–27372

    Article  Google Scholar 

  36. Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput 8(2):173–195

    Article  Google Scholar 

  37. Bouzary H, Chen FF (2019) A hybrid grey wolf optimizer algorithm with evolutionary operators for optimal QoS-aware service composition and optimal selection in cloud manufacturing. Int J Adv Manuf Technol 101(9–12):2771–2784

    Article  Google Scholar 

  38. Bendre N, Ebadi N, Prevost JJ, Najafirad P (2020) Human action performance using deep neuro-fuzzy recurrent attention model. IEEE Access 8:57749–57761

    Article  Google Scholar 

  39. Pessoa RM, Silva E, Van Sinderen M, Quartel DA, Pires LF (2008) Enterprise interoperability with SOA: a survey of service composition approaches. In: 2008 12th Enterprise Distributed Object Computing Conference Workshops. IEEE, pp 238–251

  40. Can U, Alatas B (2017) Performance comparisons of current metaheuristic algorithms on unconstrained optimization problems. Period Eng Nat Sci 5(3):328–340

    Google Scholar 

  41. Chen I, Guo J, Bao F (2014) Trust management for service composition in SOA-based IoT systems. In: 2014 IEEE Wireless Communications and Networking Conference (WCNC), Istanbul, pp 3444–3449

  42. Chen I, Guo J, Bao F (2016) Trust management for SOA-based IoT and its application to service composition. IEEE Trans Serv Comput 9(3):482–495

    Article  Google Scholar 

  43. Han SN, Khan I, Lee GM, Crespi N, Glitho RH (2016) Service composition for IP smart object using realtime web protocols: concept and research challenges. Comput Standards Interfaces 43:79–90

    Article  Google Scholar 

  44. Baker T, Asim M, Tawfik H, Aldawsari B, Buyya R (2017) An energy-aware service composition algorithm for multiple cloud-based IoT applications. J Netw Comput Appl 89:96–108

    Article  Google Scholar 

  45. Balakrishnan SM, Sangaiah AK (2017) Integrated QoUE and QoS approach for optimal service composition selection in internet of services (IoS). Multimed Tools Appl 76(21):22889–22916

    Article  Google Scholar 

  46. Khansari ME, Sharifian S, Motamedi SA (2018) Virtual sensor as a service: a new multicriteria QoS-aware cloud service composition for IoT applications. J Supercomput 74(10):5485–5512

    Article  Google Scholar 

  47. Yang Z, Jin Y, Hao K (2018) A bio-inspired self-learning coevolutionary dynamic multiobjective optimization algorithm for Internet of Things services. IEEE Trans. Evol. Comput 23:675–688

    Article  Google Scholar 

  48. Asghari P, Rahmani AM, Javadi HHS (2019) Internet of Things applications: a systematic review. Comput Netw 148:241–261

    Article  Google Scholar 

  49. Osman IH, Laporte G (1996) Metaheuristics: A bibliography. Ann Oper Res 63:513–623

    Article  Google Scholar 

  50. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  MathSciNet  Google Scholar 

  51. Glover F (1986) Future paths for integer programming and links to artificial intelligence. Comput Oper Res 13:533–549

    Article  MathSciNet  Google Scholar 

  52. Canfora G, Di Penta M (2005) An approach for QoS aware composition based on genetic algorithm. In: Proceeding of 2005 Conference on Genetic Evolutionary Computation ACM, (2005)

  53. Dorigo M (1992) Optimization, learning and natural algorithms (in italian). Ph.D. thesis, DEI, Politecnico di Milano, Italy, p 140

  54. Yu T, Lin KJ (2004) Service selection algorithms for web services with end to end QoS constraints. In: CEC 2004, Proceedings IEEE, pp 129–136

  55. Zeng L, Bentallah B, Dumas M (2003) Quality driven web service composition. In: Proceeding of 12th International

  56. Yu HQ, Reiff-Marganiec S (2009) A backwards composition context based service selection approach for service composition. In: Service Computing, IEEE, pp 419–426

  57. Alrifai M, Risse T, Dolog P (2008) A scalable approach for QoS based service selection. In: Service Oriented Computing, ICSOC. Springer (2008)

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Correspondence to Ramin Nassiri.

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Safaei, A., Nassiri, R. & Rahmani, A.M. Enterprise service composition models in IoT context: solutions comparison. J Supercomput 78, 2015–2042 (2022). https://doi.org/10.1007/s11227-021-03873-7

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