Skip to main content
SpringerLink
Log in

An efficient framework for trust evaluation of secure service selection in fog computing based on QoS, reputation, and social criteria

  • Special Issue Article
  • Published:
Computing Aims and scope Submit manuscript

Abstract

Fog computing has been recognized as a new paradigm of distributed environment which is the result of cloud computing extension. Fog layer is located between cloud data center layer and Internet of Things device layer to provide data storage, processing through executing applications on the network edges. However, security is the main challenge of fog technologies from various aspects due to processing, managing and storing private information by fog servers. Therefore, adopting a trust management system in accordance with determined criteria by trustor is the main issue of fog computing technology to address the level of trustworthiness of trustee. The efficient metrics on trustworthiness value, including Quality of Service (QoS), social relationship, and past experience such as reputation, conflict with each other which is considered as a multi criteria decision making problem. Therefore, the determination of priority and contribution level of effective metrics on trustworthiness value is an important issue that must be addressed. In this paper, a complete set of effective criteria on secure service selection in a fog computing environment is identified. Moreover, an efficient multi-criteria decision making method with corporation of fuzzy and Best Worst Method are utilized to determine the contribution level of each metric on trust level considering uncertainty of metrics. The results indicate that QoS has the great impact on secure service selection with a degree of 0.470. Furthermore, central information of security including access control, integrity and confidentiality are ranked as the most effective metrics on secure service selection with degrees of 0.109, 0.099 and 0.088. Moreover, honesty from the social relationship criteria has high impact on secure service provision with a degree of 0.074. Also, the proposed framework outperforms convergence and accuracy of trust value with 30% and 5%, respectively compared to two-way trust management framework. Besides, the proposed framework outperforms the percentage of bad fog servers selected as service providers with 25% in comparison with two-way trust management systems.

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

Similar content being viewed by others

References

  1. Yousefpour A, Fung C, Nguyen T, Kadiyala K, Jalali F, Niakanlahiji A, Kong J, Jue JP (2019) All one needs to know about fog computing and related edge computing paradigms: a complete survey. J Syst Architect 98:289–330

    Article  Google Scholar 

  2. Kouicem DE, Bouabdallah A, Lakhlef H (2018) Internet of things security: a top-down survey. Comput Netw 141:199–221

    Article  Google Scholar 

  3. Alaba FA, Othman M, Hashem IAT, Alotaibi F (2017) Internet of things security: a survey. J Netw Comput Appl 88:10–28

    Article  Google Scholar 

  4. Yi S, Qin Z, Li Q (2015) Security and privacy issues of fog computing: a survey. In: 2015 International conference on wireless algorithms, systems, and applications. Springer, pp 685–695

  5. Mukherjee M, Matam R, Shu L, Maglaras L, Ferrag MA, Choudhury N, Kumar V (2017) Security and privacy in fog computing: challenges. IEEE Access 5:19293–19304

    Article  Google Scholar 

  6. Ni J, Zhang K, Lin X, Shen X (2018) Securing fog computing for internet of things applications: challenges and solutions. IEEE Commun Surv Tutor 20(1):601–628

    Article  Google Scholar 

  7. Guan Y, Shao J, Wei G, Xie M (2018) Data security and privacy in fog computing. IEEE Netw 32(5):106–111

    Article  Google Scholar 

  8. Tariq N, Asim M, Al-Obeidat F, Zubair Farooqi M, Baker T, Hammoudeh M, Ghafir I (2019) The security of big data in fog-enabled IoT applications including blockchain: a survey. Sensors 19(8):1788

    Article  Google Scholar 

  9. Adewuyi AA, Cheng H, Shi Q, Cao J, MacDermott A, Wang X (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 

  10. Alemneh E, Senouci SM, Brunet P, Tegegne T (2020) A two-way trust management system for fog computing. Futur Gener Comput Syst 106:206–220

    Article  Google Scholar 

  11. Al-khafajiy M, Baker T, Al-Libawy H, Maamar Z, Aloqaily M, Jararweh Y (2019) Improving fog computing performance via fog-2-fog collaboration. Future Gener Comput Syst 100:266–280

    Article  Google Scholar 

  12. Junejo AK, Komninos N, Sathiyanarayanan M, Chowdhry BS (2019) Trustee: a trust management system for fog-enabled cyber physical systems. In: IEEE transactions on emerging topics in computing, p 1

  13. Wang T, Qiu L, Sangaiah AK, Xu G, Liu A (2020) Energy-efficient and trustworthy data collection protocol based on mobile fog computing in internet of things. IEEE Trans Ind Inf 16(5):3531–3539

    Article  Google Scholar 

  14. Ogundoyin SO, Kamil IA (2020) A Fuzzy-AHP based prioritization of trust criteria in fog computing services. Appl Soft Comput 97:106789

    Article  Google Scholar 

  15. Gomez Marmol F, Martinez Perez G (2012) Trip, a trust and reputation infrastructure-based proposal for vehicular ad hoc networks. J Netw Comput Appl 35(3):934–941 (special Issue on Trusted Computing and Communications)

    Article  Google Scholar 

  16. Soleymani SA, Abdullah AH, Zareei M, Anisi MH, Vargas-Rosales C, Khurram Khan M, Goudarzi S (2017) A secure trust model based on fuzzy logic in vehicular ad hoc networks with fog computing. IEEE Access 5:15619–15629

    Article  Google Scholar 

  17. Chen D, Chang G, Sun D, Li J, Jia J, Wang X (2011) TRM-IoT: a trust management model based on fuzzy reputation for internet of things. Comput Sci Inf Syst 8(4):1207–1228

    Article  Google Scholar 

  18. Bao F, Chen I (2012) Trust management for the internet of things and its application to service composition. In: 2012 IEEE international symposium on a world of wireless, mobile and multimedia networks (WoWMoM), pp 1–6

  19. Bao F, Chen I, Guo J (2013) Scalable, adaptive and survivable trust management for community of interest based internet of things systems. In: 2013 IEEE eleventh international symposium on autonomous decentralized systems (ISADS), pp 1–7

  20. Chen I, Guo J, Bao F (2014) Trust management for service composition in SOA-based IoT systems. In: IEEE wireless communications and networking conference (WCNC), pp 3444–3449

  21. Yan Z, Zhang P, Vasilakos AV (2014) A survey on trust management for internet of things. J Netw Comput Appl 42:120–134

    Article  Google Scholar 

  22. Jayasinghe U, Truong NB, Lee GM, Um T (2016) RPR: a trust computation model for social internet of things. In: 2016 International IEEE conferences on ubiquitous intelligence computing, advanced and trusted computing, scalable computing and communications, cloud and big data computing, internet of people, and smart world congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), pp 930–937

  23. Guo J, Chen IR, Tsai JJ (2017) A survey of trust computation models for service management in internet of things systems. Comput Commun 97:1–14

    Article  Google Scholar 

  24. Javaid S, Afzal H, Arif F, Iltaf N, Abbas H, Iqbal W (2019) CATSWoTS: context aware trustworthy social web of things system. Sensors 19(14):3076

    Article  Google Scholar 

  25. Huang J, Nicol D (2013) Trust mechanisms for cloud computing. J Cloud Comput 2:1–14

    Article  Google Scholar 

  26. Manuel P (2013) A trust model of cloud computing based on quality of service. Ann Oper Res 233:1–12

    Google Scholar 

  27. Hajizadeh R, Navimipour N (2017) A method for trust evaluation in the cloud environments using a behavior graph and services grouping. Kybernetes 46:00–00

    Article  Google Scholar 

  28. Chen I, Guo J, Wang D, Tsai JJP, Al-Hamadi H, You I (2019) Trust-based service management for mobile cloud IoT systems. IEEE Trans Netw Serv Manage 16:246–263

    Article  Google Scholar 

  29. Alrawais A, Alhothaily A, Hu C, Cheng X (2017) Fog computing for the internet of things: security and privacy issues. IEEE Internet Comput 21(2):34–42

    Article  Google Scholar 

  30. Roman R, Lopez J, Mambo M (2018) Mobile edge computing, fog et al.: a survey and analysis of security threats and challenges. Future Gener Comput Syst 78:680–698

    Article  Google Scholar 

  31. Rahman FH, Au TW, Newaz SS, Suhaili WS, Lee GM (2020) Find my trustworthy fogs: a fuzzy-based trust evaluation framework. Future Gener Comput Syst 109:562–572

    Article  Google Scholar 

  32. Wang Q, Chen D, Zhang N, Ding Z, Qin Z (2017) PCP: a privacy-preserving content-based publish-subscribe scheme with differential privacy in fog computing. IEEE Access 5:17962–17974

    Article  Google Scholar 

  33. Abdel-Basset M, Manogaran G, Mohamed M (2019) A neutrosophic theory based security approach for fog and mobile-edge computing. Comput Netw 157:122–132

    Article  Google Scholar 

  34. Rezaei J (2015) Best-worst multi-criteria decision-making method. Omega 53:49–57

    Article  Google Scholar 

  35. Rezaei J, Nispeling T, Sarkis J, Tavasszy L (2016) A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method. J Clean Prod 135:577–588

    Article  Google Scholar 

  36. Hussain A, Chun J, Khan M (2020) A novel customer-centric methodology for optimal service selection (MOSS) in a cloud environment. Future Gener Comput Syst 105:562–580

    Article  Google Scholar 

  37. Tran VX, Tsuji H, Masuda R (2009) A new QoS ontology and its QoS-based ranking algorithm for web services. Simul Model Practi Theory 17(8):1378–1398 (Dependable service-orientated computing systems)

    Article  Google Scholar 

  38. Princy Bathla SV (2014) Sla aware cost based service ranking in cloud computing. Int J Appl Innov Eng Manag (IJAIEM) 3(7):257–268

    Google Scholar 

  39. Karamoozian A, Hafid A, Boushaba M, Afzali M (2016) QoS-aware resource allocation for mobile media services in cloud environment. In: 2016 13th IEEE annual consumer communications networking conference (CCNC), pp 732–737

  40. Buyya G, Bukozkan R, Goccer F, Feyziouglu O (2018) Cloud computing technology selection based on interval-valued intuitionistic fuzzy MCDM methods. Soft Comput 22(15):5091–5114

    Article  Google Scholar 

  41. Li LGYMC, Hang J (2017) Using an integrated group decisionmethod based on SVM, TFN-RS-AHP, and TOPSIS-CD for cloud service supplier selection. Math Probl Eng, 1–14

  42. Zanni A, Forsstrom S, Jennehag U, Bellavista P (2018) Elastic provisioning of internet of things services using fog computing: An experience report. In: 2018 6th IEEE international conference on mobile cloud computing, services, and engineering (MobileCloud), pp 17–22

  43. Kumar RR, Mishra S, Kumar C (2017) Prioritizing the solution of cloud service selection using integrated MCDM methods under fuzzy environment. J Supercomput 73(11):4652–4682

    Article  Google Scholar 

  44. Lee S, Seo KK (2016) A hybrid multi-criteria decision-making model for a cloud service selection problem using BSC, fuzzy Delphi method and fuzzy AHP. Wirel Pers Commun 86(1):57–75

    Article  Google Scholar 

  45. Rady M, Abdelkader T, Ismail R (2019) Integrity and confidentiality in cloud outsourced data. Ain Shams Eng J 10(2):275–285

    Article  Google Scholar 

  46. Sun L, Dong H, Hussain FK, Hussain OK, Ma J, Zhang Y (2014) A hybrid fuzzy framework for cloud service selection. In: 2014 IEEE international conference on web services, pp 313–320

  47. Mansouri W, Ali KB, Zarai F, Obaidat M (2015) Radio resource management for heterogeneous wireless networks: schemes and simulation analysis. In: Modeling and simulation of computer networks and systems: methodologies and applications, pp 767–792

  48. Garg SK, Versteeg S, Buyya R (2013) A framework for ranking of cloud computing services. Future Gener Comput Syst 29(4):1012–1023

    Article  Google Scholar 

  49. Mehak F, Masood R, Ghazi Y, Shibli A, Khan S (2014) Security aspects of database-as-a-service (DBaaS) in cloud computing. Springer, Berlin, pp 297–324

    Google Scholar 

  50. Mahmud M, Buyya R (2016) Fog computing: a taxonomy, survey and future directions

  51. Deng R, Lu R, Lai C, Luan TH, Liang H (2016) Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE Internet Things J 3(6):1171–1181

    Google Scholar 

  52. Do CT, Tran NH, Chuan Pham, Alam MGR, Jae Hyeok Son, Hong CS (2015) A proximal algorithm for joint resource allocation and minimizing carbon footprint in geo-distributed fog computing. In: 2015 international conference on information networking (ICOIN), pp 324–329

  53. Al Faruque MA, Vatanparvar K (2016) Energy management as a service over fog computing platform. IEEE Internet Things J 3(2):161–169

    Article  Google Scholar 

  54. Atzori L, Iera A, Morabito G (2011) Siot: giving a social structure to the internet of things. IEEE Commun Lett 15(11):1193–1195

    Article  Google Scholar 

  55. Bao F, Chen I (2012) Dynamic trust management for internet of things applications. pp 1–6

  56. Bao F, Chen I, Chang M, Cho J (2011) Trust-based intrusion detection in wireless sensor networks. In: 2011 IEEE international conference on communications (ICC), pp 1–6

  57. Chen I, Guo J (2014) Dynamic hierarchical trust management of mobile groups and its application to misbehaving node detection. In: 2014 IEEE 28th international conference on advanced information networking and applications, pp 49–56

  58. Zadeh L (1975) The concept of a linguistic variable and its application to approximate reasoning. Inf Sci 8(3):199–249

    Article  MathSciNet  MATH  Google Scholar 

  59. Guo S, Zhao H (2017) Fuzzy best-worst multi-criteria decision-making method and its applications. Knowl Based Syst 121:23–31

    Article  Google Scholar 

  60. Chen S, Hsieh C (2000) Representation, ranking, distance, and similarity of LR type fuzzy number and application. Aust J Intell Process Syst 6:217–229

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Graduate University of Advanced Technology, Kerman, Iran

    Mahboubeh Afzali

  2. Department of ISOM, California State University, Dominguez Hills, Carson, CA, USA

    Hamid Pourmohammadi

  3. Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran

    Amin Mohammad Vali Samani

Authors
  1. Mahboubeh Afzali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamid Pourmohammadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amin Mohammad Vali Samani
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Afzali, M., Pourmohammadi, H. & Mohammad Vali Samani, A. An efficient framework for trust evaluation of secure service selection in fog computing based on QoS, reputation, and social criteria. Computing 104, 1643–1675 (2022). https://doi.org/10.1007/s00607-022-01053-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-022-01053-w

Keywords

Mathematics Subject Classification

Search

Navigation