Top

The Journal of Supercomputing

Published in:

25-04-2023

TTLA: two-way trust between clients and fog servers using Bayesian learning automata

Authors: Niloofar Barati Bakhtiari, Masood Rafighi, Reza Ahsan

Published in: The Journal of Supercomputing | Issue 14/2023

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Fog computing is a promising paradigm for use as an efficient architecture for Internet-of-Things applications. This architecture’s advantages include proximity, low latency, adaptable resource capacity, and a distributed structure. A considerable amount of generated data and their requisites to real-time processes cause fog nodes to offload the number of tasks to the others, causing trust issues. In this paper, each client prefers to offload a task to a trusted server, and each server prefers to serve trusted clients. This may take some time, especially if we wish to reduce energy consumption. This paper proposes a Bayesian learning automaton-based two-way trust management strategy to address this issue. The proposed method outperforms current state-of-the-art methods regarding energy consumption, network usage, latency, response time, and trust value.

previous article Embedding channel pruning within the CNN architecture design using a bi-level evolutionary approach

next article Lightweight and authentic symmetric session key cryptosystem for client–server mobile communication

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Ogundoyin SO, Kamil IA (2021) A trust management system for fog computing services. Internet Things 14:100382. https://doi.org/10.1016/j.iot.2021.100382CrossRef

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. https://doi.org/10.1016/j.future.2019.12.045CrossRef

Tarahomi M, Izadi M, Ghobaei-Arani M (2021) An efficient power-aware VM allocation mechanism in cloud data centers: a micro genetic-based approach. Clust Comput 24:919–934CrossRef

Kang J, Yu R, Huang X, Zhang Y (2017) Privacy-preserved pseudonym scheme for fog computing supported internet of vehicles. IEEE Trans Intell Transp Syst 19(8):2627–2637. https://doi.org/10.1109/TITS.2017.2764095CrossRef

Okay FY, Ozdemir S (2016, May). A fog computing based smart grid model. In: 2016 International Symposium on Networks, Computers and Communications (ISNCC). IEEE, pp 1–6. https://doi.org/10.1109/ISNCC.2016.7746062

Rahmani AM, Gia TN, Negash B, Anzanpour A, Azimi I, Jiang M, Liljeberg P (2018) Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: a fog computing approach. Futur Gener Comput Syst 78:641–658. https://doi.org/10.1016/j.future.2017.02.014CrossRef

Al Hamid HA, Rahman SMM, Hossain MS, Almogren A, Alamri A (2017) A security model for preserving the privacy of medical big data in a healthcare cloud using a fog computing facility with pairing-based cryptography. IEEE Access 5:22313–22328. https://doi.org/10.1109/ACCESS.2017.2757844CrossRef

Elmisery AM, Rho S, Botvich D (2016) A fog based middleware for automated compliance with OECD privacy principles in internet of healthcare things. IEEE Access 4:8418–8441. https://doi.org/10.1109/ACCESS.2016.2631546CrossRef

Moosavi SR, Gia TN, Nigussie E, Rahmani AM, Virtanen S, Tenhunen H, Isoaho J (2016) End-to-end security scheme for mobility enabled healthcare Internet of Things. Futur Gener Comput Syst 64:108–124. https://doi.org/10.1016/j.future.2016.02.020CrossRef

10.

Liu X, Deng RH, Yang Y, Tran HN, Zhong S (2018) Hybrid privacy-preserving clinical decision support system in fog-cloud computing. Futur Gener Comput Syst 78:825–837. https://doi.org/10.1016/j.future.2017.03.018CrossRef

11.

Etemadi M, Ghobaei-Arani M, Shahidinejad A (2021) A cost-efficient auto-scaling mechanism for IoT applications in fog computing environment: a deep learning-based approach. Cluster Comput 24:3277–3292. https://doi.org/10.1007/s10586-021-03307-2CrossRef

12.

Zhang P, Zhou M, Fortino G (2018) Security and trust issues in Fog computing: a survey. Futur Gener Comput Syst 88:16–27. https://doi.org/10.1016/j.future.2018.05.008CrossRef

13.

Alemneh E, Senouci SM, Brunet P (2017, October) PV-Alert: A fog-based architecture for safeguarding vulnerable road users. In: 2017 Global Information Infrastructure and Networking Symposium (GIIS). IEEE, pp 9–15. https://doi.org/10.1109/GIIS.2017.8169804

14.

Hbaieb A, Ayed S, Chaari L (2022) A survey of trust management in the Internet of Vehicles. Comput Netw 203:108558. https://doi.org/10.1016/j.comnet.2021.108558CrossRef

15.

Taghizadeh J, Ghobaei-Arani M, Shahidinejad A (2023) An efficient data replica placement mechanism using biogeography-based optimization technique in the fog computing environment. J Ambient Intell Human Comput 14:3691–3711. https://doi.org/10.1007/s12652-021-03495-0CrossRef

16.

Fernández-Caramés TM, Fraga-Lamas P, Suárez-Albela M, Vilar-Montesinos M (2018) A fog computing and cloudlet based augmented reality system for the industry 4.0 shipyard. Sensors 18(6):1798. https://doi.org/10.3390/s18061798CrossRef

17.

Torabi E, Ghobaei-Arani M, Shahidinejad A (2022) Data replica placement approaches in fog computing: a review. Cluster Comput 25:3561–3589. https://doi.org/10.1007/s10586-022-03575-6CrossRef

18.

Tekiyehband M, Ghobaei-Arani M, Shahidinejad A (2022) An efficient dynamic service provisioning mechanism in fog computing environment: a learning automata approach. Expert Syst Appl 198:116863. https://doi.org/10.1016/j.eswa.2022.116863CrossRef

19.

Singh S, Kandpal M (2022) A comprehensive survey on trust management in Fog computing. ICT Anal Appl. https://doi.org/10.1007/978-981-16-5655-2_9CrossRef

20.

Almas A, Iqbal W, Altaf A, Saleem K, Mussiraliyeva S, Iqbal MW (2023) Context-based adaptive Fog computing trust solution for time-critical smart healthcare systems. IEEE Internet Things J

21.

Ghobaei-Arani M, Asadianfam S, Abolfathi A (2022) Deploying IoT services on the fog infrastructure: a graph partitioning-based approach. Softw Pract Exp 52(9):1967–1986. https://doi.org/10.1002/spe.3111CrossRef

22.

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. https://doi.org/10.3390/s19081788CrossRef

23.

Reiss-Mirzaei M, Ghobaei-Arani M, Esmaeili L (2023) A review on the edge caching mechanisms in the mobile edge computing: a social-aware perspective. Int Things 22:100690. https://doi.org/10.1016/j.iot.2023.100690CrossRef

24.

Abdelghani W, Amous I, Zayani CA, Sèdes F, Roman-Jimenez G (2022) Dynamic and scalable multi-level trust management model for social internet of things. J Supercomput 78(6):8137–8193CrossRef

25.

Shahidinejad A, Ghobaei-Arani M, Souri A, Shojafar M, Kumari S (2021) Light-edge: a lightweight authentication protocol for IoT devices in an edge-cloud environment. IEEE Consum Electron Mag 11(2):57–63CrossRef

26.

Wei L, Yang Y, Wu J, Long C, Li B (2022) Trust management for internet of things: a comprehensive study. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2021.3139989CrossRef

27.

Dybedokken TS (2017) Trust management in fog computing (Master’s thesis, NTNU). http://hdl.handle.net/11250/2454375

28.

Yan Z, Zhang P, Vasilakos AV (2014) A survey on trust management for Internet of Things. J Netw Comput Appl 42:120–134. https://doi.org/10.1016/j.jnca.2014.01.014CrossRef

29.

Salimian M, Ghobaei-Arani M, Shahidinejad A (2021) Toward an autonomic approach for internet of things service placement using gray wolf optimization in the fog computing environment. Soft Pract Exp 51(8):1745–1772. https://doi.org/10.1002/spe.2986CrossRef

30.

Agushaka JO, Ezugwu AE (2022) Initialisation approaches for population-based metaheuristic algorithms: a comprehensive review. Appl Sci 12(2):896. https://doi.org/10.3390/app12020896CrossRef

31.

Jazayeri F, Shahidinejad A, Ghobaei-Arani M (2021) Autonomous computation offloading and auto-scaling the in the mobile fog computing: a deep reinforcement learning-based approach. J Ambient Intell Humaniz Comput 12:8265–8284CrossRef

32.

Thakur PS, Kiran U, Sahu OP (2022) A review on: machine learning techniques to mitigate security risks in IoT framework state of the art. In: Futuristic Communication and Network Technologies. Springer, Singapore, pp 671–679. https://www.springerprofessional.de/en/a-review-on-machine-learning-techniques-to-mitigate-security-ris/19748932

33.

Farahbakhsh F, Shahidinejad A, Ghobaei-Arani M (2021) Multiuser context-aware computation offloading in mobile edge computing based on Bayesian learning automata. Trans Emerging Telecommun Technol 32(1):e4127. https://doi.org/10.1002/ett.4127CrossRef

34.

Saha A, Chowdhury C, Jana M, Biswas S (2021) IoT sensor data analysis and fusion applying machine learning and meta-heuristic approaches. Enabling AI Appl Data Sci. https://doi.org/10.1007/978-3-030-52067-0_20CrossRef

35.

Rahman FH, Au TW, Newaz SS, Suhaili WS, Lee GM (2020) Find my trustworthy fogs: a fuzzy-based trust evaluation framework. Futur Gener Comput Syst 109:562–572. https://doi.org/10.1016/j.future.2018.05.061CrossRef

36.

Rathee G, Sandhu R, Saini H, Sivaram M, Dhasarathan V (2020) A trust computed framework for IoT devices and fog computing environment. Wireless Netw 26(4):2339–2351. https://doi.org/10.1007/s11276-019-02106-3CrossRef

37.

Al-Khafajiy M, Baker T, Asim M, Guo Z, Ranjan R, Longo A, Puthal D, Taylor M (2020) COMITMENT: a fog computing trust management approach. J Parallel and Distrib Comput 137:1–16. https://doi.org/10.1016/j.jpdc.2019.10.006CrossRef

38.

Hussain Y, Zhiqiu H, Akbar MA, Alsanad A, Alsanad AAA, Nawaz A, Khan IA, Khan ZU (2020) Context-aware trust and reputation model for fog-based IoT. IEEE Access 8:31622–31632. https://doi.org/10.1109/ACCESS.2020.2972968CrossRef

39.

Awan KA, Din IU, Almogren A, Almajed H, Mohiuddin I, Guizani M (2020) Neurotrust-artificial neural network-based intelligent trust management mechanism for large-scale internet of medical things. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2020.3029221CrossRef

40.

El-Sayed H, Ignatious HA, Kulkarni P, Bouktif S (2020) Machine learning based trust management framework for vehicular networks. Veh Commun 25:100256. https://doi.org/10.1016/j.vehcom.2020.100256CrossRef

41.

Wang T, Zeng J, Lai Y, Cai Y, Tian H, Chen Y, Wang B (2020) Data collection from WSNs to the cloud based on mobile Fog elements. Futur Gener Comput Syst 105:864–872. https://doi.org/10.1016/j.future.2017.07.031CrossRef

42.

Ilyas M, Ullah Z, Khan FA, Chaudary MH, Malik MSA, Zaheer Z, Durrani HUR (2020) Trust-based energy-efficient routing protocol for Internet of things-based sensor networks. Int J Distrib Sens Netw. https://doi.org/10.1177/1550147720964358CrossRef

43.

Subramanian N, GB SM, Martin JP, Chandrasekaran K (2020). HTmRPL++: a trust-aware RPL routing protocol for fog enabled Internet of Things. In: 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS). IEEE, pp 1–5

44.

Haseeb K, Islam N, Javed Y, Tariq U (2020) A lightweight secure and energy-efficient fog-based routing protocol for constraint sensors network. Energies. https://doi.org/10.3390/en14010089CrossRef

45.

Atzori L, Iera A, Morabito G (2011) Siot: giving a social structure to the internet of things. IEEE Commun Lett 15(11):1193–1195. https://doi.org/10.1109/LCOMM.2011.090911.111340CrossRef

46.

Haßlinger G, Hohlfeld O (2008, March) The Gilbert-Elliott model for packet loss in real time services on the Internet. In: 14th GI/ITG Conference-Measurement, Modelling and Evalutation of Computer and Communication Systems. VDE, pp 1–15

47.

Velloso PB, Laufer RP, Cunha DDO, Duarte OCM, Pujolle G (2010) Trust management in mobile ad hoc networks using a scalable maturity-based model. IEEE Trans Netw Serv Manage 7(3):172–185. https://doi.org/10.1109/TNSM.2010.1009.I9P0339CrossRef

48.

Bao F, Chen I (2012) Dynamic trust management for internet of things applications. In: Proceedings of the International Workshop on Self-aware Internet of Things, ACM. https://doi.org/10.1145/2378023.2378025

49.

Chen I, Guo J (2014) Dynamic hierarchical trust management of mobile groups and its application to misbehaving node detection, In: IEEE 28th International Conference on Advanced Information Networking and Applications. https://doi.org/10.1109/AINA.2014.13

50.

Jøsang A (2016) Subjective logic. Springer, Cham, pp 51–82. https://doi.org/10.1007/978-3-319-42337-1CrossRefMATH

51.

Jøsang A, Marsh S, Pope S (2006) Exploring different types of trust propagation. In: International Conference on Trust Management. Springer, Berlin, pp 179–192. https://doi.org/10.1007/11755593_14

52.

Chen R, Guo J, Bao F, Cho JH (2013) Integrated social and quality of service trust management of mobile groups in ad hoc networks. In: 2013 9th International Conference on Information, Communications & Signal Processing. IEEE, pp 1–5. https://doi.org/10.1109/ICICS.2013.6782950

53.

Tofighy S, Rahmanian AA, Ghobaei-Arani M (2018) An ensemble CPU load prediction algorithm using a Bayesian information criterion and smooth filters in a cloud computing environment. Soft Pract Exp 48(12):2257–2277. https://doi.org/10.1002/spe.2641CrossRef

54.

Gheisari S, Meybodi MR (2017) A new reasoning and learning model for cognitive wireless sensor networks based on Bayesian networks and learning automata cooperation. Comput Netw 124:11–26. https://doi.org/10.1016/j.comnet.2017.05.031CrossRef

55.

Mahmoudi M, Faez K, Ghasemi A (2020) Defense against primary user emulation attackers based on adaptive Bayesian learning automata in cognitive radio networks. Ad Hoc Netw 102:102147. https://doi.org/10.1016/j.adhoc.2020.102147CrossRef

56.

Gupta H, Vahid Dastjerdi A, Ghosh SK, Buyya R (2017) iFogSim: a toolkit for modeling and simulation of resource management techniques in the Internet of Things, edge and Fog computing environments. Softw Pract Exp 47(9):1275–1296. https://doi.org/10.1002/spe.2509CrossRef

Title: TTLA: two-way trust between clients and fog servers using Bayesian learning automata
Authors: Niloofar Barati Bakhtiari
Masood Rafighi
Reza Ahsan
Publication date: 25-04-2023
Publisher: Springer US
Published in: The Journal of Supercomputing / Issue 14/2023
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-023-05264-6

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 14/2023

CovSumm: an unsupervised transformer-cum-graph-based hybrid document summarization model for CORD-19

A survey on automation approaches of smart contract generation

A two-phase detection method against APT attack on flow table management in SDN

Blockchain-enabled secure and efficient data sharing scheme for trust management in healthcare smartphone network

Examining the status of CPU working load, processing load and controller bandwidth under the influence of packet-in buffer status located in Openflow switches in SDN-based IoT framework

Dynamic stochastic game-based security of edge computing based on blockchain

Premium Partner