Skip to main content
SpringerLink
Log in

Evaluating the Sustainability of Cerebral Edge Computing Inventiveness for Acquiring Internet of Things Substructure Autonomously

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Advancements in IoT have integrated it into every aspect of human life. By using the Internet as its foundation, IoT connects a vast array of cyber-physical devices, from simple sensors to advanced servers. However, this extensive connectivity also broadens the attack surface, increasing vulnerability to cyber threats due to the complex communication and non-standard technologies involved. The proposed response selection method addresses this by employing fuzzy logic inference at edge nodes, which processes the ambiguous data generated by IoT devices. The system evaluates four metrics: device importance, severity score, response cost, and success rate, calculated efficiently at the edge. Proximity to end devices makes edge nodes ideal for this task. Simulations reveal that the edge-based approach improves response success rates by about 5% compared to cloud-based implementations. This underscores the model’s ability to accurately and swiftly select appropriate responses, demonstrating the effectiveness of edge computing in enhancing IoT security and performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Data Availability

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

References

  1. Balte A, Kashid A&, Patil B. Security issues in internet of things (IoT): a survey. Int J Adv Res Comput Sci Softw Eng. 2015;5(4):450–5.

    Google Scholar 

  2. Banerjee Anjishnu, David B, Dunson, Surya T, Tokdar. ‘Efficient gaussian process regression for large datasets’. Biometrika. 2012;100(1):75–89.

  3. Kolias C, Kambourakis G, Stavrou A&, Gritzalis S. Intrusion detection in 802.11 networks: empirical evaluation of threats and a public dataset. IEEE Commun Surv Tutorials. 2016;18(1):184–208.

    Article  Google Scholar 

  4. Kott A, Wang C, Erbacher RF. Cyber defense and situational awareness’. Springer; 2015;62.

  5. Roy V. An effective FOG computing based distributed forecasting of cyber-attacks in internet of things. J Cybersecur Inform Manage. 2023;12(2):8–17.

    Article  Google Scholar 

  6. Riggins Frederick J. & Samuel Fosso Wamba 2015, ‘Research directions on the adoption, usage, and impact of the internet of things through the use of big data analytics’, 48th Hawaii International Conference on System Sciences (HICSS), pp. 1531–1540.

  7. Dewanta F, Mambo MBPT, Scheme. Establishing trusted vehicular FOG computing service for rural area based on blockchain approach. IEEE Trans Veh Technol. 2021;70:1752–69.

    Article  Google Scholar 

  8. Yakubu J, Abdulhamid SM, Christopher HA, Chiroma H, Abdullahi M. Security challenges in edge-computing environment: a systematic appraisal of current developments. J Reliab Intell Environ. 2019;5:209–33.

    Article  Google Scholar 

  9. Roy V. A context-aware internet of things (IoT) founded approach to scheming an operative priority-based scheduling algorithms. J Cybersecur Inform Manage. 2024;13(1).

  10. Wang J, Li D. Adaptive computing optimization in software-defined network-based industrial internet of things with FOG computing. Sensors. 2018;18:2509.

    Article  Google Scholar 

  11. Sahil; Sood SK. FOG-cloud centric IoT-based cyber physical framework for panic oriented disaster evacuation in smart cities. Earth Sci Inf. 2022;15:1449–70.

    Article  Google Scholar 

  12. Kumar P, Gupta GP, Tripathi RA, Distributed Ensemble. Design based intrusion detection system using FOG computing to protect the internet of things networks. J Ambient Intell Humaniz Comput. 2021;12:9555–72.

    Article  Google Scholar 

  13. Saini K, Kalra S, Sood SK. Disaster emergency response framework for smart buildings. Future Gener Comput Syst. 2022;131:106–20.

    Article  Google Scholar 

  14. Roy V, Roy L, Ahluwalia R, Khambra G, Ramesh M, Rajasekhar K. An advance implementation of machine learning techniques for the prediction of cervical cancer, 2023 IEEE International Conference on ICT in Business Industry & Government.

  15. Villegas-Ch W, Barahona-Espinosa S, Gaibor-Naranjo W, Mera-Navarrete A. Model for the detection of falls with the use of artificial intelligence as an assistant for the care of the elderly. Computation. 2022;10:195.

    Article  Google Scholar 

  16. Rezapour R, Asghari P, Javadi HHS, Ghanbari S. Security in FOG computing: a systematic review on issues, challenges and solutions. Comput Sci Rev. 2021;41:100421.

    Article  Google Scholar 

  17. Wang S, Zhao T, Pang S. Task scheduling algorithm based on improved firework algorithm in FOG computing. IEEE Access. 2020;8:32385–94.

    Article  Google Scholar 

  18. Ortiz-Garcés I, Villegas-Ch W. Model of telecommunications infrastructure for the deployment of technological services from the CLOUD to all the localities of the Ministry of Education in Ecuador. RISTI—Rev Iber Sist Tecnol Inf. 2019;E19:531–42.

    Google Scholar 

  19. Singh J, Singh P, Gill SSFOG, Computing. A taxonomy, systematic review, current trends and research challenges. J Parallel Distrib Comput. 2021;157:56–85.

    Article  Google Scholar 

  20. Bellavista P, Berrocal J, Corradi A, Das SK, Foschini L, Zanni A. A survey on FOG computing for the internet of things. Pervasive Mob Comput. 2019;52:71–99.

    Article  Google Scholar 

  21. Sarkar S, Chatterjee S, Misra S. Assessment of the suitability of FOG computing in the context of internet of things. IEEE Trans Cloud Comput. 2018;6:46–59.

    Article  Google Scholar 

  22. Zhang PY, Zhou MC, Fortino G. Security and trust issues in FOG computing: a survey. Future Gener Comput Syst. 2018;88:16–27.

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Lera I, Guerrero C, Juiz CYAFS. A simulator for IoT scenarios in FOG computing. IEEE Access. 2019;7:91745–58.

    Article  Google Scholar 

  25. Mouradian C, Naboulsi D, Yangui S, Glitho RH, Morrow MJ, Polakos PA. A comprehensive survey on FOG computing: state-of-the-art and research challenges. IEEE Commun Surv Tutor. 2018;20:416–64.

    Article  Google Scholar 

  26. Moshayedi AJ, Roy AS, Taravet A, Liao L, Wu J, Gheisari MA. Secure traffic police remote sensing approach via a deep learning-based low-altitude vehicle speed detector through UAVs in smart cites: algorithm, implementation and evaluation. Future Transp. 2023;3:12.

    Article  Google Scholar 

  27. Roy V. An improved image encryption consuming fusion transmutation and edge operator. J Cybersecur Inform Manage. 2021;8(1):42–52.

    Article  Google Scholar 

  28. Tselios C, Politis I, Amaxilatis D, Akrivopoulos O, Chatzigiannakis I, Panagiotakis S, Markakis EK. Melding FOG computing and IoT for deploying secure, response-capable healthcare services in 5G and beyond. Sensors. 2022;22:3375.

    Article  Google Scholar 

  29. Prabavathy S, Sundarakantham K, Mercy Shalinie S. Design of cognitive FOG computing for autonomic security system in critical infrastructure. J Univers Comput Sci. 2018;24(5):577–602.

    Google Scholar 

  30. Alrizq M, Stalin S, Alyami S, Roy V, Mishra A, Chandanan AK. Optimization of sensor node location utilizing artificial intelligence for mobile wireless sensor network. Wireless Netw. 2023:1–13.

Download references

Funding

Not Applicable.

Author information

Authors and Affiliations

  1. Instituto Tecnológico Superior Rumiñahui, Sangolquí, Ecuador

    Marcelo Zambrano-Vizuete, Juan Minango-Negrete & Jorge Pérez-Chimborazo

  2. Consejo de Aseguramiento de la Calidad de la Educación Superior, Instituto Tecnológico Superior Rumiñahui, Quito, Ecuador

    Wladimir Paredes-Parada

  3. Escuela Politécnica Nacional, Quito, Ecuador

    Ana Zambrano-Vizuete

Authors
  1. Marcelo Zambrano-Vizuete
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juan Minango-Negrete
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wladimir Paredes-Parada
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jorge Pérez-Chimborazo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ana Zambrano-Vizuete
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Marcelo Zambrano-Vizuete: Consumption and design of study and Acquisition of the data, Juan Minango-Negrete: Analysis and interpretation of the data and Drafting, Wladimir Paredes-Parada: Formalization an editing, Review and investigation, Jorge Pérez-Chimborazo: Conceptualization, Ana Zambrano-Vizuete: Investigation and analysis.

Corresponding author

Correspondence to Marcelo Zambrano-Vizuete.

Ethics declarations

Ethical Approval

This article does not contain any studies with animals performed by any of the authors.

Conflict of Interest

The authors declare no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zambrano-Vizuete, M., Minango-Negrete, J., Paredes-Parada, W. et al. Evaluating the Sustainability of Cerebral Edge Computing Inventiveness for Acquiring Internet of Things Substructure Autonomously. SN COMPUT. SCI. 5, 922 (2024). https://doi.org/10.1007/s42979-024-03220-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-024-03220-6

Keywords

Search

Navigation