Skip to main content

Advertisement

Log in

Enhanced subtraction-average-based optimizer and blockchain for security and load balancing in fog computing

  • Original Paper
  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Fog computing has provided answers to several issues in recent years, including the accuracy and caliber of the data gathered. Due to its inherent distributed architecture, security and load-balancing techniques present difficulties. This study proposes blockchain-based user authentication for fog computing and enhanced subtraction-average-based optimizer (ESABA)-based load balancing. Fog computing has integrated blockchain technology into its user authentication procedure. Subtraction Average Based Optimizer (SABO) and Oppositional Based Learning (OBL) functions are then combined to form the ESABA. The oppositional function in the SABO is employed to improve the initial population through their solutions. This procedure yields the best results for load balancing and user authentication. The suggested approach is put into practice in Python, and several performance metrics like waiting time, processing time, memory consumption for user verification, and authentication are assessed. The performance compared with the existing algorithm. The results demonstrate superior performance in both security and load balancing, highlighting the method’s potential for practical applications in fog computing. The proposed method takes a minimum response time of 0.5 s for twenty-five task scheduling.

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

Access this article

Subscribe and save

Springer+
from $39.99 /Month
  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

Data sharing does not apply to this article as no datasets were generated or analyzed during the current study.

References

  1. CISCO, (2015). “Cisco Fog Computing Solutions: Unleash the Power of the Internet of Things,”p. 6, [Online]. Available: https://www.cisco.com/c/dam/en us/solutions/trends/iot/docs/computing-solutions.pdf.

  2. Dastjerdi, H., Gupta, R., Calheiros, S., Ghosh, and Buyya, R. (2016). Fog Computing: principles,architectures, and applications,” in Internet of Things. Elsevier, pp. 61–75.[Online].Available:http://linkinghub.elsevier.com/retrieve/pii/B9780128053959000046.

  3. Tordera, E. M., Masip-Bruin, X., Garc´ıa-Almi˜nana, J., Jukan, A., Ren, G.-J., and Zhu, J. (2017). Do we all really know what a fog node is? current trends towards.an open definition,” Computer Communications. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S0140366416307113?via=ihub.

  4. Xu, X., Fu, S., Cai, Q., Tian, W., Liu, W., Dou, W., Sun, X., & Liu, A. X. (2018). Dynamic resource allocation for load balancing in fog environment. Wireless Communications and Mobile Computing, 2018, 6421607.

    Article  Google Scholar 

  5. Al-fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of things : A survey on enabling internet of things : A survey on enabling technologies, protocols, and applications. IEEE communications surveys & tutorials, 17(4), 2347–2376.

    Article  Google Scholar 

  6. Puthal, D., Obaidat, M. S., Nanda, P., Prasad, M., Mohanty, S. P., & Zomaya, A. Y. (2018). Secure and sustainable load balancing of edge data centers in fog computing. IEEE Communications Magazine, 56(5), 60–65.

    Article  Google Scholar 

  7. Verma, M., Bhardwaj, N., & Yadav, A. K. (2016). Real time efficient scheduling algorithm for load balancing in fog computing environment. International Journal of Information Technology and Computer Science, 8(4), 1–10.

    Article  MATH  Google Scholar 

  8. Puthal, D., Ranjan, R., Nanda, A., Nanda, P., Jayaraman, P. P., & Zomaya, A. Y. (2019). Secure authentication and load balancing of distributed edge datacenters. Journal of Parallel and Distributed Computing, 124, 60–69.

    Article  MATH  Google Scholar 

  9. Talaat, F. M., Ali, S. H., Saleh, A. I., & Ali, H. A. (2019). Effective load balancing strategy (ELBS) for real-time fog computing environment using fuzzy and probabilistic neural networks. Journal of Network and Systems Management, 27, 883–929.

    Article  MATH  Google Scholar 

  10. Mazumdar, N., Nag, A., & Singh, J. P. (2021). Trust-based load-offloading protocol to reduce service delays in fog-computing-empowered IoT. Computers & Electrical Engineering, 93, 107223.

    Article  MATH  Google Scholar 

  11. Srinivas, KV., and Krishna, BG., fog computing for secure and sustainable load balancing of edge data centers.

  12. He, X., Ren, Z., Shi, C., & Fang, J. (2016). A novel load balancing strategy of software-defined cloud/fog networking in the Internet of Vehicles. China Communications, 13(Supplement2), 140–149.

    Article  Google Scholar 

  13. Kashyap, V., Ahuja, R., & Kumar, A. (2024). A hybrid approach for fault-tolerance aware load balancing in fog computing. Cluster Computing. https://doi.org/10.21203/rs.3.rs-3315754/v1

    Article  MATH  Google Scholar 

  14. Premkumar, N., & Santhosh, R. (2024). Secure authentication scheme with Archimedes optimization algorithm for load balancing technique in fog computing. International Journal of Information Technology. https://doi.org/10.1007/s41870-024-01861-7

    Article  MATH  Google Scholar 

  15. Mahapatra, A., SK, Majhi., K, Mishra., R, Pradhan., DC, Rao., and SK, Panda. (2024). An energy-aware task offloading and load balancing for latency-sensitive IoT applications in the Fog-Cloud continuum. IEEE Access.

  16. Hasan, M. K., Sundararajan, E., Islam, S., Ahmed, F. R., Babiker, N. B., Alzahrani, A. I., Alalwan, N., & Khan, M. A. (2024). A novel segmented random search based batch scheduling algorithm in fog computing. Computers in Human Behavior, 158, 108269.

    Article  Google Scholar 

  17. Trojovský, P., & Dehghani, M. (2023). Subtraction-average-based optimizer: A new swarm-inspired metaheuristic algorithm for solving optimization problems. Biomimetics, 8(2), 149.

    Article  MATH  Google Scholar 

  18. Smaili, I. H., Almalawi, D. R., Shaheen, A. M., & Mansour, H. S. (2024). Optimizing PV sources and shunt capacitors for energy efficiency improvement in distribution systems using subtraction-average algorithm. Mathematics, 12(5), 625.

    Article  Google Scholar 

  19. Pradhan, M., Roy, P. K., & Pal, T. (2018). Oppositional based grey wolf optimization algorithm for economic dispatch problem of power system. Ain Shams Engineering Journal, 9(4), 2015–2025.

    Article  MATH  Google Scholar 

  20. Alzubi, J. A., Alzubi, O. A., Singh, A., & Mahmod, A. T. (2023). A blockchain-enabled security management framework for mobile edge computing. International Journal of Network Management, 33(5), e2240.

    Article  Google Scholar 

  21. Alzubi, A. J. (2021). Blockchain-based lamport merkle digital signature: Authentication tool in IoT healthcare. Computer Communications. https://doi.org/10.1016/j.comcom.2021.02.002

    Article  MATH  Google Scholar 

Download references

Funding

The authors declare that they do not have competing interests and funding.

Author information

Authors and Affiliations

Authors

Contributions

The author read and approved the final manuscript.

Corresponding author

Correspondence to N. Premkumar.

Ethics declarations

Conflict of interests

The corresponding author states that there is 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

Premkumar, N., Sridharan, S., Viswanathan, R.V. et al. Enhanced subtraction-average-based optimizer and blockchain for security and load balancing in fog computing. Wireless Netw 31, 2243–2255 (2025). https://doi.org/10.1007/s11276-024-03869-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-024-03869-0

Keywords