EFFICIENT LOAD BALANCING TECHNIQUES FOR MOBILE FOG NETWORKS IN THE FOG LAYER: A REVIEW

Authors

  • Selumun Agber
    Rev Fr Moses Orshio Adasu University (formerly Benue State University), Makurdi
  • Dr. M. B. Abdullahi
  • Dr. S. A. Bashir
  • Dr. O. A. Ojerinde

Keywords:

Internet of Things (IoT), Cloud Computing, Fog Networks (FogNets), Load balancing, Fog layer, Mobile Fog Networks, Fog Computing Environments

Abstract

The proliferation of Internet of Things (IoT) devices has significantly increased the demand for cloud services, leading to the emergence of Mobile Fog Networks (FogNets) as an effective paradigm for edge processing. This paper reviews efficient load-balancing techniques within the Fog layer, focusing on their ability to handle increasing workloads and ensure optimal resource utilization. A total of forty (40) peer-reviewed articles published between 2020-2025 were systematically reviewed using a descriptive and comparative analysis approach. The findings reveal that heuristic-based, hybrid, and machine learning driven load-balancing methods demonstrate superior performance in terms of response time, energy efficiency, and latency reduction. However, most existing studies rely on simulation-based evaluation and lack real-world deployment validation. The review concludes that future research should emphasize adaptive, context-aware, and energy-efficient load-balancing strategies to enhance real-time service delivery in FogNets.

Dimensions

Abdussami, A. A., & Farooqui, F. (2020). A Systematic Literature Review on Fog Computing Cite this paper. International Journal of Advanced Science and Technology, 29(7), 12755–12769

Aldossary, D., Aldahasi, E., Balharith, T., & Helmy, T. (2025). A Systematic Literature Review on Load-Balancing Techniques in Fog Computing: Architectures, Strategies, and Emerging Trends. In Computers (Vol. 14, Issue 6). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/computers14060217

Alwakeel, A. M. (2021). An overview of fog computing and edge computing security and privacy issues. Sensors, 21(24). https://doi.org/10.3390/s21248226

Angel, N. A., Ravindran, D., Vincent, P. M. D. R., Srinivasan, K., & Hu, Y. C. (2022). Recent advances in evolving computing paradigms: Cloud, edge, and fog technologies. In Sensors (Vol. 22, Issue 1). https://doi.org/10.3390/s22010196

Atieh, A. T. (2021). The Next Generation Cloud technologies: A Review On Distributed Cloud, Fog And Edge Computing and Their Opportunities and Challenges. In ResearchBerg Review of Science and Technology (Vol. 1, Issue 1)

Bachiega, J., Costa, B., Carvalho, L. R., Rosa, M. J. F., & Araujo, A. (2023). Computational Resource Allocation in Fog Computing: A Comprehensive Survey. ACM Computing Surveys, 55(14 S). https://doi.org/10.1145/3586181

Bajaj, K., Sharma, B., & Singh, R. (2022). Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data. Complex and Intelligent Systems, 8(5). https://doi.org/10.1007/s40747-021-00434-6

Dahiya, N., Dalal, S., & Jaglan, V. (2021). Efficient Green Solution for a Balanced Energy Consumption and Delay in the IoT-Fog-Cloud Computing. In Green Internet of Things for Smart Cities. https://doi.org/10.1201/9781003032397-7

Das, R., & Inuwa, M. M. (2023). A review on fog computing: Issues, characteristics, challenges, and potential applications. In Telematics and Informatics Reports (Vol. 10). https://doi.org/10.1016/j.teler.2023.100049

Ebrahim, M., & Hafid, A. (2023). Resilience and load balancing in Fog networks: A Multi-Criteria Decision Analysis approach. Microprocessors and Microsystems, 101. https://doi.org/10.1016/j.micpro.2023.104893

Gu, Y., Chang, Z., Pan, M., Song, L., & Han, Z. (2018). Joint Radio and Computational Resource Allocation in IoT Fog Computing. IEEE Transactions on Vehicular Technology, 67(8), 7475–7484. https://doi.org/10.1109/TVT.2018.2820838

Gupta, S., & Singh, N. (2022). Fog-GMFA-DRL: Enhanced deep reinforcement learning with hybrid grey wolf and modified moth flame optimization to enhance the load balancing in the fog-IoT environment. Advances in Engineering Software, 174. https://doi.org/10.1016/j.advengsoft.2022.103295

Hameed, A. R., ul Islam, S., Ahmad, I., & Munir, K. (2021). Energy- and performance-aware load-balancing in vehicular fog computing. Sustainable Computing: Informatics and Systems, 30. https://doi.org/10.1016/j.suscom.2020.100454

Kadhim, A. S., & Manaa, M. E. (2022). Hybrid load-balancing algorithm for distributed fog computing in internet of things environment. Bulletin of Electrical Engineering and Informatics, 11(6), 3462–3470. https://doi.org/10.11591/eei.v11i6.4127

Kadry, S., Rauf, H. T., Khalid, A., Khattak, H. A., Abbas, A., & Awaisi, K. S. (2022). A dynamic load balancing mechanism for fog computing environment. International Journal of Web and Grid Services, 18(3). https://doi.org/10.1504/ijwgs.2022.10045721

Kanbar, A. B., & Faraj, K. (2022). Region aware dynamic task scheduling and resource virtualization for load balancing in IoT–fog multi-cloud environment. In Future Generation Computer Systems (Vol. 137). https://doi.org/10.1016/j.future.2022.06.005

Kanellopoulos, D., & Sharma, V. K. (2022). Dynamic Load Balancing Techniques in the IoT: A Review. In Symmetry (Vol. 14, Issue 12). https://doi.org/10.3390/sym14122554

Kashani, M. H., Ahmadzadeh, A., & Mahdipour, E. (2022). Load balancing mechanisms in fog computing: A systematic review. IEEE Transactions on Services Computing. https://doi.org/10.1109/TSC.2022.3174475

Kashani, M. H., & Mahdipour, E. (2023). Load Balancing Algorithms in Fog Computing. IEEE Transactions on Services Computing, 16(2). https://doi.org/10.1109/TSC.2022.3174475

Kaur, H., Harnal, S., Baggan, V., & Tiwari, R. (2024). Resource Management Solutions for IoT Devices in Fog Computing Architecture. In S. Harnal, R. Tiwari, L. Garg, & A. Mathur (Eds.), Cloud and Fog Optimization-based Solutions for Sustainable Developments. CRC Press. https://doi.org/10.1201/9781003494430

Kaur, M., & Aron, R. (2021). A systematic study of load balancing approaches in the fog computing environment. Journal of Supercomputing, 77(8). https://doi.org/10.1007/s11227-020-03600-8

Kavitha, M. S., Sadashiv, N., & Dilip Kumar, S. M. (2023). Load Balancing in Fog Computing: A Detailed Survey. International Journal of Computing and Digital Systems, 13(1). https://doi.org/10.12785/ijcds/130158

Keshari, N., Singh, D., & Maurya, A. K. (2022). A survey on Vehicular Fog Computing: Current state-of-the-art and future directions. Vehicular Communications, 38. https://doi.org/10.1016/j.vehcom.2022.100512

Mall, L., Vashisht, P., & Narang, A. (2024). Quality of Service Based on Scheduling in Cloud/Fog Environment. International Journal of Innovative Research in Engineering and Management, 11(3), 65–70. https://doi.org/10.55524/ijirem.2024.11.3.10

Mijuskovic, A., Chiumento, A., Bemthuis, R., Aldea, A., & Havinga, P. (2021). Resource Management Techniques for Cloud / Fog and Edge Computing : An Evaluation Framework and Classification. Sensors, 1–22. https://doi.org/10.3390/s21051832

Mishra, K., & Majhi, S. K. (2020). A state-of-art on cloud load balancing algorithms. International Journal of Computing and Digital Systems, 9(2). https://doi.org/10.12785/IJCDS/090206

Misirli, J., & Casalicchio, E. (2024). An Analysis of Methods and Metrics for Task Scheduling in Fog Computing. In Future Internet (Vol. 16, Issue 1). https://doi.org/10.3390/fi16010016

Mondragón-Ruiz, G., Tenorio-Trigoso, A., Castillo-Cara, M., Caminero, B., & Carrión, C. (2021). An experimental study of fog and cloud computing in CEP-based Real-Time IoT applications. Journal of Cloud Computing, 10(1). https://doi.org/10.1186/s13677-021-00245-7

Pereira, E. P., Padoin, E. L., Medina, R. D., & Mehaut, J. F. (2020). Increasing the efficiency of Fog Nodes through of Priority-based Load Balancing. Proceedings - IEEE Symposium on Computers and Communications, 2020-July. https://doi.org/10.1109/ISCC50000.2020.9219576

Rateb, R., Hadi, A. A., Tamanampudi, V. M., Abualigah, L., Ezugwu, A. E., Alzahrani, A. I., Alblehai, F., & Jia, H. (2025). An optimal workflow scheduling in IoT-fog-cloud system for minimizing time and energy. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-86814-1

Rathi, S., Nagpal, R., Mehrotra, D., & Srivastava, G. (2022). A metric focused performance assessment of fog computing environments: A critical review. Computers and Electrical Engineering, 103. https://doi.org/10.1016/j.compeleceng.2022.108350

Sarma, B., Kumar, R., & Tuithung, T. (2023). A Dynamic Load Balancing Architecture for Fog Computing using Tree Base Resource Arrangement and Flexible Task Prioritization A Dynamic Load Balancing Architecture for Fog Computing using Tree Base Journal of Information Technology Management, 15.

Seraj, Y., Fadaei, S., Safaei, B., Javadi, A., Monazzah, A. M. H., & Hemmatyar, A. M. A. (2024). LIMO: Load-balanced Offloading with MAPE and Particle Swarm Optimization in Mobile Fog Networks, 2024 5th CPSSI International Symposium on Cyber-Physical Systems (Applications and Theory) (CPSAT), pp. 1-8, https://doi.org/10.1109/CPSAT64082.2024.10745455

Shafiq, D. A., Jhanjhi, N. Z., & Abdullah, A. (2022). Load balancing techniques in cloud computing environment: A review. In Journal of King Saud University - Computer and Information Sciences (Vol. 34, Issue 7). https://doi.org/10.1016/j.jksuci.2021.02.007

Singh, J., Singh, P., Amhoud, E. M., & Hedabou, M. (2022). Energy-Efficient and Secure Load Balancing Technique for SDN-Enabled Fog Computing. Sustainability (Switzerland), 14(19). https://doi.org/10.3390/su141912951

Singh, P., Kaur, R., Rashid, J., Juneja, S., Dhiman, G., Kim, J., & Ouaissa, M. (2022). A Fog-Cluster Based Load-Balancing Technique. Sustainability (Switzerland), 14(13). https://doi.org/10.3390/su14137961

Sirjani, M. S., Ahmad, M., & Sobati-Moghadam, S. (2025). Optimizing Task Scheduling in Fog Computing with Deadline Awareness. http://arxiv.org/abs/2509.07378

Vijarania, M., Gupta, S., Agrawal, A., Adigun, M. O., Ajagbe, S. A., & Awotunde, J. B. (2023). Energy Efficient Load-Balancing Mechanism in Integrated IoT–Fog–Cloud Environment. Electronics (Switzerland), 12(11). https://doi.org/10.3390/electronics12112543

Yakubu, I. Z., & Murali, M. (2023). An efficient meta-heuristic resource allocation with load balancing in IoT-Fog-cloud computing environment. Journal of Ambient Intelligence and Humanized Computing, 14(3). https://doi.org/10.1007/s12652-023-04544-6

Yan, J., Wu, D., Zhang, C., Wang, H., & Wang, R. (2018). Socially aware D2D cooperative communications for enhancing Internet of Things application. Eurasip Journal on Wireless Communications and Networking, 2018(1). https://doi.org/10.1186/s13638-018-1127-0

Fog Computing Network Architecture (Yan et al., 2018)

Published

17-11-2025

How to Cite

Agber, S., Abdullahi, M. B., Bashir, S. A., & Ojerinde, O. A. (2025). EFFICIENT LOAD BALANCING TECHNIQUES FOR MOBILE FOG NETWORKS IN THE FOG LAYER: A REVIEW. FUDMA JOURNAL OF SCIENCES, 9(12), 35-44. https://doi.org/10.33003/fjs-2025-0911-3939

Issue

Vol. 9 No. 12 (2025): FUDMA Journal of Sciences - Vol. 9 No. 12

Section

Review Articles
Copyright & Licensing

Copyright (c) 2025 Selumun Agber, Dr. M. B. Abdullahi, Dr. S. A. Bashir, Dr. O. A. Ojerinde

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Agber, S., Abdullahi, M. B., Bashir, S. A., & Ojerinde, O. A. (2025). EFFICIENT LOAD BALANCING TECHNIQUES FOR MOBILE FOG NETWORKS IN THE FOG LAYER: A REVIEW. FUDMA JOURNAL OF SCIENCES, 9(12), 35-44. https://doi.org/10.33003/fjs-2025-0911-3939

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