EFFICIENT LOAD BALANCING TECHNIQUES FOR MOBILE FOG NETWORKS IN THE FOG LAYER: A REVIEW
DOI:
https://doi.org/10.33003/fjs-2025-0912-3939Keywords:
Internet of Things (IoT), Cloud Computing, Fog Networks (FogNets), Load balancing, Fog layer, Mobile Fog Networks, Fog Computing EnvironmentsAbstract
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.
References
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
Downloads
Published
Issue
Section
Categories
License
Copyright (c) 2025 Selumun Agber, Dr. M. B. Abdullahi, Dr. S. A. Bashir, Dr. O. A. Ojerinde
This work is licensed under a Creative Commons Attribution 4.0 International License.
