[1] Boubaker, N. E. H., Zarour, K., Guermouche, N., & Benmerzoug, D. (2025). A comprehensive survey on resource management for IoT applications in edge–fog–cloud environments. IEEE Access.
[2] Sohrabi, S., Sakhaei-nia, M., Nassiri, M., & Mohammadi, R. (2025). IJFSA-OPG: A hybrid energy efficient load-balancing mechanism for fog-based IoT healthcare. Manuscript submitted for publication, Cloud Computing Journal.
[3] Ghorbian, Mohsen, Mostafa Ghobaei-Arani, and Leila Esmaeili. "A survey on the scheduling mechanisms in serverless computing: a taxonomy, challenges, and trends." Cluster Computing (2024): 1-40.
[4] Mokni, I., & Yassa, S. (2024). A multi-objective approach for op-timizing IoT applications offloading in fog–cloud environments with NSGA-II. The Journal of Supercomputing, 1-39.
[5] Aghazadeh, Rafat, Ali Shahidinejad, and Mostafa Ghobaei‐Arani. "Proactive content caching in edge computing environment: A re-view." Software: Practice and Experience 53.3 (2023): 811-855.
[6] Bezdan, T.; Zivkovic, M.; Bacanin, N.; Strumberger, I.; Tuba, E.; Tuba, M. Multi-objective task scheduling in a cloud computing environment by hybridized bat algorithm. J. Intell. Fuzzy Syst. 2022, 42, 411–423
[7] Available online: https://eucloudedgeiot.eu/ (accessed on 14 Feb-ruary 2023).
[8] Ghorbian, Mohsen, Mostafa Ghobaei-Arani, and Rohollah Asado-lahpour-Karimi. "Function Placement Approaches in Serverless Computing: A Survey." Journal of Systems Architecture (2024): 103291.
[9] Abohamama, A.S.; El-Ghamry, A.; Hamouda, E. Real-time task scheduling algorithm for IoT-based applications in the cloud–fog environment. J. Netw. Syst. Manag. 2022, 30, 1–35.
[10] Zhang, Y., et al. (2022). "Task Scheduling in Cloud Computing Environment Using Advanced Phasmatodea Population Evolution Algorithm." MDPI Electronics, 11(9), 1451. DOI: 10.3390/electronics11091451MDPI
[11] Kumar, R., et al. (2022). "An Efficient Task Scheduling in a Cloud Computing Environment Using Hybrid Genetic Algorithm - Parti-cle Swarm Optimization (GA-PSO) Algorithm." World Scientific Journal, 28(3), 1850024. DOI: 10.1142/S0219622018500244ResearchGate+1
[12] Chen, W., et al. (2021). "Blockchain-Enhanced Fair Task Schedul-ing for Cloud-Fog-Edge Systems." IEEE Transactions on Industri-al Informatics, 17(4), 2567-2576. DOI: 10.1109/TII.2020.3023184buyya.com
[13] Baniata, H., Anaqreh, A., Kertesz, A.: PF-BTS: a privacy-aware fog-enhanced blockchain-assisted task scheduling. Inf. Process. Manage. (2021). https:// doi. org/ 10. 1016/j. ipm. 2020. 102393.
[14] Mirmohseni, S.M.; Tang, C.; Javadpour, A. FPSO-GA: A Fuzzy Metaheuristic Load Balancing Algorithm to Reduce Energy Con-sumption in Cloud Networks. Wirel. Pers. Commun. 2022, 127, 2799–2821.
[15] Malik, M.; Suman. Lateral Wolf Based Particle Swarm Optimiza-tion (LW-PSO) for Load Balancing on Cloud Computing. Wirel. Pers. Commun. 2022, 1, 1–20.
[16] Baniata, H., Anaqreh, A., Kertesz, A.: PF-BTS: a privacy-aware fog-enhanced blockchain-assisted task scheduling. Inf. Process. Manage. (2021). https:// doi. org/ 10. 1016/j. ipm. 2020. 102393.
[17] Beraldi R, Canali C, Lancellotti R, Mattia G. Distributed load bal-ancing for heterogeneous fog computing infrastructures in smart cities. Pervasive Mob Comput 2020;67. https://doi.org/10.1016/j.pmcj.2020.101221.
[18] Sumathi M, Vijayaraj N, Raja SP, Rajkamal M. HHO-ACO hy-bridized load balancing technique in cloud computing. Int J Inf Technol 2023;15(3):1357–65.
[19] Gabhane JP, Pathak S, Thakare NM. A novel hybrid multi-resource load balancing approach using ant colony optimization with Tabu search for cloud computing. Innov Syst Softw Eng 2023;19(1):81–90. https://doi.org/10.1007/s11334-022-00508- 9.
[20] Salimi, R., Azizi, S., & Dogani, J. (2025). A hybrid priority-aware genetic algorithm and opposition-based learning for scheduling IoT tasks in green fog computing. Computer Networks, 267, Arti-cle 111349.
[21] Alsadie, D., & Alsulami, M. (2025). Modified grey wolf optimiza-tion for energy-efficient Internet of Things task scheduling in fog computing. Scientific Reports, 15, Article 14730.
[22] Rateb, R., Hadi, A. A., Tamanampudi, V. M., Abualigah, L., Ezugwu, A. E., et al. (2025). An optimal workflow scheduling in IoT–fog–cloud systems for minimizing time and energy. Scientific Reports, 15, Article 3607.
[23] Nazeri, M., Soltanaghaei, M., & Khorsand, R. (2024). A predictive energy-aware scheduling framework for scientific workflows in fog computing using MAPE-K and ANFIS. Expert Systems with Appli-cations, 247, Article 123192.
[24] S. Sohrabi, M. Sakhaei-nia, M. Nassiri, and R. Mohammadi, “Fog-based architecture and efficient task offloading methodology in IoT-based applications for smart irrigation system,” Computing, vol. 107, no. 3, p. 76, 2025.
[25] N. Jangu and Z. Raza, “Improved Jellyfish Algorithm-based multi-aspect task scheduling model for IoT tasks over fog integrated cloud environment,” Journal of Cloud Computing, vol. 11, no. 1, p. 98, 2022.
[26] Khaledian, Navid, et al. "An energy-efficient and deadline-aware workflow scheduling algorithm in the fog and cloud environment." Computing 106.1 (2024): 109-137.
[27] Liu, W., Li, C., Zheng, A., Zheng, Z., Zhang, Z., & Xiao, Y. (2023). Fog computing resource-scheduling strategy in IoT based on artifi-cial bee colony algorithm. Electronics, 12(7), 1511.