Fog Computational-Based Deep Learning Model for Optimization of Micro Grid Connected WSN With Load Balancing
Keywords:
Cloud Computing, Fog Computing, Load Balancing, Smart Grid, WSNs.Abstract
IoT applications for the smart environments have proliferated with the introduction of the Cloud Computing. However, delay-sensitive programmes can't use these resources because of how far apart they are. Fog computing has arisen to give such capabilities in close proximity to end devices via dispersed resources, and it plays a crucial role in optimizing the connection between load balancing, microgrids, and WSNs. Using the idea of the "stateless micro-Fog service replicas", these constrained resources may work together to support dispersed IoT application operations, ensuring service availability even in the face of failures. Through load balancing, workloads are distributed between Fog nodes in an equitable manner, maximizing the use of computation and network resources while reducing lag time for application execution.
References
[1] Alhasnawi, B. N., Jasim, B. H., & Siano, P. (2021). A Novel Real-Time Electricity Scheduling for Home Energy Management System Using the Internet of Energy. https://doi.org/10.3390/en14113191
[2] Ali, M., Ismail, M., Pervaiz, H., Atat, R., Bayram, I. S., & Ni, Q. (2023). e-Prime - Advances in Electrical Engineering , Electronics and Energy Reinforcement learning-based allocation of fog nodes for cloud-based smart grid. E-Prime - and Energy, 4(March), 100144. https://doi.org/10.1016/j.prime.2023.100144
[3] Kashani, M. H. (2022). Proof. July. https://doi.org/10.1109/TSC.2022.3174475
[4] Khan, S., Parkinson, S., & Qin, Y. (2017). Fog computing security : a review of current applications and security solutions. https://doi.org/10.1186/s13677-017-0090-3
[5] Malik, S., Gupta, K., Gupta, D., Singh, A., Ibrahim, M., Ortega-mansilla, A., Goyal, N., & Hamam, H. (2022). Intelligent Load-Balancing Framework for Fog-Enabled Communication in Healthcare. 1-18. https://doi.org/10.3390/electronics11040566
[6] Manna, M. E. (2022). Hybrid load-balancing algorithm for distributed fog computing in internet of things environment Hybrid load-balancing algorithm for distributed fog computing in internet of things environment. October. https://doi.org/10.11591/eei.v11i6.4127
[7] Masri, A., & Al-jabi, M. (2021). Toward fault tolerant modelling for SCADA based electricity distribution networks , machine learning approach. https://doi.org/10.7717/peerj-cs.554
[8] Moura, J., & Hutchison, D. (n.d.). Fog Computing Systems : State of the Art , Research Issues and Future Trends , with a Focus on Resilience. 1-38.
[9] Saif, F. A., Latip, R., & Hanapi, Z. M. (2023). Multi-Objective Grey Wolf Optimizer Algorithm for Task Scheduling in Cloud-Fog Computing. IEEE Access, 11(March), 20635-20646. https://doi.org/10.1109/ACCESS.2023.3241240
[10] Sheikh, M., Mostafa, S., Kashani, H., & Mahdipour, E. (2022). Towards effective offloading mechanisms in fog computing. In Multimedia Tools and Applications. Springer US. https://doi.org/10.1007/s11042-021-11423-9
[11] Singh, J., Singh, P., & Gill, S. S. (2021). Fog Computing : A Taxonomy , Systematic Review , Current Trends and Research Challenges. 1-39. https://doi.org/10.1016/j.jpdc.2021.06.005
[12] Singhal, S., Athithan, S., Alomar, M. A., Kumar, R., Sharma, B., Srivastava, G., & Lin, J. C. (2023). Energy Aware Load Balancing Framework for Smart Grid Using Cloud and Fog Computing. https://doi.org/10.3390/s23073488
[13] Varmaghani, A., Nazar, A. M., Ahmadi, M., Sharifi, A., Ghoushchi, S. J., & Pourasad, Y. (2021). Research Article DMTC : Optimize Energy Consumption in Dynamic Wireless Sensor Network Based on Fog Computing and Fuzzy Multiple Attribute Decision-Making. 2021 https://doi.org/10.1155/2021/9953416
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