Vehicular Traffic Congestion Detection System and Improved Energy-Aware Cost Effective Task Scheduling Approach for Multi-Objective Optimization on Cloud Fog Network

A current research area called fog computing aims to extend the advantages of cloud computing to network edges. Task scheduling is a crucial problem for fog device data processing since a lot of data from the sensor or Internet of Things layer is generated at the fog layer. This research suggested a vehicular traffic congestion detection model and an energy-aware cost effective task scheduling (ECTS) method in a cloud fog scenario. This research proposes an ECTS approach to allocate jobs to the fog nodes effectively. The recommended scheduling approach minimizes energy consumption and decreases expenses for time-sensitive real-time applications. The ECTS algorithm is implemented, and results are analysed using the iFogSim simulator. The proposed method minimizes energy consumption and cost. The suggested ECTS method is tested with five sets of inputs in this paper. The experiment's results show that an ECTS minimizes energy consumption in comparison to alternative algorithms. It also reduces the execution cost. The suggested approach outperforms both the Round-Robin (RR) and Genetic Algorithm techniques. According to the simulation results, the suggested algorithm reduced overall costs by 13.38% and energy usage by 6.59% compared to the Genetic Algorithm (GA). Compared to RR, the proposed method minimizes energy use by 13.76% and total costs by 18.46%.

@article{Mishra2024,
  title     = {Vehicular Traffic Congestion Detection System and Improved Energy-Aware Cost Effective Task Scheduling Approach for Multi-Objective Optimization on Cloud Fog Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Praveen Kumar Mishra and Amit Kumar Chaturvedi},
  doi       = {10.14569/IJACSA.2024.0150943},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150943}
}