Task Scheduling in Fog Computing-Powered Internet of Things Networks: A Review on Recent Techniques, Classification, and Upcoming Trends

The Internet of Things (IoT) phenomenon influences daily activities by transforming physical equipment into smart objects. The IoT has achieved a wealth of technological innovations that were previously unimaginable. IoT application areas cover various sectors, including medical care, home automation, smart grids, and industrial operations. The massive growth of IoT applications causes network congestion because of the large volume of IoT tasks pushed to the cloud. Fog computing mitigates these transfers by placing resources near the edge. However, new challenges arise, such as limited computing power, high complexity, and the distributed characteristics of fog devices, negatively affecting the Quality of Service (QoS). Much research has been conducted to address these challenges in designing QoS-aware task scheduling optimization techniques. This paper comprehensively reviews task scheduling techniques in fog computing-powered IoT networks. We classify these techniques into heuristic-based, metaheuristic-based, and machine learning-based algorithms, evaluating their objectives, advantages, weaknesses, and performance metrics. Additionally, we highlight research gaps and propose actionable recommendations to address emerging challenges. Our findings offer a structured framework for researchers and practitioners to develop efficient, QoS-aware task scheduling solutions in fog computing environments.

@article{TIAN2025,
  title     = {Task Scheduling in Fog Computing-Powered Internet of Things Networks: A Review on Recent Techniques, Classification, and Upcoming Trends},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {1},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Dongge TIAN},
  doi       = {10.14569/IJACSA.2025.0160182},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160182}
}