چکیده مقاله
The rapid expansion of the Internet of Things IoT has intensified the demand for efficient task scheduling in fog computing environments, where computational resources are distributed closer to data sources to reduce latency and enhance responsiveness This paper presents a comprehensive review of task scheduling techniques tailored for fog enabled IoT systems, focusing on four primary categories: metaheuristic algorithms, heuristic methods, machine learning approaches, and mathematical optimization techniques We analyze the strengths and limitations of each method in addressing critical challenges such as energy efficiency, latency reduction, deadline adherence, and resource heterogeneity Furthermore, we identify key research gaps, including the need for multi objective optimization frameworks that incorporate security, scalability, and real time adaptability The paper also outlines promising future directions, emphasizing hybrid models that integrate learning based and mathematical approaches to improve scheduling performance while ensuring privacy and computational efficiency This survey aims to provide researchers and practitioners with a clear understanding of the current landscape and inspire innovative solutions for optimizing IoT task scheduling in fog computing
کلیدواژهها
نویسندگان
Maedeh Abedini Bagha
Roshdiyeh Higher Education Institute, Tabriz, East Azerbaijan Province, Iran
Shervindokht Mihankhah
Department of Computer Science, University of Isfahan, Isfahan, Iran
Simin Zolfaghari
Department of Computer Engineering, Sa C , Islamic Azad University, Sanandaj, Iran
Elay Faraji Bashir
Department of Electrical Engineering, Tabriz University of Technology, Tabriz, Iran
شیوه ارجاع
Abedini Bagha, Maedeh and Mihankhah, Shervindokht and Zolfaghari, Simin and Faraji Bashir, Elay,1404,Challenges and Advances in IoT Task Scheduling within Fog Computing: A Review,21st International Conference on Innovation and Research in Engineering Sciences
ارائهشده در
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