<p>Task scheduling in fog computing environments is vital for the efficient allocation of computational resources and the optimization of performance metrics. As the volume of Internet of Things data continues to grow, effective task scheduling has become increasingly challenging. Many existing algorithms focus mainly on reducing waiting times and improving response speeds but often overlook the varying time sensitivity requirements of different applications and the need for fair execution across diverse task types. To address these limitations, we propose a novel time-aware scheduling algorithm called IPAQ, which classifies tasks according to their time sensitivity. This ensures that high-time sensitivity tasks are prioritized, while low-time sensitivity tasks also benefit from reduced response times. Additionally, to determine the optimal task scheduling order under multi-objective conditions, IPAQ integrates an enhanced Particle Swarm Optimization algorithm with the Analytic Hierarchy Process (AHP), resulting in a real-time dynamic scheduling framework called IP-AHP. This innovative approach demonstrates superior performance in managing large volumes of tasks within fog computing environments, significantly outperforming other algorithms in this domain.</p>