Optimizing QoE and Energy Consumption for IoT Applications in Fog Computing

Fog computing has emerged as a pivotal paradigm in addressing the computational and latency demands of Internet of Things (IoT) applications, offering significant potential to enhance Quality of Experience (QoE) while managing energy consumption. This review critically examines existing strategies for optimizing QoE and energy efficiency within fog computing environments, focusing on their applicability to diverse IoT scenarios. Key topics include resource allocation techniques and energy-efficient scheduling mechanisms. The paper also explores the trade-offs between computational performance, energy usage, and QoE, highlighting innovative decision-making frameworks that leverage artificial intelligence and machine learning. By synthesizing insights from recent advancements, this review identifies critical challenges, such as scalability, heterogeneity, and dynamic workload variations, and proposes future directions to guide the development of sustainable and high-performance IoT-fog ecosystems. This study aims to contribute to the growing body of knowledge, paving the way for practical implementations that balance user satisfaction and energy sustainability in IoT applications.