Exploring the potential and limitations of Fog Computing toward efficient & scalable solutions in modern network

Fog computing has emerged as a pivotal paradigm to support latency-sensitive, bandwidth-efficient, and context-aware services across diverse application domains such as healthcare, smart mobility, and industrial Internet of Things (IIoT). Unlike prior reviews that offer broad overviews, this study presents a comparative, standards-driven analysis of fog computing frameworks, uniquely categorizing them into four performance pillars: interoperability, security and privacy, latency and Quality of Service (QoS), and energy efficiency with resource management. By synthesizing recent developments from 2020 to 2024, the review uniquely maps specific frameworks to these standards and evaluates their trade-offs, limitations, and application-specific suitability. The paper further distinguishes itself by integrating advanced strategies such as federated learning, blockchain trust models, deep reinforcement learning, and edge AI and analyzing their implications in fog architectures. Summary tables and architectural illustrations enhance understanding, while key gaps—like power inefficiencies and scalability bottlenecks—are critically discussed. Finally, the review offers targeted future directions, emphasizing the role of edge intelligence, adaptive orchestration, and standardization in building resilient fog-enabled systems. This structured review provides a roadmap for researchers and practitioners aiming to develop scalable, secure, and intelligent fog computing solutions.