Adaptive Fog Computing Framework (AFCF): Bridging IoT and Blockchain for Enhanced Data Processing and Security

Abstract

This research introduces an Adaptive Fog Computing Framework (AFCF) aimed at enhancing the efficiency and scalability of IoT ecosystems through blockchain technology integration, addressing task allocation, resource management, and task offloading challenges within fog and cloud computing paradigms. Employing simulations, the study utilized task distribution strategies, blockchain stability assessment, and cloud server workload management, demonstrating the framework’s capacity to maintain performance across diverse IoT settings. Quantitative results revealed a cent percent success rate in task processing, with a balanced load distribution at 50% and an average task complexity of 6.47893 in arbitrary units. The system demonstrated a latency of 48.479 milliseconds and a throughput of 2.469697 tasks per timestep, showcasing high scalability (97.8%) and energy efficiency (15.774194 in arbitrary efficiency units), emphasizing the AFCF’s robustness in varied tasks and resource dynamics. The study concludes the AFCF’s potential for real-world IoT applications, highlighting its implications for future research and practical deployment, underscoring its contribution to fog and cloud computing literature and paving the way for further exploration into adaptive computing frameworks.