Hybrid Optimization and Case-Based Reasoning Framework for IoT Task Offloading in Cloud Radio Access Networks

This article introduces an innovative decision-making framework for optimizing task offloading in cloud radio access networks (CRANs), specifically designed to overcome the constraints of battery power and computing resources in Internet of Things (IoT) devices. While traditional optimization methods have made notable advancements in addressing offloading challenges, their effectiveness is often compromised by fluctuating network conditions and uncertainties. Recent machine learning approaches offer adaptive solutions; however, they demand extensive real-time data, which can impede efficiency and result in local optima. To address these issues, we propose a hybrid framework that synergizes optimization techniques with case-based reasoning (CBR). Initially, we establish a comprehensive decision database through offline optimization to secure global optimal solutions for task offloading. This database serves as a critical resource for the CBR method, which selects the most pertinent decision script based on the current network state, thereby facilitating effective real-time task offloading decisions. Our simulation results demonstrate that this framework not only significantly enhances decision-making efficiency but also consistently yields near-optimal offloading strategies. This improvement directly contributes to the performance of IoT systems operating within CRANs, showcasing the potential of our approach to elevate resource management in complex network environments. By integrating optimization with adaptive reasoning, this work advances state-of-the-art IoT task-offloading solutions.