A hierarchical fog computing framework for efficient and scalable task offloading in IoT systems

The rapid evolution and growing complexity of the modern automotive sector, along with increasing interconnectivity among vehicular systems, have sparked significant academic and industrial interest. Emerging intelligent transportation systems demand computational architectures capable of achieving ultra-low latency and high processing efficiency. However, these performance requirements are constrained by high energy consumption and system wide power limitations. To mitigate these challenges, intelligent workload distribution specifically, adaptive task allocation and optimal utilization of decentralized computing resources offers a scalable and energy efficient solution. This study proposes an advanced framework for resource provisioning and task offloading, embedded within a hierarchical tri-tier vehicular fog-cloud infrastructure. In this architecture, vehicles function as mobile IoT nodes, and fog computing acts as an intermediate service layer to reduce latency between edge devices and centralized cloud data centers. The proposed system supports reliable, real-time task processing and dynamic resource allocation, tailored for latency-critical applications in fast changing vehicular environments. To enhance the efficiency of the fog layer, a meta-heuristic optimization algorithm is introduced to maximize resource usage and reduce end-to-end communication delays. The framework is implemented and evaluated using iFogSim, a specialized simulation tool for fog computing research. Experimental results confirm the proposed MPSO reduces average latency by 23.8%, energy consumption by 22.1%, execution time by 32.4%, and convergence iterations by 34.9% compared with conventional PSO and other heuristic algorithms. It also improves resource utilization by 9.7% and task success rate by 4.5%. Overall these results demonstrate better performance of propose MPSO against other methodologies.