Heuristic-guided BSO for efficient task scheduling in IoT-driven fog–cloud environment

The rapid expansion of IoT devices demands efficient task scheduling in fog-cloud infrastructures. This study presents a Heuristic-Guided Butterfly Swarm Optimisation (BSO) algorithm, integrating the Minimum Completion Time (MCT) heuristic into BSO’s initialization phase to enhance convergence and scheduling quality. A utility function balances processing time and execution cost while ensuring scalability across heterogeneous workloads. Simulations with 40-500 tasks demonstrate superiority over TCaS, MPSO, BLA, and RR algorithms, achieving up to 33.5% faster execution in fog-only settings and 64.89% higher scheduling efficiency in fog-cloud environments with minimal cost overhead. These results confirm the proposed scalable, cost-efficient solution for IoT-driven scheduling.