A Task Assignment Strategy Based on Improved Hybrid Quantum Pigeon-Inspired Optimization in Fog Computing Environment

With the advent of the 5 G era and the widespread adoption of smart devices, the volume of data generated by terminal devices has increased exponentially. Offloading computing tasks to nearby fog nodes can effectively reduce both latency and the energy consumption of terminal devices. To efficiently assign tasks to fog nodes and optimize resource allocation with minimal latency and terminal energy usage, this paper proposes a novel task allocation and resource scheduling method. First, a system model is constructed with the dual optimization objectives of minimizing latency and terminal energy consumption. Second, an enhanced hybrid quantum pigeoninspired optimization (PIO) algorithm is developed to select suitable computing nodes for each task. In this algorithm, the positional information of the pigeon flock is mapped to the task assignment strategy, and the encoding is discretized to process the velocity and position data of the pigeon flock. Finally, simulation experiments are conducted to evaluate the proposed method against existing approaches across various environments. The results demonstrate that the proposed approach outperforms others in reducing overall latency and terminal energy consumption of mobile devices.