New approach of computing task offloading for IOV based on sparrow search optimization strategy

With the rapid development of the Internet of Vehicles (IoV), the computation and communication demands of vehicles are increasing. The traditional centralized computing model can no longer meet these demands. Consequently, task offloading techniques have become crucial for enhancing computational performance and reducing vehicle load in IoV. In this paper, we propose a new method of task offloading for IoV computing based on the sparrow search optimization strategy. Specifically, we address the multifactorial influences on task offloading. Firstly, we design an offloading model that integrates multiple optimization objectives, such as delay and energy consumption. Secondly, we develop a fitness function that balances delay and energy consumption to evaluate and select task offloading strategies. Additionally, we design a network access model to maintain network access stability. Finally, we conduct an iterative optimization search for the offloading strategy using an improved sparrow search optimization algorithm. Through extensive simulation experiments and real-world scenario tests, we validated the effectiveness and performance advantages of the proposed method. The experimental results demonstrate that our new IoV task offloading method, based on the improved sparrow search optimization algorithm, enhances computational performance while reducing vehicle load, showing great potential for applications in the field of IoV task offloading.