Optimization Strategy for Task Offloading in Internet of Vehicles Based on Improved Sparrow Search Algorithm

车联网中计算密集型任务对车辆计算能量消耗和网络延迟提出了更高要求。本文提出一种基于改进的麻雀搜索算法(ISSA)的低延迟低能耗的任务卸载优化策略，以应对车联网动态性、异构性和资源受限的挑战。该方法综合考虑任务特性、网络状态和计算资源，建立以最小化延迟、能耗为目标的优化模型，并设计改进的麻雀搜索算法选择最优的卸载策略。仿真结果表明，该方法能有效降低任务处理延迟和能耗，提高资源利用率，为车联网密集型的应用提供支持。The computation-intensive tasks in the Internet of Vehicles (IoV) impose higher demands on vehicle computing energy consumption and network latency. This paper proposes a low-latency, low-energy task offloading optimization strategy based on an improved Sparrow Search Algorithm-GA-SSA to address the challenges of dynamicity, heterogeneity, and resource constraints in IoV. The method comprehensively considers task characteristics, network state, and computational resources to establish an optimization model aimed at minimizing delay and energy consumption, and designs an improved Sparrow Search Algorithm to select the optimal offloading strategy. Simulation results show that this method can effectively reduce task processing delay and energy consumption, improve resource utilization, and support intensive applications in IoV.