Reliability-oriented edge computation offloading strategy for Internet of Vehicles: based on improved hybrid fox optimization algorithm
推荐理由
Abstract In the Internet of Vehicles (IoV), edge computing supports computation-intensive tasks through roadside unit servers. However, guaranteeing reliable t…
核心判断
论文摘要（中文）
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In the Internet of Vehicles (IoV), edge computing supports computation-intensive tasks through roadside unit servers. However, guaranteeing reliable task execution remains challenging in dynamic environments with limited resources. We propose a reliability-oriented computation offloading strategy based on the Improved Hybrid Fox Optimization (IHFOX) algorithm. The algorithm integrates sine chaotic mapping and Lévy flight to balance exploration and exploitation. It further employs a standard normal distribution for position updates, thereby accelerating convergence and avoiding local optima. For model construction, we establish a singlereplica reliability model from dual dimensions of transmission reliability and computational reliability, meeting system-level reliability constraints through a multi-replica parallel redundancy mechanism. Meanwhile, vehicle mobility, resource/energy constraints, and joint weight factors are integrated to form a comprehensive optimization model under reliability constraints. Experiments demonstrate that the IHFOX algorithm guarantees high reliability with over 95% task completion rate, while significantly reducing system cost, delay, and energy consumption, exhibiting superior practical value in reliability-sensitive IoV applications.
研究动机（中文总结）
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However, despite their popularity, DRL-based approaches face inherent limitations that prevent them from directly addressing our reliability-ori - ented problem.
创新与贡献（中文总结）
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In addition, the model takes vehicle mobility into account and uses joint weighting factors to balance delay and energy costs. ● Algorithm: We propose an Improved Hybrid Fox Optimization (IHFOX) algorithm.
方法与证据
方法（中文总结）
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In addition, the model takes vehicle mobility into account and uses joint weighting factors to balance delay and energy costs. ● Algorithm: We propose an Improved Hybrid Fox Optimization (IHFOX) algorithm.
实验结果（中文总结）
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A moderate population size ( 𝑃𝑃 𝑃 𝑃𝑃) achieves the optimal balance: 𝑃𝑃 𝑃 𝑃𝑃 suffers from insufficient initial diversity and premature convergence to suboptimal solutions, while 𝑃𝑃 𝑃 𝑃𝑃 accelerates early descent but is eventually outperformed by 𝑃𝑃 𝑃 𝑃𝑃 due to diminished selection pressure and redundant Lévy flight perturbations in later iterations.