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<p>In recent years, the internet of vehicles (IoV) has become an important enabler of intelligent transportation systems, providing vehicle-edge computing for latency-sensitive and computation-intensive vehicular applications. In such environments, the efficient offloading of tasks is pivotal; yet existing approaches primarily focus on optimising performance, often under the assumption of benign operating conditions or by employing static, trust-based mechanisms. However, these methods fall short in practical IoV implementations due to high mobility, short-lived connectivity, and the adversarial nature of IoV, where misbehaviour and resource exhaustion can substantially compromise the system’s reliability and security. In response to these problems, we design SpiralEdge-IoV, a secure and adaptive task offloading framework that tightly integrates defence and optimisation. This framework embeds a logarithmic spiral defence (LSD) mechanism that models trust as a deepening, adaptive path over time, enabling online risk evaluation and incremental offensive action against suspicious parties. We integrate these risk scores into an in-built bio-inspired Addax-optimisation-based decision model (LSD-AddaxNet) to obtain security-aware multi-objective offloading decisions that not only minimise latency, energy consumption, and execution cost, but also maximise robustness. A combination of realistic vehicular edge-offloading traces and the VeReMi misbehaviour dataset is employed to conduct an extensive simulation-based evaluation, confirming the effectiveness of the proposed framework. Relative to representative optimisation- and learning-based baselines, SpiralEdge-IoV delivers up to 18% lower average task latency, reduces energy consumption by about 15%, and increases task success rates in adversarial settings by over 20%. In addition, the analyses on convergence and scalability demonstrate that the framework enables stable optimisation with acceptable runtime overhead in dense vehicular scenarios. SpiralEdge-IoV can be helpful for attack-resilient, low-latency IoV edge computing and is thus suitable for safety-critical vehicular applications and future intelligent transportation systems, as shown in the results.</p>

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SpiralEdge-IoV: secure and adaptive task offloading in internet of vehicles edge computing using logarithmic spiral defense

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月2日

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  • Shankar J,
  • Nagaraja S R

摘要

In recent years, the internet of vehicles (IoV) has become an important enabler of intelligent transportation systems, providing vehicle-edge computing for latency-sensitive and computation-intensive vehicular applications. In such environments, the efficient offloading of tasks is pivotal; yet existing approaches primarily focus on optimising performance, often under the assumption of benign operating conditions or by employing static, trust-based mechanisms. However, these methods fall short in practical IoV implementations due to high mobility, short-lived connectivity, and the adversarial nature of IoV, where misbehaviour and resource exhaustion can substantially compromise the system’s reliability and security. In response to these problems, we design SpiralEdge-IoV, a secure and adaptive task offloading framework that tightly integrates defence and optimisation. This framework embeds a logarithmic spiral defence (LSD) mechanism that models trust as a deepening, adaptive path over time, enabling online risk evaluation and incremental offensive action against suspicious parties. We integrate these risk scores into an in-built bio-inspired Addax-optimisation-based decision model (LSD-AddaxNet) to obtain security-aware multi-objective offloading decisions that not only minimise latency, energy consumption, and execution cost, but also maximise robustness. A combination of realistic vehicular edge-offloading traces and the VeReMi misbehaviour dataset is employed to conduct an extensive simulation-based evaluation, confirming the effectiveness of the proposed framework. Relative to representative optimisation- and learning-based baselines, SpiralEdge-IoV delivers up to 18% lower average task latency, reduces energy consumption by about 15%, and increases task success rates in adversarial settings by over 20%. In addition, the analyses on convergence and scalability demonstrate that the framework enables stable optimisation with acceptable runtime overhead in dense vehicular scenarios. SpiralEdge-IoV can be helpful for attack-resilient, low-latency IoV edge computing and is thus suitable for safety-critical vehicular applications and future intelligent transportation systems, as shown in the results.